Iran election in Twitter

One 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.)

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?

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.

We initially expected to see a power-law distribution 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.

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.

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).

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.

The 1st International Workshop on Mining Social Media

If you're working on social networks and data mining, here's a perfect workshop to consider at a wonderful south of Spain:

Mining Social Media (MSM'09)
Paper submission deadline: September 6th
Venue: November 9th, 2009 Seville, Spain

Timely research

I'm reading a nature paper that was published yesterday, Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic.

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.

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.

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.

Twitter FAQ

So it happened that I finally came across something to ReTweet about. My very first use of "RT". For those of you who are not familiar with Twitter codes, here is a brief and useful tutorial on Twitter FAQ.

RT means ReTweet or Repeat.

Copy the message you want your want to retweet and start with "RT @UserName"

OH means OverHeard

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.

HT means HeardThrough

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.

Starting with @ sign means Reply

When you want to reply to someone, start your tweet with @UserName.

Hash Tags (#) help to designate topics that people might search for

When you head to conferences, look for hash tags.

#FollowFriday means recommended followers

This is like book recommendations from a friend. You can list your top followers in the form of @UserName after this tag.

ICWSM'09 note - day three

Leveraging Diversity

- Ideas embracing diversity in opinions getting popular: Sidelines, Google moderator

- Goal is to project an accurate proportion of users supporting different opinions. let users get an exposure to challenges or new ideas.

- Quick Q: do people really like diversity?

- Similar work on news media bias "NewsCube" (CHI'09). This work looks at content to aggregate similar news and project different themes news articles. Sidelines paper simply looks at voting counts.

- "Diversity in user activity and content quality in online communities" by Tad Hogg. How many activities does a user do per day?

- Users with very little online time or little activity harder to model (i.e., difficulty of modeling in a heavy-tail distribution).

- 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 paper)

- Check out Lada Adamic's write-up on social networks at HP labs.

"Unlike viruses, which spread indiscriminately from host to host, pieces of information are propagated by people who find them interesting 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, information becomes less relevant further away from the source and is unlikely to spread throughout the network. 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. " (See paper)

- Spetrum: retrieving different points of view from the blogosphere.

- Meta search engine for blogs. Would be nice to see memes in the search results. Predicting bloggers' interests in realtime difficult.

- Blog directory (blog category, blogging fusion, yahoo! directory) - Do these sites really work? Blogs are ephemeral.

Duncan Watts keynote speech at ICWSM

(1) evolution of social network structure over time

- Work with Gueorgi Kossinets

- 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.

- What does this mean in terms of social applications?

(2) macro-sociological experiment on social influence

- Music lab experiment -- very cute idea.

- 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)

(3) network survey on facebook

- Launched "Friend Sense" app on Facebook and asked political preferences of users and their friends.

(4) influence of financial rewards on performance

- Crowd sourcing site Amazon's Mechanical Turk (AMT) is a fantastic place to launch quick and inexpensive social science studies.

- Increased pay resulted in more work, but not increased accuracy in work. People always feel they are underpaid---the anchoring effect in psychology

(5) final remarks

- We moved from having too little data to having too much data.

- Facebook's 200+M users don't fit on a single memory for us to analyze!

- 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.

- Lada Adamic's recent work on diffusion of gesture on second life

- Duncan said, "You might ask why are we even asking this obvious question. After many years of being a sociologist, nothing is obvious!"

Tag cloud on Sam Gosling's tutorial #icwsm

Based on my summary below, Wordle says