tag:blogger.com,1999:blog-26964435325799112642024-02-21T22:18:11.196-08:00Thinking SocialDaily log of research, thoughts, and notes on cool papersUnknownnoreply@blogger.comBlogger26125tag:blogger.com,1999:blog-2696443532579911264.post-68619800608448511042010-08-02T12:39:00.000-07:002010-08-03T00:40:26.728-07:00Detecting influenza outbreaks by analyzing Twitter messagesToday I read an interesting paper that used Twitter data. It's a paper by <span class="Apple-style-span"><b><span class="Apple-style-span" style="background-color: white;">Aron Culotta</span></b></span> on detecting influenza outbreaks.<br />
<a href="http://arxiv.org/abs/1007.4748">http://arxiv.org/abs/1007.4748</a><br />
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<span class="Apple-style-span" style="color: purple;"><b><span class="Apple-style-span" style="color: black; font-weight: normal;"><b><span class="Apple-style-span" style="color: purple;">Summary =======</span></b></span></b></span><br />
This paper examines the correlation between flu-related tweets and the actual flu trends (reported by the US centers for disease control and prevention, CDC). The author borrows the methodology used in Google's Nature paper on flu tracking (Ginsberg et al. 2009) and uses the log-odds to measure the correlation between two variables: (a) the fraction of population that have flu over several weeks, reported by CDC, and (b) the fraction of tweets that contain flu related keywords.<br />
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<span class="Apple-style-span" style="color: purple;"><b><span class="Apple-style-span" style="color: black; font-weight: normal;"><b><span class="Apple-style-span" style="color: purple;">Findings =======</span></b></span></b></span><br />
(1) Similar to the Google's Nature paper, the accuracy in prediction is pretty high---around 95%. (Google paper, based on google search keywords, reported 97% accuracy.)<br />
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(2) What is new in this work is that it investigates the need to prune out spurious words like 'swine', 'h1n1', 'vaccine', and 'http'. Tweets containing spurious words likely contain information about the flu passing, but not flu symptoms. However, removing spurious words didn't always increase the accuracy in the prediction of flu trends.<br />
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(3) Hence, the author tried out further with two different supervised learning methods to see if spurious words could be removed in a smart way. The answer is partly yes, based on one of the classification methods used.<br />
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<b><span class="Apple-style-span" style="color: purple;">My thoughts =======</span></b><br />
The paper made me think about what sorts of research we are pursuing on the field of data-driven social science. Application-driven research usually has a clear goal, but it always leaves me the feeling of wanting to know more about the data. <br />
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(1) Higher accuracy really needed? After reading the first part of the paper, I was less convinced why the author went after increasing the accuracy in prediction. 95% accuracy seems high enough to be used in a surveillance system.<br />
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(2) What's next? Maybe to look at social network topology or geography? The frequency of words is one interesting statistic someone can draw from data. But the data has so much more to tell. How are the users connected -- are users who talk about flu symptoms connected? Or are they located nearby in geography?<br />
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PS: I am limiting comments only to the members of this blog, as I started getting too many spams.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-10245979093711214572010-04-27T02:06:00.000-07:002010-04-27T16:21:10.651-07:00The fragility of interdependencyToday, at <a href="http://aa.kaist.ac.kr/">AALab</a>, we had an interesting discussion about a Nature paper that was published this month:<b><a href="http://www.nature.com/nature/journal/v464/n7291/abs/nature08932.html"> Catastrophic cascade of failures in interdependent networks</a></b> by Sergey V. Buldyrev, Roni Parshani, Gerald Paul, H. Eugene Stanley & Shlomo Havlin<br />
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The paper is about surprising characteristics of a failure on two mutually dependent networks. While most existing studies on network robustness have focused on a single network, this doesn't mean that most real networks are independent of each other. Real networks are often mutually dependent, like the power network and the Internet described in the paper. A router cannot function if a nearby power station is out; a power station cannot function if it is disconnected (i.e., if Internet fails). Logical networks like those of financial and political networks are also mutually dependent. Some networks even share the same entities; we belong to multiple social networks like Facebook and Twitter. And when networks are mutually dependent, a single local failure could trigger a disruptive avalanche of cascading and escalating failures. <br />
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Here are three surprising results from the paper:<br />
(Summarized well in <a href="http://www.nature.com/nature/journal/v464/n7291/full/464984a.html">Vespignani's article</a>) <br />
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1. Networks exhibit a critical threshold value for the fraction of nodes that can be removed above which the network becomes totally fragmented (i.e., the size of the giant component becomes 0). Compared to a single independent network, mutually dependent networks have a much smaller threshold value. This means that mutually dependent networks could collapse at a smaller level of damage. <br />
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2. In independent networks, increasing random failures will gradually harm the integrity of the network. However, in mutually dependent networks, increasing random failures lead to abrupt collapse of the network as shown in the graph below. G means the fraction of nodes that belong to the giant connected component. Failures in mutually dependent networks cause a step-like first-order jump around q_c. <br />
<div class="separator" style="clear: both; text-align: center;"><a href="http://www.nature.com/nature/journal/v464/n7291/images/464984a-f2.2.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="259" src="http://www.nature.com/nature/journal/v464/n7291/images/464984a-f2.2.jpg" width="320" /></a></div><br />
3. In independent networks, heavy-tailed degree distributions have been proven to add great robustness under random failures. Power-law graphs are known more robust to random failures than Erdos-Renyi random graphs. This, however, is also not true in mutually dependent networks. Power-law graphs are more fragile when they are mutually connected. The broader the degree distribution is, the more fragile the networks are.Unknownnoreply@blogger.com4tag:blogger.com,1999:blog-2696443532579911264.post-70523315802876199632010-01-12T17:15:00.000-08:002010-01-12T17:48:15.683-08:00A Nature paper on human insurgency<a href="http://www.nature.com/nature/journal/v462/n7275/full/nature08631.html">Common ecology quantifies human insurgency</a><br />
by Bohorquez, Gourley, Dixon, Spagat, and Johnson<br />
(CEIBA complex systems research center in Columbia, etc) <br />
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The paper presents an empirical data analysis of 9 major insurgencies, consisting of 54,679 events worldwide. The paper centers around the theme of finding a common factor across different insurgent conflicts and proposes a model of conflict organization, which treats insurgent population as an ecology of dynamically evolving, decision-making groups. Although groups are heterogeneous in terms of their strategies, they tend to converge towards similar response when fed the same information.<br />
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<i>Finding (a)</i> Insurgent conflicts have different characteristics from the traditional wars: their scaling factor is around 2.5 and typically show a power-law trend (rejects log-normality), while traditional wars cannot reject log-normality and show a smaller scaling factor. The implication of this scaling factor (2.5) is at its robustness. (It's hard to fragment such insurgent group.)<br />
<i>Finding (b)</i> Burstiness in terms of the number of events per day shows abundance of heavy and light days, or conversely, lack of 'medium' days. The implication of this burstiness is at maximizing the media attention. (Modern insurgent conflicts are not about killing others, but about media show. It's better to attack on a quiet day)<br />
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Model assumes two mechanisms: <i>(a)</i> on-going group dynamics within the insurgent population (coalescence and fragmentation of members to different groups over time) <i>(b) </i>group decision making about when to attack<br />
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Personal note: I read this paper twice. At first, the model seemed too simple and I wasn't really impressed by the work. Given the general theme of the paper, I expected there to be lots of sophisticated ideas and theories that people have looked at. The model in the paper, however, was extremely simple. After a second read, I starting seeing how a simple model could link together many different empirical patterns the paper showed (e.g., why insurgencies occur around a scaling factor of 2.5, why the distribution of Columbian and Afghanistan insurgencies have different tail shapes.) <br />
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Among the tricks I've learned from the paper, I liked the frequency distribution of real wars against random wars. 10,000 random wars were generated such that dates of the events were shuffled and the average frequency was taken across the samples. Authors then compare the frequency distribution of actual data with this randomly shuffled wars. I also liked the link between this work and the same patterns shown in financial data (how people herd around different stock items each day) -- it might really show a crucial link between violent and non-violent forms of human behavior.<br />
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Now moving forward, how could we utilize the result of this work to other social network data like Twitter? Insurgency, rallies, and demonstration -- all seem difficult in terms of parsing data. But if we can do anything, what would I want to look at? Adopting methodology is an obvious way, but could we do something cooler?Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-2696443532579911264.post-1488494964087618132010-01-11T23:05:00.000-08:002010-01-11T23:38:49.598-08:00Decentralized Search AlgorithmsComplex network research focuses on large-scale network structures such as social systems, cell biology, neurology, etc. The goal is to bserve real-world network properties and <i>model</i> the observed properties under random mechanisms.<br />
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Erdos-Renyi (1959) random graph G(n,p) connects two pairs of nodes with a probability p=c/n where c is a constant. It has two important properties: <i>(a)</i> when c is less than 1, composed of small components all of which have O(logn) <i>(b)</i> when c is greater than 1, a.a.s. (asymptotically almost surely), a unique giant component containing theta(n) nodes appears, which is called the phase transition. <br />
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Stanley Milgram's 6-degree separation states that not only there exist a short path in a society, it is possible for individuals to find such a path using only local information.<br />
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Small world networks by Watts and Strongatz uses grids (to guarantee clusters) and makes short cuts in the network uniformly random. This leads to preserving small-world properties (i.e., short distance and high clustering). The prevailing need for this model is that Erdos-Renyi random graph does not support the notion of clusters.<br />
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Extended model by Kleinberg is a variant of the small-world model. Rather than making shortcuts that are uniformly random, Kleinberg assumes shortcuts follow a particular distance distribution, rho(v,w)^-alpha, and showed that <i>(a)</i> alpha=2, decentralized algorithm exists that is of O(log^2 n). <i>(b)</i> Otherwise, there does not exist any poly-logarithmic decentralized algorithm.<br />
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Further readings:<br />
1. J. Kleinberg. <a href="http://www.cs.cornell.edu/home/kleinber/nat00.pdf">Navigation in a Small World.</a> Nature 406(2000), 845.<br />
2. J. Kleinberg. <a href="http://www.cs.cornell.edu/home/kleinber/icm06-swn.pdf">Complex Networks and Decentralized Search Algorithms.</a> Proceedings of the International Congress of Mathematicians (ICM), 2006.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-67682963577654822342010-01-11T17:21:00.000-08:002010-01-11T23:47:10.312-08:00Winter School on Algorithms and CombinatoricsAttending <a href="http://wsac.kaist.ac.kr/">a very interesting winter school</a> organized by KAIST professors. It's refreshing to brush up on theoretical concepts.<br />
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<span style="color: blue;">(1) Talk by Heekap Ahn (Postech) on Voronoi Diagram</span><br />
Computational efficiency<i> </i>assumes that basic operations (+,-,*,assign,etc) take constant time O(1) and tries to count,<i> </i>given an input size n, how many basic operations is needed.<br />
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Convex hull is a set S of points in the plane that is the smallest convex set containing S: A naive algorithm will incur n^3 (n^2 to pick two points and see if it's at the edge) , but a smart algorithm will scan the network in clockwise direction (called the plane sweeping technique) and solve in <i>nlogn </i>(sort by x-axis) + <i>2n</i> (clockwise step)<br />
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Line segment intersection has the<i> </i>worst case of <i>n^2, but an </i>output-sensitive algorithm (relative to the number of intersection) runs in<i> (2n*2+2k)*logn = nlogn + klogn</i><br />
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<span style="color: blue;">(2) Talk by Kyomin Jung (KAIST) on NP-Completeness</span><br />
Interesting observation about how our brains do arithmetics: We remember the addition of two numbers between 0-9, like a turing machine storing 10^2 combinations!, then extends this knowledge to do more complicated calculations. Turing machine is a device with a finite amount of read-only "hard" memory (states) and an unbounded amount of read/write tape-memory. 변하지 않는 부분의 메모리 (하드웨어) + 연습장 (소프트웨어)<br />
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Notes on P and NP: <i>(a)</i> P is the set of problems that can be solved in polynomial time, given an input size n <i>(b)</i> NP is the set of problems that can be *verified* in polynomial time, Given candidate solutions, one can verify whether a solution is right or wrong in P time <i>(c)</i> P is a subset of NP <i>(d) <span style="font-style: normal;">The question of P</span></i>=NP is related to the meaning of intelligence. (Let's say you've solved NP. If P=NP, then there might not be intelligence in the brain that solved it) <i>(e) </i>Cryptography relies on P!=NP. Public keys are given out, but the combinations to generate a public key is practically hard to get. <i>(f) </i>Unless we know P=NP, it is important to develop approximation algorithms!<br />
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<div style="margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px;">Hamiltonian Path Problem (HP) - visit each node exactly once<br />
</div><div>Traveling salesman problem (TSP) - find the shortest path that is HP<br />
</div><br />
If P (HP) reduces in polynomial time to Q (TSP), then P is <i>no harder to solve than</i> Q. <i>(a)</i> NP-hard: if all problems all R\in NP are reducible to P, then P is NP-hard; NP중에서 가장 어려운 것 <i>(b)</i> NP-complete: if P is NP-hard and P\in NP, P is NP-complete (NP중에서 가장 쉬울 수 있다) <i>(c)</i> Cook-Levin theorem: the first real case of NP-complete<br />
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Combinatorial optimization, max cut, underlying assumption is again P!=NP: <i>(a)</i> rho (ALGO, G) : given a graph G, how efficient is algorithm ALGO? <i>(b) </i> rho (ALGO) = min_G ( rho(ALGO, G) ): given any G instances, what is the best algorithm to solve a problem? <i>(c)</i> rho (maxcut) = max_algo ( rho(algo) ): how hard is the problem itself?<br />
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PTAS (polynomial time approximation scheme): 1+/-e of the optimal <br />
FPTAS (fully PTAST)<br />
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<span style="color: blue;">(3) Talk by Jinwoo Shin (MIT) on Metropolis-Hastings Rule</span><br />
Suppose we only know local information, how could you sample users uniformly random? This problem is to find random walk without bias. <i>(a) </i> Uniformly random property is p_ij = p_ji <i>(b) </i> Adding a self loop can fix this easily, but could be prone to even-odd number oscillations. <i>(c)</i> A seminar work: Metropolis-Hastings rule since 1950s<br />
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<span style="color: red;">(4) Talk by Sang-il Oum (KAIST) on Parameterized Complexity</span><br />
Concepts: <i>(a)</i> Vertex cover : a set of vertices meeting all edges <i>(b)</i> vertex cover problem (G, k): does G have a vertex cover of size <= k? <i> (c)</i> Minor of a graph: a graph that could be obtained by contracting nodes or deleting nodes or edges<br />
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FTP (fixed parameter tractable): a problem with a parametr k is FPT if it can be answered in time O(f(k) n^c) for a computable function f and a fixed c. that is exponential on a fixed parameter k, but not in the input size n. Rod Downey & Michael Fellows<br />
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Kernelization: A decidable problem is FTP if and only if it has a kernel<br />
문제가 input 사이즈와 상관없이 사이즈 k에 대해 변형될 수있을때<br />
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<div style="margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px;"><span style="color: red;">(5) Talk by Heekap Ahn (Postech) on Geometric Graphs</span><br />
</div><div>Notations: <i>(a)</i> A graph without cycle is a forest; a connected forest is a tree. <i>(b)</i> A separating set of G will make the graph disconnected. <i>(c) </i>k-connected, if separating set has cardinality of at least k. <i> (d)</i> A graph is planar if it can be drawn in the plane wihtout crossing edges. That is, a planer graph has a crossing number of zero. (e) Geometric graph is a subset of topological graph.<br />
</div><div><br />
Main definition: The crossing number, denoted by cr(G), of a graph G is the least possible number of pairs of crossing edges in a graph of G. <i>(a)</i> Finding the crossing number is NP-hard. <i>(b)</i> There exist efficient algorithms to find crossing number of smaller than k; that is the problem is fixed parameter tractable.<br />
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<span style="color: red;">(6) Talk by Kyomin Jung on </span><a href="http://en.wikipedia.org/wiki/Randomized_algorithm"><span style="color: red;">Randomized Algorithms</span></a><br />
Define a turing machine that can through a binary random coin, called a <i>probabilistic</i> turing machine. <i>(a)</i> What is good about it? Smoothes the "worst case input distribution" into "randomness of algorithm." <i>(b)</i> Law of large number property means that independent random samplings lead to an average that is close to the expectation, e.g., Erdos-Renyi random graph G(n,p) (when does a giant component appears?), voting poll (how to find good sample for a poll?).<br />
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Chernoff Bound: <i>(a)</i> Suppose we have a coin with a probability of a head p, then the expected number of heads is 1*p + 0*(1-p) = p. <i>(b)</i> After flipping a coin m times, the error rate is <= exp ( - lamda^2*m<br />
) where lambda is an error. <i>(c) </i>Las Vegas algorithm is a randomized algorithm that always gives correct answer. <i>(d)</i> Monte Carlo algorithm has deterministic running time, but its output could be correct with a certain probability. <br />
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BPP (bounded-error, probabilistic, polynomial time): the set of problems that have Monte Carolo algorithms. <i>(a)</i> Is BPP=P (deterministic language)? <i>(b)</i> Pseudo-random generator (PRG) picks a random number not purely random, but is very hard to determine its randomness in polynomial time. <br />
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Randomized min cut algorithm by David Karger: Repeat until |V|<=2, pick a random edge and contract the edge. The two remaining nodes represent the cut points.<br />
</div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-11689486702294508622009-08-28T07:40:00.000-07:002009-08-28T09:35:55.362-07:00Iran election in TwitterOne of the most exciting events in social media would definitely be how the use of Twitter lead to some of the rallies and protests in Iran. Our team at MPI-SWS started looking at Twitter to study the patterns of information propagation. After numerous days of data collection and parsing, finally we are ready to investigate how tens of millions of users communicated with each other. Here's a sneak peek of our on-going research: (Disclaimer: this is definitely an exciting, yet preliminary result and could be changed later on.)<div><br /></div><div>We've looked at whether users who posted tweet(s) on Iran election are connected in the social graph. Imagine the entire social graph of Twitter. Then mark all nodes (=users) who wrote at least one tweet about Iran election. Remove all other nodes in the social graph. Now focus on the remaining nodes in the network. What do they look like? </div><div><br /><a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJUFGnof2dy4RV0D0MZskj5YqsrwQANHD9swz0Lvh9quHsN5fPYlM2E_wH3p3Odhabhly1o99gHByuS67NikcI7k7XI4UAAasaRWV6nIRkhUlJASanJPuudQhWlSh3BkLfKbu-3sd-rHUU/s1600-h/Picture+2.png"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 400px; height: 330px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJUFGnof2dy4RV0D0MZskj5YqsrwQANHD9swz0Lvh9quHsN5fPYlM2E_wH3p3Odhabhly1o99gHByuS67NikcI7k7XI4UAAasaRWV6nIRkhUlJASanJPuudQhWlSh3BkLfKbu-3sd-rHUU/s400/Picture+2.png" border="0" alt="" id="BLOGGER_PHOTO_ID_5375027749662375506" /></a><div style="text-align: left;"><br /></div><div style="text-align: left;">The plot above shows the size and the number of connected components, where each connected component represents a set of users who are connected by friendship. There were 200,000 users who talked about Iran election in our (sampled) dataset. Surprisingly, 85% of the users belonged to a single large component and 2% of the users to smaller tons. 15% of the users were singletons; they were not connected to any other users who talked about Iran election. </div><div style="text-align: left;"><br /></div><div style="text-align: left;">We initially expected to see a <a href="http://en.wikipedia.org/wiki/Power_law">power-law distribution</a> whose characteristic pattern is a straight line. This means that the number of connected component should have x^a relationship with the size of the connected component x. (a is called the power-law exponent). But we see two different exponents in the plot. </div><div style="text-align: left;"><div style="text-align: left; "><br /></div><div style="text-align: left; ">So why don't we see a straight line in the size distribution of connected components? I have several hypotheses for why we might see a multi-scaling trend, such as the language barrier and the effect of mass media.---I like these moments when I encounter unusual patterns. This is what makes research all the more challenging and fun.</div><div><br /></div></div><div style="text-align: left;"><a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiAZY2rRTp4rOxiLXVjyuStUm95yISzgcXgPoCIvrK7rlw78xXUEvHroq-T4TGVMXNC77njanQUvMLrPMeo89n89IJdILdMRU_StEUT02vSGOFqJrxTc-Gq76mec7tUICcrRYoUvgkzx3SW/s1600-h/Picture+3.png"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 320px; height: 250px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiAZY2rRTp4rOxiLXVjyuStUm95yISzgcXgPoCIvrK7rlw78xXUEvHroq-T4TGVMXNC77njanQUvMLrPMeo89n89IJdILdMRU_StEUT02vSGOFqJrxTc-Gq76mec7tUICcrRYoUvgkzx3SW/s320/Picture+3.png" border="0" alt="" id="BLOGGER_PHOTO_ID_5375047423040139890" /></a><div style="text-align: left;"><span class="Apple-style-span" style="color:#0000EE;"><span class="Apple-style-span" style="text-decoration: underline;"><br /></span></span></div></div><div style="text-align: left;">Focusing on the largest connected component, users in this group do show a power-law distribution in their connectivity. Some users potentially influenced tweets of more than 1,000 others (meaning that these users had more than 1,000 fans who also wrote about Iran election); likewise, a user can be influenced by more than 1,000 others in one's subscription list. Both indegree and outdegree distributions follow a power-law trend; but interestingly these quantities turn out to be not related (correlation coefficient of 0.3066). </div><div style="text-align: left;"><br /></div><div style="text-align: left;">There are a lot that need to be done and I'm fascinated to investigate how social media like Twitter have changed the way we encounter new information and collaboratively propagate messages among users. </div></div>Unknownnoreply@blogger.com3tag:blogger.com,1999:blog-2696443532579911264.post-36351754309005448282009-08-12T03:00:00.000-07:002009-08-12T03:07:22.280-07:00The 1st International Workshop on Mining Social MediaIf you're working on social networks and data mining, here's a perfect workshop to consider at a wonderful south of Spain:<br /><br /> <a href="http://www.socialgamingplatform.com/msm09/">Mining Social Media (MSM'09)</a><br /> Paper submission deadline: September 6th<br /> Venue: November 9th, 2009 Seville, SpainUnknownnoreply@blogger.com2tag:blogger.com,1999:blog-2696443532579911264.post-47188297655334556492009-06-26T01:59:00.000-07:002009-06-26T02:09:24.367-07:00Timely research<div>I'm reading a nature paper that was published yesterday, <a href="http://www.nature.com/nature/journal/v459/n7250/full/nature08182.html">Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic</a>. </div><div><br /></div><div>This is a 4-page letter paper. According to the Nature authors' guide, this means the paper provides an outstanding finding whose importance means that it will be of interest to scientists in other fields. Regular articles in Nature is 5 page long and needs to make a substantial advance in understanding of an important problem. </div><div><br /></div><div>As the title says, the paper is about the recent 2009 swine flu. I'm amazed that scientists put together good work in such a short period of time. Well, informally, I've heard of a couple of immediate rejections on the Swine flu to Nature. This says, there are *lots* of scientists who are quick and good. </div><div><br /></div><div>What can social network researchers do with the abundance of data and the recent Iran election? This could turn into another great Nature letter paper in a few months.</div><div><br /></div><div><span class="Apple-style-span" style="font-family:Verdana, Geneva, Arial, Helvetica, sans-serif;font-size:100%;"><span class="Apple-style-span" style=" -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px;font-size:13px;"><b><br /></b></span></span></div><div><br /></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-57724109368815052652009-06-17T04:55:00.000-07:002009-06-17T05:15:49.694-07:00Twitter FAQ<div><br /></div><div>So it happened that I finally came across something to ReTweet about. <a href="http://twitter.com/nekozzang/statuses/2205505096">My very first use</a> of "RT". For those of you who are not familiar with Twitter codes, here is a brief and useful <a href="http://www.brentozar.com/archive/2008/08/twitter-101/">tutorial on Twitter FAQ</a>.</div><div><br /></div><div><b>RT</b> means ReTweet or Repeat. </div><div>Copy the message you want your want to retweet and start with "RT @UserName"</div><div><br /></div><div><b>OH</b> means OverHeard</div><div>When you hear something funny or insightful with your ears (as opposed to reading it on Twitter) and you want to repeat it, prefix it with OH. Generally, this is used anonymously, not for quoting people. </div><div><br /></div><div><b>HT </b>means HeardThrough</div><div>This is similar to OH in that you use it to repeat things you heard with your ears. A difference is that you can quote the person's name. </div><div><br /></div><div><b>Starting with @ sign</b> means Reply</div><div>When you want to reply to someone, start your tweet with @UserName.</div><div><br /></div><div><b>Hash Tags (#) </b>help to designate topics that people might search for</div><div>When you head to conferences, look for hash tags. </div><div><br /></div><div><b>#FollowFriday</b> means recommended followers</div><div>This is like book recommendations from a friend. You can list your top followers in the form of @UserName after this tag.</div><div><br /></div><div><br /></div><div><br /></div><div><br /></div>Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-2696443532579911264.post-10737181713559997542009-05-20T10:39:00.000-07:002009-05-20T16:23:27.448-07:00ICWSM'09 note - day three<div><span class="Apple-style-span" style="font-family:Helvetica;font-size:7;"><span class="Apple-style-span" style="font-size:48px;"><span class="Apple-style-span" style="font-family:arial;font-size:130%;"><span class="Apple-style-span" style="font-size:16px;"><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Arial; color: #5dc732"><b>Leveraging Diversity</b></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Arial">- Ideas embracing diversity in opinions getting popular: Sidelines, <a href="http://moderator.appspot.com/"><span style="color:#4d2184;">Google moderator</span></a></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Arial">- Goal is to project an accurate proportion of users supporting different opinions. let users get an exposure to challenges or new ideas. </p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica">- Quick Q: do people really like diversity?</p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Arial"><span style="font: 12.0px Helvetica">- Similar work on news media bias</span><span style="color:#4d2184;"> <a href="http://nclab.kaist.ac.kr/papers/Conference/NewsCube.pdf">"NewsCube"</a></span> (CHI'09). This work looks at content to aggregate similar news and project different themes news articles. Sidelines paper simply looks at voting counts. </p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Arial; min-height: 14.0px"><br /></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Arial; min-height: 14.0px"><br /></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica">- "Diversity in user activity and content quality in online communities" by <a href="http://www.hpl.hp.com/research/scl/people/tad/">Tad Hogg</a>. How many activities does a user do per day? </p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica">- Users with very little online time or little activity harder to model (i.e., difficulty of modeling in a heavy-tail distribution).</p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica">- Visibility (or exposure) is the key mechanism by which information spreads? (whether exposed by friends or by serendipitous browsing). Visibility and interest are different. (look <a href="http://arxiv.org/pdf/0803.3482v1">paper</a>)</p><div><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><br /></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><br /></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica">- Check out Lada Adamic's write-up on <a href="http://www.hpl.hp.com/research/idl/projects/social/index.html"><span style="text-decoration: underline ; color:#4d2184;">social networks</span></a> at HP labs. </p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:Arial;">"Unlike viruses, which spread indiscriminately from host to host<span class="Apple-style-span" style="color:#FF6600;">, pieces of information are propagated by people who find them interesting</span> and who pass the information to others who they think may be interested. Since people are most similar to their immediate contacts, and this similarity decays as the distance in the social network between individuals increases,<span class="Apple-style-span" style="color:#FF6600;"> information becomes less relevant further away from the source and is unlikely to spread throughout the network.</span> This holds true even in networks with power-law connectivity distributions where highly connected individuals, known as hubs, have the opportunity to potentially spread information to a large number of people. "<span class="Apple-style-span" style="color:#003300;"> (See <a href="http://www.hpl.hp.com/research/idl/papers/flow/">paper</a>)</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><br /></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><br /></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica">- Spetrum: retrieving different points of view from the blogosphere.</p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica">- Meta search engine for blogs. Would be nice to see memes in the search results. Predicting bloggers' interests in realtime difficult. </p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica">- Blog directory (blog category, blogging fusion, yahoo! directory) - Do these sites really work? Blogs are ephemeral.</p><p></p><p></p></div></span></span></span></span></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-25117135238277671472009-05-19T16:32:00.000-07:002009-05-19T16:33:50.356-07:00Duncan Watts keynote speech at ICWSM<div><span class="Apple-style-span" style="font-family:arial;color:#009900;"><b><br /></b></span></div><div><span class="Apple-style-span" style="font-family: arial; font-size: 12px; "><span class="Apple-style-span" style="color:#009900;"><b>(1) evolution of social network structure over time</b></span></span></div><div><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Work with </span></span><a href="http://gkossinets.googlepages.com/"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">Gueorgi Kossinets</span></span></a></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Social network changes over time. Averages about network structure (e.g., path length, clustering coefficient, node degree) stay rather steady over time, but individual quantities (e.g., rank) change rapidly.</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- What does this mean in terms of social applications?</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><br /></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><span class="Apple-style-span" style="color:#FF0000;"><b>(2) macro-sociological experiment on social influence</b></span></span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Music lab experiment -- very cute idea.</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- People do get influenced by others. However, the top favorites in the social influence world also did well in the independent world. (Same trend for the bottom ones in the popularity distribution. Lots of noise in medium hot content)</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><br /></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><b><span class="Apple-style-span" style="color:#3333FF;">(3) network survey on facebook</span></b></span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Launched "Friend Sense" app on Facebook and asked political preferences of users and their friends.</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><br /></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><b><span class="Apple-style-span" style="color:#006600;">(4) influence of financial rewards on performance</span></b></span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Crowd sourcing site Amazon's Mechanical Turk (AMT) is a fantastic place to launch quick and inexpensive social science studies.</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Increased pay resulted in more work, but not increased accuracy in work. <span class="Apple-style-span" style="font-family: Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">People always feel they are underpaid---</span></span><a href="http://en.wikipedia.org/wiki/Anchoring"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">the anchoring effect</span></span></a><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"> in psychology</span></span></span></span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; min-height: 14px; "><span class="Apple-style-span" style="font-family:arial;"><br /></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><b><span class="Apple-style-span" style="color:#660000;">(5) final remarks</span></b></span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- We moved from having too little data to having too much data.</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Facebook's 200+M users don't fit on a single memory for us to analyze!</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Having one more zero in your dataset (huge-data) doesn't mean that you're asking big questions. It's more important to ask important questions and set up experiment that can answer the question.</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Lada Adamic's recent work on diffusion of gesture on second life</span></span></p><p style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font: normal normal normal 12px/normal Helvetica; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Duncan said, <span class="Apple-style-span" style="color:#FF0000;">"</span></span></span><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><span class="Apple-style-span" style="color:#FF0000;">You might ask why are we even asking this obvious question. After many years of being a sociologist, nothing is obvious!</span></span></span><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><span class="Apple-style-span" style="color:#FF0000;">"</span></span></span></p><div><span class="Apple-style-span" style="font-family:arial;font-size:100%;color:#FF0000;"><span class="Apple-style-span" style="font-size: 12px;"><br /></span></span></div></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-9110474843447193912009-05-19T15:33:00.000-07:002009-05-19T15:38:24.537-07:00Tag cloud on Sam Gosling's tutorial #icwsm<div style="text-align: center;">Based on my <a href="http://socialmia.blogspot.com/2009/05/tutorial-psychology-of-social-media.html">summary</a> below, <a href="http://www.wordle.net/gallery/wrdl/865622/sam_gosling%27s_tutorial_icwsm">Wordle</a> says</div><a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhhFzhm9Jx_p2TzniTNdoGiU034jQhfx0Z0YxD3PaW5yG9qQQ9reAeG8oYMy2kpFuFfWxYRrWx7TvmlXjVFW0Qq-VUVwvToYODBoyLu6VcEb1QKTMS5rVRa1kDYhpRX-sTbgCYEy03muQtv/s1600-h/Picture+1.png"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 320px; height: 219px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhhFzhm9Jx_p2TzniTNdoGiU034jQhfx0Z0YxD3PaW5yG9qQQ9reAeG8oYMy2kpFuFfWxYRrWx7TvmlXjVFW0Qq-VUVwvToYODBoyLu6VcEb1QKTMS5rVRa1kDYhpRX-sTbgCYEy03muQtv/s320/Picture+1.png" border="0" alt="" id="BLOGGER_PHOTO_ID_5337667786942400434" /></a>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-58289018955323520832009-05-19T15:12:00.000-07:002009-05-19T16:34:10.232-07:00ICWSM'09 note - day two<div><span class="Apple-style-span" style="font-family:arial;color:#009900;"><b><br /></b></span></div><div><span class="Apple-style-span" style="font-family: arial; color: rgb(0, 0, 153); font-size: 12px; font-weight: bold; ">Panel Discussion</span></div><div><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Cameron Marlow@Facebook: Look at data to do informed design</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Q: How do users find information on site? Are social links actively used? What about search? </span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Slideshare A: We do see clicks from social networking sites, but most requests come from Google. So we put a lot of efforts and make sure our slides are easily searchable in Google. Front page is also important -- but that's more of a setting a tone about the site. (identity). We do lots of editorials in the front page. </span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Facebook A: news feed is definitely the most important. This is a huge optimization problem. Facebook provides two feeds: live feed, highlights. </span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><b><span class="Apple-style-span" style="color:#00CCCC;"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">Modeling Social Dynamics</span></span></span></b></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Interesting paper </span></span><a href="http://arxiv.org/pdf/0904.0016v1"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">"Stochastic models of user-contributory web sites"</span></span></a><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"> does modeling in social sites, similar to the famous Huberman et al. Science paper from 1998.</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- One thought. Modeling is based on visible data. How would a model of user behavior change if we were to consider both visible and invisible activity? (e.g., browsing takes up most of our online time!) </span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"> </p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Prediction of content popularity based on early data possible--we've seen the same trend in YouTube</span></span><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">. Is this a general trend in any massive content system?</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Flickr and YouTube are not about social relationship, but ultimately about information sharing. See evidence: paper "</span></span><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">Personal Information Management vs. Resource Sharing</span></span><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">"</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Social dimension (Shneiderman 2002): People search for other people's content on Flickr and YouTube. vs. People search for their own content on Delicious and Connotea. So is the purpose of tagging.</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Social computing & sustainability of sites: it's important to understand </span></span><i><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">what</span></span></i><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">, </span></span><i><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">where</span></span></i><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">, and </span></span><i><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">why</span></span></i><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"> people share. </span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Interesting </span></span><a href="http://infolab.stanford.edu/~mor/research/Nov_Naaman_Ye_ICWSM_2009_final.pdf"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">paper</span></span></a><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"> by Nov et al.</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Motivation for sharing, two axes: self or others, intrinsic or extrinsic. All four combinations have positive loop to user behavior.</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- This reminds me of comment from a director at Big Brothers. He said people post videos in YouTube, because they want to be the Steven Spielberg on the web.</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><br /></span></p></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-10471213145138292482009-05-18T14:07:00.000-07:002009-05-19T16:27:31.554-07:00ICWSM'09 note - day one<div><span class="Apple-style-span" style="color:#000099;"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><span class="Apple-style-span" style="font-size: small;">Some of the interesting comments and conversations I heard: </span></span></span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"><br /></span></span></div><div><b><span class="Apple-style-span" style="color:#009900;"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">Keynote speech</span></span></span></b></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- </span></span><a href="http://www.cs.cornell.edu/home/llee/"><span class="Apple-style-span" style="color:#000000;"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">Lillian Lee</span></span></span></a><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">'s new book on </span></span><i><a href="http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="color:#006600;"><span class="Apple-style-span" style="font-size: small;">Opinion mining and sentiment analysis</span></span></span></a></i><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"> available for free.</span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- In opinion mining, simply using bag of words is not enough. Structure of the text is also important, e.g., look for "still" and "however".</span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- ? and ! are negative feedback cues</span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"><br /></span></span></div><div><b><span class="Apple-style-span" style="color:#FF6600;"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">Community</span></span></span></b></div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- Over a billion dollars spent on influential viral marketing.</span></span><div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- My social cascade work got some attention. </span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- People's exposure to feed is the most critical factor in social cascade. Number of friends didn't play any significant role (i.e., accidental influential users?).<br /></span></span><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- Search "Facebook data" in Facebook. Join the Page!</span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- In a system like Yahoo! Answers, people reward answerers the most on topics like Music, Computer, and Medicine. However, Science questions were rewarded low.</span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"><br /></span></span></div><div><b><span class="Apple-style-span" style="color:#33CC00;"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">Psychology and Users</span></span></span></b></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- </span></span><a href="http://sociogeek.admin-mag.com/"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="color:#006600;"><span class="Apple-style-span" style="font-size: small;">sociogeek</span></span></span></a><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"> paper is based on 150,000 online surveys</span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- Like Stanley Milgram's familiar strangers in the offline world, we can find familiar strangers in </span></span><a href="http://www.public.asu.edu/~nagarwa6/FamiliarStrangers-ICWSM09.pdf"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="color:#006600;"><span class="Apple-style-span" style="font-size: small;">the online counterpart</span></span></span></a><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">. One key insight to do this is that it will benefit a lot of people. (Social networks have power-law degree distribution, where many people have few contacts. Knowing familiar stranger for people in the tail.)</span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- Download geotag data at </span><a href="http://wiki.dbpedia.org/Downloads32"><span class="Apple-style-span" style="font-size: small;">DBpedia</span></a></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"><br /></span></span></div><div><span class="Apple-style-span" style="color: rgb(0, 204, 204); font-weight: bold; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">Ranking</span></span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- CourseRank program in Stanford by </span></span><span class="Apple-style-span" style="border-collapse: collapse; "><a href="http://www.stanford.edu/~koutrika/"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="color:#006600;"><span class="Apple-style-span" style="font-size: small;">Georgia Koutrika</span></span></span></a><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">, popularly used by students to rank courses and plan course scheduling. </span></span><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"><span class="Apple-style-span" style="border-collapse: separate; "></span></span></span></span></div><div><span class="Apple-style-span" style="border-collapse: collapse; "><span class="Apple-style-span" style="border-collapse: separate; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- Some cute findings: (a) the larger the department size, the smaller individual contribution is. (b) students who got better grades gave higher ranking to the course. </span></span></span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- High median node degree in social networks can reflect spurious relationships? Search "top friends" application on Facebook.</span></span></div><div><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- Similar to </span></span><a href="http://social.cs.uiuc.edu/people/gilbert/30"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="color:#006600;"><span class="Apple-style-span" style="font-size: small;">"Predicting Tie Strength With Social Media"</span></span></span></a><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"> from CHI2009, paper "</span></span><span class="Apple-style-span" style="font-weight: 500; line-height: 15px; -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">Using Transactional Information to Predict Link Strength in Online Social Networks" looked at wall and photo posts to guess tie strength. They used Top Friends app results as the ground truth. </span></span></span></div><div><span class="Apple-style-span" style="font-weight: 500; line-height: 15px; -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"><br /></span></span></span></div><div><span class="Apple-style-span" style="font-weight: 500; line-height: 15px; -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;"><br /></span></span></span></div><div><span class="Apple-style-span" style="line-height: 15px; -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px; "><b><span class="Apple-style-span" style="color:#CC33CC;"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">Some thoughts on tie strength and influential users</span></span></span></b></span></div><div><span class="Apple-style-span" style="font-weight: 500; line-height: 15px; -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- After listening to some of the talks, I had the following thought. Network-activity based studies on user interaction mostly focus on visible interactions, e.g., wall posts and photo tagging. This might be a good predictor when we want to measure the strength of ties between individuals (or simply ask who are my best friends?). Then, what does a tie strength mean in terms of viral marketing? Are friends of stronger ties a good indicator of influential people? </span></span></span></div><div><span class="Apple-style-span" style="font-weight: 500; line-height: 15px; -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- I get a feeling that this is not the case. Based on the Facebook paper "</span></span><span class="Apple-style-span" style="font-weight: normal; line-height: normal; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">Gesundheit! Modeling Contagion Through Facebook News Feed", we also learned that adoption of information has to do with simple exposure (but not on how popular a friend is). Indeed, exposure is an important and dominant factor in social networks. </span></span><span class="Apple-style-span" style="font-weight: 500; line-height: 15px; -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px; "><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">My recent work using social network clickstream data showed that more than 80% of user activity had to do with "silent" browsing---which a crawl-based study cannot capture. Which friends are important then in viral marketing? Best friends or those whom I get exposed to? </span></span></span></span></span></div><div><span class="Apple-style-span" style=" font-weight: 500; line-height: 15px; -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px;"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size: small;">- Obviously there will be lots of overlap between the two groups. But, my conclusion today is that influential users are the ones whom I "actively" get exposed to. This means friends whom I follow up on their updates by visiting their pages. Would accidental influentials would be those whom I got opportunistically exposed to (e.g., Facebook's news feed) and joined cascades?</span></span></span></div><div><span class="Apple-style-span" style="font-family:arial;font-size:100%;"><span class="Apple-style-span" style=" font-weight: 500; line-height: 15px; -webkit-border-horizontal-spacing: 2px; -webkit-border-vertical-spacing: 2px;font-size:13px;"><br /></span></span></div></div></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-89179497727033864862009-05-18T06:06:00.001-07:002009-05-18T14:17:26.273-07:00Tutorial: Psychology of Social Media<p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">This was a 2-hour long </span><a href="http://www.icwsm.org/2009/tutorials.shtml"><span class="Apple-style-span" style="font-size:small;">tutorial</span></a><span class="Apple-style-span" style="font-size:small;"> at ICWSM'09, delivered by </span><a href="http://homepage.psy.utexas.edu/homepage/faculty/gosling/"><span class="Apple-style-span" style="font-size:small;">Sam Gosling</span></a><span class="Apple-style-span" style="font-size:small;"> (UT Austin) and Kate Niederhoffer (Nielsen Online). It was refreshing to listen to people in different fields who look at the very same problems (audience span psychologists, sociologists, computer scientists, wall street analysts). Sharing my note below.</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><br /></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="color:#CC0000;"><b><span class="Apple-style-span" style="font-size:small;">1. Social media (like Facebook, Twitter) is serving some psychological needs. What?</span></b></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Everybody spends their day by doing something. What do they do and what does that tell us about the person?</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="color:#000000;"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Psychologist </span><a href="http://en.wikipedia.org/wiki/Abraham_Maslow"><span class="Apple-style-span" style="font-size:small;">Maslow</span></a></span></span><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"> says everyone has his own "hierarchy of human needs". This means that we all have our own set of priority in the action set.</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="color:#006600;"><b><span class="Apple-style-span" style="font-size:small;">2. Fundamental social needs of people: People want to (1) get along and (2) get ahead. </span></b></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Get along meaning, socialize. Get ahead meaning, step up in the social hierarchy level. How could this be projected in social media?</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- What do our friends tell us about us? In a lab test, people with low self-esteem wanted to hang out with those who gave negative feedback, against those who gave positive feedback. Homophily?</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><b><span class="Apple-style-span" style="color:#000099;"><span class="Apple-style-span" style="font-size:small;">3. People want to be seen accurately, than being projected more positively than they actually are. </span></span></b></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Sam looked at webpages of people, contacted the webpage owners, interviewed them and asked what person they'd like to be, asked their friends what the person is like, and found the projection is rather accurate.</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Would this hold in Facebook too? </span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="color:#660000;"><b><span class="Apple-style-span" style="font-size:small;">4. What are identity claims? </span></b></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- This mean the things about you that is deliberately chosen for other people. Examples are t-shirts that you wear, bumper sticker, webpage, pictures you hang in your room, etc.</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Music is not an identity claim. It's a tone-setter. It is typically chosen to set our mood in a particular way. You listen to upbeat music when you head out for clubbing vs. when you are home, you may listen to a different music.</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Books are inadvertent identity claims. This is not so deliberate, but is a behavioral residue over a longer time period. Just by looking at the variety, topics, and organization of books at home, we can tell so much about the person. For example, does the person read a wide variety of books (openness), are the books actually read, are there notes, how are they ordered -- neatly or messy, are cheesy books hidden behind, etc.</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><b><span class="Apple-style-span" style="color:#00CCCC;"><span class="Apple-style-span" style="font-size:small;">5. Language is the backbone of our expression. </span></span></b></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- There are a lot of approach to understand human behaviors based on their linguistic styles. The use of pronouns (which we think is garbage word) can already reveal sex and age of a person. </span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- 140 characters in Twitter can tell so much.</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">- Usage of word "I" : more common among females. more common among people with low social status. Usage of word "We": group action. but also reflects future trouble (meaning, followed by "but")</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="color:#FFFF00;"><b><span class="Apple-style-span" style="font-size:small;">6. What do we need to know to know a person?</span></b></span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">(a) big five traits (openness = creativity + interest + opinions, conscientiousness = daily life, extraversion = documenting life, agreeableness = polite topic covered, neuroticism = cathartic or auto-therapeutic purpose - standard way, look <span class="Apple-style-span" style="font-family: Helvetica; ">Goldberg 1992, Costa and McCrae 1992<span class="Apple-style-span" style="font-family: arial; ">)</span></span></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">(b) personal concerns (visions and goals)</span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;">(c) identity (personal myth - very difficult to measure)</span></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><br /></span></p><p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica"><span class="Apple-style-span" style="font-family:arial;"><br /></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><span class="Apple-style-span" style="font-family:arial;"><span class="Apple-style-span" style="font-size:small;"><br /></span></span></p> <p style="margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px Helvetica; min-height: 14.0px"><br /></p>Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-2696443532579911264.post-68050498931519105452009-05-15T06:08:00.000-07:002009-05-18T06:26:39.690-07:00John WilkesJohn (who is my Facebook friend!) visited MPI-SWS and gave a talk about data center storage design. His theme was the design of<span class="Apple-style-span" style="color:#FF6600;"> </span><b><span class="Apple-style-span" style="color:#FF9900;">"light out data center"</span></b>: can we design a data center that will continue to work when all the light goes out (without human intervention?) This means automated control. <div><br /><div>He also shared one very interesting thought. SLAs are ad-hoc and random (e.g., service outage should be within three-nines, or 0.999 availability). Sometimes meeting this arbitrary number means investing arbitrary money and effort. So why not have flexible SLA and see what are the losses as we fail to meet the needs?---what a refreshing idea. John further said cost of not meeting the needs at Google may mean loosing customers. He said "users are one click away from leaving to other services" and the cause of leaving may depend on many axes including a particular shade of blue they use in the logo.</div><div><br /><div><br /></div><div><br /></div></div></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-57740362614785084472009-05-09T04:07:00.000-07:002009-05-09T04:12:05.912-07:00Chocolate pie chartI have been quite busy with a paper deadline, advisory board visit, etc. While looking for a nice graphics tool to plot a pie chart, I came across this irresistible chart made of 70% milk + 20% dark + 10% milk chocolate. I'd love to make a program to generate this.<div><a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgv2kfSp2UqbbsORF92rYKcm1Wo5JuJUNDvXHnXHWWghc3pkMf5ZaTYdFELeu66CUrNFuSFvCXKeRe0WylX-Mz1oNy9ANriq7521l8aAaAELk9gB_HPy8bh5_J4xr3NKuVbTNzO-vT70JkX/s1600-h/cpc_01.jpg"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 320px; height: 209px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgv2kfSp2UqbbsORF92rYKcm1Wo5JuJUNDvXHnXHWWghc3pkMf5ZaTYdFELeu66CUrNFuSFvCXKeRe0WylX-Mz1oNy9ANriq7521l8aAaAELk9gB_HPy8bh5_J4xr3NKuVbTNzO-vT70JkX/s320/cpc_01.jpg" border="0" alt="" id="BLOGGER_PHOTO_ID_5333779414378071746" /></a>If tempted to put on your paper, consult <a href="http://maryandmatt.net/store/cpc.html">Mary and Matt</a> store</div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-48770532538628147642009-04-18T06:29:00.000-07:002009-04-18T06:42:02.811-07:00Social Mobile Web 2009<div>I am looking forward to reviewing interesting papers for the <b><span class="Apple-style-span" style="font-weight: normal;"><a href="http://www.thesocialmobileweb.org/">Social Mobile Web 2009</a> </span><span class="Apple-style-span"><span class="Apple-style-span" style="font-weight: normal;">workshop. We have very cool Program Committee members and I am happy to be part of the group. Mobile devices are everywhere and I would love to see innovations coming out in this area.</span></span></b></div><div><br /></div><div><b>Call for Paper:</b></div>The mobile space is evolving at an astonishing rate. At present there are over 3.5 billion mobile subscribers worldwide and with continued advances in devices, services and billing models, the mobile web looks set to inspire a new age of anytime, anywhere information access. The inherent characteristics of mobile phones enable new types of interactions, e.g. mobile phones are personal to the individual, they are always on and always connected. And as such we are seeing a shift towards mobile devices for social mediated tasks. The world is also witnessing an explosion in social web services. Online social networking sites such as Facebook and MySpace continue to experience huge increases in usage, with more and more users seeking novel ways of interacting with their friends and family.<br /><br /><b>Topics:</b><br />Novel social interactions on mobile devices.<br />Social mobile content sharing and distribution services.<br />Context aware mobile services - beyond location based services.<br />Social mobile search and social mobile browsing.<br />User evaluations of social mobile services.<br />Mobile user interfaces that incorporate social elements.<br />Mobility and social networks.<br />Models of mobile social behavior and mobile traces.<br />Urban gaming, mobile mixed reality, etc.<br />Innovative social mobile applications.<div><br /></div><div><b>Deadline: </b>May 11th 2009</div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-80480330251755542009-04-09T15:09:00.000-07:002009-04-09T15:24:22.437-07:00Plotting Venn diagrams<div>I'm having fun with this marvelous program <a href="http://www.neoformix.com/Projects/TwitterVenn/view.php">Twitter Venn</a>: a program that takes in search keywords and plots a Venn diagram that represents the overlap in the use of keywords in Twitter messages. Making of this application appear in <a href="http://www.neoformix.com/2008/TwitterVenn.html">Jeff Clark's blog</a>.</div><a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjz6XWYwQ1UKhLP499DEbu8qDV_Uyu_udpE8nZ_j-UMj4T4CtHeZUulDYPeb6JRB-LP9MfItuvWuhxPejcZ1LzAPvxijdNfUN36AAC9u2S0CR_2TLILL2EkMnva-W8U-OReyzHM6kGCzL-5/s1600-h/Picture+1.png"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 320px; height: 179px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjz6XWYwQ1UKhLP499DEbu8qDV_Uyu_udpE8nZ_j-UMj4T4CtHeZUulDYPeb6JRB-LP9MfItuvWuhxPejcZ1LzAPvxijdNfUN36AAC9u2S0CR_2TLILL2EkMnva-W8U-OReyzHM6kGCzL-5/s320/Picture+1.png" border="0" alt="" id="BLOGGER_PHOTO_ID_5322818719187349138" /></a><div>Twitter makes the search possible online (<a href="http://search.twitter.com/">search.twitter.com</a>), which allowed this to happen. Very nice. I want to make more sophisticated Venn diagram: similar to the Google's key word trend, I'd like to see how the relationship evolves. Or a given keyword, I'd like to see the hottest matching set of keywords. </div><div><br /></div><div>BTW, what went happened with such small overlap in cat and sleep? Am I the only one with a cat that sleeps all day long? </div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-23101775838516819932009-04-07T07:37:00.000-07:002009-04-07T07:53:24.894-07:00Crawling YouTube<div><br /></div><div>I started crawling YouTube site (again!) to get video comments. This time, I'm using <a href="http://code.google.com/apis/gdata/">Google Data API</a> and properly approaching the site. The API makes the code very short and I like that it runs fast. Here is a sample code for getting comments.</div><div><span class="Apple-style-span" style="font-size:small;"><br /></span></div><div><span class="Apple-style-span" style="font-family:'courier new';"><span class="Apple-style-span" style=""><span class="Apple-style-span" style=""><span class="Apple-style-span" style="color: rgb(255, 102, 0);"><span class="Apple-style-span" style="font-size:small;">comment_feed=yt_service.GetYouTubeVideoCommentFeed(video_id=video_id)<br />for comment_entry in comment_feed.entry:<br /></span></span></span></span></span><span class="Apple-style-span" style=""><span class="Apple-style-span" style="color: rgb(255, 102, 0);"><span class="Apple-style-span" style="font-size:small;"> </span></span></span><span class="Apple-tab-span" style="white-space:pre"><span class="Apple-style-span" style="font-family:'courier new';"><span class="Apple-style-span" style=""><span class="Apple-style-span" style=""><span class="Apple-style-span" style="color: rgb(255, 102, 0);"><span class="Apple-style-span" style="font-size:small;"> </span></span></span></span></span></span><span class="Apple-style-span" style="font-family:'courier new';"><span class="Apple-style-span" style=""><span class="Apple-style-span" style=""><span class="Apple-style-span" style="color: rgb(255, 102, 0);"><span class="Apple-style-span" style="font-size:small;">print comment_entry.ToString()</span></span></span></span></span><span class="Apple-style-span" style="font-size:medium;"><span class="Apple-style-span" style="color: rgb(255, 102, 0);"><br /></span></span><br /></div><div>Strangely, the code gives me a subset of video comments (say 100), even when there are thousands of comments I can see in YouTube. I'll have to go through the documentation or switch back to wget and urlopen. </div><div><br /></div><div>PS: Crawling YouTube is rather distracting. I ended up watching 20 cat videos and participated in viral spreading of those videos (i.e., sending spam video links to friends). My favorite of the day: <a href="http://www.youtube.com/watch?v=Lr6c_TJfLUI">cat massage</a>.</div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-20949793664901764282009-04-06T15:20:00.000-07:002009-04-06T16:34:11.607-07:00Summary of the ACM SNS workshop<div><br /></div><div>Here is my summary of the recent <a href="http://www.eecs.harvard.edu/~stein/SocialNets-2009/program.html">ACM Social Network Systems 2009</a></div><div><br /></div><div><br /></div><div><span class="Apple-style-span" style="color: rgb(0, 0, 153);"><span class="Apple-style-span" style="font-weight: bold;">* Security at a Large Social Network, Tao Stein (Facebook)</span></span><span class="Apple-style-span" style="font-weight: bold;"> </span></div>Tao's talk started with the "<a href="http://www.nytimes.com/2009/03/29/technology/internet/29face.html?_r=1&partner=rss&emc=rss">The Road to 200 Million</a>" article from NYT. Facebook has three data centers, each in charge of a major continent: VA (asia) SC (europe), SF (us). Data consistency is hard to achieve, so Facebook only uses a single server for writing and the other servers for read only operations. Servers use 25TB of RAM for MySQL.<br /><br />There are obviously lots of attacks on Facebook. Their long term goal is to achieve that one identity in the system corrsponds to one real identity. In every security policy, trade-off is at site integrity and user experience: throwing in more CAPCHA will increase security, but then users experience will degrade. <div><div><br /></div><div>The #1 problem is at account takeovers. Here are a few example attacks:</div><div><div>(a) photo/video scam (e.g., "This applet will show you which friends viewed your photo")<br />(b) 419 attack by Nigirian spammers (e.g., "I am lost in London, please send me $1000 to Western Union") <br />(c) koobface (a botnet that sends spam URLs)<br />(d) fake chain letter (e.g., "Facebook is overpopulated")<br /><br />Often users use the same login credentials across multiple sites. (Yes, I do too!) So if one site gets compromised, then all are compromised. Because most sites force users to use complex password, uses end up using a common password across sites. Facebook tries a lot to educate users with sophisticated privacy setting. </div><div> </div><div>How is the network security different in online social network? (a) education and (b) coefficient (= strength of ties) in the social graph. <br /><br /><span class="Apple-style-span" style="font-weight: bold;"><span class="Apple-style-span" style="color: rgb(0, 0, 153);"><br /></span></span></div><div><span class="Apple-style-span" style="font-weight: bold;"><span class="Apple-style-span" style="color: rgb(0, 0, 153);">*Botnets vs. Social Networks, Elie Bursztein (Stanford)</span></span><br />Second talk was by Elie, who is a post-doc at Stanford. Elie gave a brief overview on his research: how to turn online social networks into a botnet. Elie found that a number of existing systems (e.g., MSN messenger) have vulnerabilities: a malicious user can send codes to turn his friends' (and their friends') host machines into a botnet. <br /><br /><br /><span class="Apple-style-span" style="font-weight: bold;"><span class="Apple-style-span" style="color: rgb(0, 0, 153);">* Eight Friends Are Enough: Social Graph Approximation via Public Listings, Joseph Bonneau (University of Cambridge)</span></span><br />I greatly enjoyed this talk. The talk demonstrated how revealing limited information about a social network (e.g., Facebook's public listing, which shows 8 random friends of a user) can say so much about the entire social graph structure. </div><div><br /></div><div>I've heard a new term "social graph privacy". It means to prevent data aggregators from reconstructing large portions of the social graph, composed of users and their friendship links. Joseph said protecting social graph is more difficult than protecting personal data, because personal data can be managed individually by users, while information about a user's place in the social graph can be revealed by any of the user's friends. This work <a href="http://www.guardian.co.uk/technology/2009/apr/02/facebook-profiles-personal-information">got popular in media</a>. I also saw BBC interview with the authors.<br /></div></div></div>Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-2696443532579911264.post-40789487073787063262009-03-30T03:46:00.000-07:002009-03-30T03:58:55.631-07:00Geographic distance of social ties<a href="http://en.wikipedia.org/wiki/Barry_Wellman">Barry Wellman</a> published an interesting article on geographic distance of social ties. Below is from his <a href="http://www.chass.utoronto.ca/~wellman/publications/index.html">publication webpage</a>. I'm all intrigued. I'll be soon posting stories on the Flickr counterpart.<br /><br /><span class="Apple-style-span" style="color: rgb(255, 0, 0);">"Does Distance Still Matter in the Age of the Internet?"</span> (Diana Mok, Juan-Antonio Carrasco and Barry Wellman, <a href="http://chass.utoronto.ca/~wellman/publications/has_distance_died/Distance.pdf">Urban Studies, 2009</a>). Our study is part of the broad debate about the role of distance and technology for interpersonal contact. To the best of our knowledge, this is the first study that systematically and explicitly compares the role of distance in social networks pre- and post-Internet. We analyze the effect of distance on the frequency of email, phone, face-to-face and overall contact in personal networks, and we compare the findings with its pre-Internet counterpart whose data were collected in 1978 in the same East York, Toronto locality. We use multilevel models with spline specification to examine the nonlinear effects of distance on the frequency of contact. We compare these effects for both very close and somewhat close ties, and for different role relationships: immediate kin, extended kin, friends and neighbours. The results show that email contact is generally insensitive to distance, but tends to increase for transoceanic relationships greater than 3,000 miles apart. Face-to-face contact remains strongly related to short distances (within five miles), while distance has little impact on how often people phone each other at the regional level (within 100 miles). The study concludes that email has only somewhat altered the way people maintain their relationships. The frequency of face-to-face contact among socially-close friends and relatives has hardly changed between the 1970s and the 2000s, although the frequency of phone contact has slightly increased. Moreover, the sensitivity of these relationships to distance has remained similar, despite the communication affordances of the Internet and low-cost telephony.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-71777908273682438882009-03-27T13:11:00.001-07:002009-04-05T06:43:43.524-07:00ACM Social Network Systems 2009 WorkshopTao Stein (@Facebook) and I are organizing a wonderful workshop in Nuremberg next Tuesday. We will have 8 paper presentations and 2 invited talks on security -- all look very interesting.<div><a href="http://www.eecs.harvard.edu/~stein/SocialNets-2009/program.html">ACM Social Network Systems 2009</a><div><div><div><div><span class="Apple-style-span" style="color: rgb(0, 0, 153); font-weight: bold;"><br /></span></div><div><span class="Apple-style-span" style="color: rgb(0, 0, 153); font-weight: bold; ">Program</span><br /></div><span class="Apple-style-span" style="color: rgb(0, 0, 153);"><span class="Apple-style-span" style="color: rgb(0, 0, 0);">0900 - 1000: Security at a Large Social Network, Tao Stein (Facebook)<br />1000 - 1030: Botnets vs. Social Networks, Elie Bursztein (Stanford)<br /></span><span class="Apple-style-span" style="color: rgb(51, 51, 51);">1030 - 1100: Break</span></span></div><div><span class="Apple-style-span" style="color: rgb(0, 0, 153);"><span class="Apple-style-span" style="color: rgb(51, 51, 51);"><br /></span><span class="Apple-style-span" style="font-weight: bold;">1100 - 1230: Privacy and Security</span><br /><span class="Apple-style-span" style="color: rgb(0, 0, 0);">Eight Friends Are Enough: Social Graph Approximation via Public Listings</span></span><br /><span class="Apple-style-span" style="color: rgb(51, 51, 51);">Joseph Bonneau, Jonathan Anderson, Ross Anderson, Frank Stajano (University of Cambridge)</span><br /></div><div><span class="Apple-style-span" style="">Anonymous Opinion Exchange over Untrusted Social Networks</span><br /><span class="Apple-style-span" style="color: rgb(51, 51, 51);">Mouna Kacimi (Max Planck Institute for Informatics), Stefano Ortolani (Vrije Universiteit), Bruno Crispo (University of Trento)<br /></span><span class="Apple-style-span" style="color: rgb(0, 0, 153);"><span class="Apple-style-span" style="color: rgb(0, 0, 0);">PeerSoN: P2P Social Networking - Early Experiences and Insights</span><br /></span><span class="Apple-style-span" style="color: rgb(51, 51, 51);">Sonja Buchegger, Doris Schiöberg (TU Berlin, Deutsche Telekom Laboratories), Le Hung Vu (EPFL), Anwitaman Datta (NTU Singapore)</span></div><div><span class="Apple-style-span" style="color: rgb(0, 0, 153);"><span class="Apple-style-span" style="color: rgb(51, 51, 51);">1230 - 1330: Lunch</span></span></div><div><span class="Apple-style-span" style="color: rgb(0, 0, 153);"><span class="Apple-style-span" style="color: rgb(51, 51, 51);"><br /></span><span class="Apple-style-span" style="font-weight: bold;">1330 - 1500: The Ties that Bind</span><br /><span class="Apple-style-span" style="color: rgb(0, 0, 0);">On the Strength of Weak Ties in Mobile Social Networks</span><br /></span><span class="Apple-style-span" style="color: rgb(51, 51, 51);">Stratis Ioannidis, Augustin Chaintreau (Thomson)</span><br /><span class="Apple-style-span" style="">Centralities: Capturing the Fuzzy Notion of Importance in Social Graphs</span><br /><span class="Apple-style-span" style="color: rgb(51, 51, 51);">Erwan Le Merrer (INRIA), Gilles Tredan (University of Rennes 1)</span><br /><span class="Apple-style-span" style="">Buzztraq: Predicting Geographical Access Patterns of Social Cascades using Social Networks</span><br /><span class="Apple-style-span" style="color: rgb(51, 51, 51);">Nishanth Sastry, Eiko Yoneki, Jon Crowcroft (University of Cambridge)<br />1500 - 1530: Break</span></div><div><span class="Apple-style-span" style="color: rgb(51, 51, 51);"><br /></span><span class="Apple-style-span" style="color: rgb(0, 0, 153);"><span class="Apple-style-span" style="font-weight: bold;">1530 - 1630: Personalizing Search</span><br /><span class="Apple-style-span" style="color: rgb(0, 0, 0);">Towards Personalized Peer-to-Peer Top-K Processing</span><br /></span><span class="Apple-style-span" style="color: rgb(51, 51, 51);">Xiao Bai, Marin Bertier (INSA de Rennes), Rachid Guerraoui (EPFL, Switzerland), Anne-Marie Kermarrec (INRIA Rennes, France)<br /></span><span class="Apple-style-span" style="">Toward Personalized Query Expansion</span><br /><span class="Apple-style-span" style="color: rgb(51, 51, 51);">Marin Bertier (INSA de Rennes, France), Rachid Guerraoui (EPFL, Switzerland), Anne-Marie Kermarrec, Vincent Leroy (INSA de Rennes, France)</span><br /></div></div></div></div>Unknownnoreply@blogger.com1tag:blogger.com,1999:blog-2696443532579911264.post-36283240555702730822009-03-27T02:43:00.000-07:002009-04-05T06:44:13.827-07:00Flash Floods and Ripples<div>My recent paper investigates the role of blogosphere as a social media. <br /></div><span class="Apple-style-span" style="color: rgb(255, 0, 0);"><a href="http://an.kaist.ac.kr/~mycha/docs/icwsm09data-cha.pdf">Flash Floods and Ripples: The Spread of Media Content through the Blogosphere.</a> </span><span class="Apple-style-span" style="color: rgb(0, 0, 102);"></span><div><span class="Apple-style-span" style="color: rgb(0, 0, 102);">Meeyoung Cha, Juan Antonio Navarro Perez, and Hamed Haddadi<br />In Proc. of the AAAI Conference on Weblogs and Social Media (ICWSM) Data Challenge Workshop, San Jose, May 2009<br /></span><br />We tracked down the occurrences of YouTube videos in blog posts and named the two key patterns we found: <span class="Apple-style-span" style="font-style: italic;">flash floods</span> and <span class="Apple-style-span" style="font-style: italic;">ripples</span>. Flash floods represent rapid cascade events, which we see in the spread of political videos. Ripples represent a slow propagation, which we see for old music videos. So just how rapid are flash floods? The graph below shows the time it took to propagate YouTube videos, based on their topics. News videos propagate by the hour and stop spreading after a week. Music videos continue to spread after several months.<br /><a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiePR_pZvfNdwxwtTXFptkcmqkB-5BzQusXTYqpzFBvcmXWjrSypCTuY6CLaYaAydq6yDxvKufRqqoCpyJAdOpqLzYadZUmzLkthcG74Ym2cD4K-Xeiv4Ul8YT0skiXQ4cn_tgrTGKwX4l8/s1600-h/Picture+4.png"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 357px; height: 190px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiePR_pZvfNdwxwtTXFptkcmqkB-5BzQusXTYqpzFBvcmXWjrSypCTuY6CLaYaAydq6yDxvKufRqqoCpyJAdOpqLzYadZUmzLkthcG74Ym2cD4K-Xeiv4Ul8YT0skiXQ4cn_tgrTGKwX4l8/s400/Picture+4.png" border="0" alt="" id="BLOGGER_PHOTO_ID_5317875255907000354" /></a><div>Plot below shows the propagation pattern of one of the popular YouTube videos in the blogosphere. The video was an advertisement made by the Republican party for the U.S. Presidential Election, 2008. Like other news videos, it spread quickly in the network and was blogged about 79 times within a week! </div><a onblur="try {parent.deselectBloggerImageGracefully();} catch(e) {}" href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh48thpmEGSkB8lk572BSqMo5xOma-YsvmxcxIr-ba2v54J_lCV5IQv9VdlscPwM9e28VxrsgAXF6xl4uLMQrFm1e5OVIlnJ8WRIujzHMvFWT7rWjdz0GJ4_Xu3Vn5m2ONOoPiPDO_WNm1y/s1600-h/Picture+3.png"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 320px; height: 301px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh48thpmEGSkB8lk572BSqMo5xOma-YsvmxcxIr-ba2v54J_lCV5IQv9VdlscPwM9e28VxrsgAXF6xl4uLMQrFm1e5OVIlnJ8WRIujzHMvFWT7rWjdz0GJ4_Xu3Vn5m2ONOoPiPDO_WNm1y/s320/Picture+3.png" border="0" alt="" id="BLOGGER_PHOTO_ID_5317875001786550210" /></a></div>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-2696443532579911264.post-1862362641652270222009-03-26T08:44:00.001-07:002009-03-26T09:12:55.824-07:00Tag cloud on flickr researchAbstract of my recent submission on information propagation.<br /><br /><a href="http://www.wordle.net/gallery/wrdl/692803/paper_abstract_2" title="Wordle: paper abstract 2"><img src="http://www.wordle.net/thumb/wrdl/692803/paper_abstract_2" alt="Wordle: paper abstract 2" style="padding:4px;border:1px solid #ddd" /></a><br /><br />ps: loving wordle!Unknownnoreply@blogger.com0