Social Mobile Web 2009

I am looking forward to reviewing interesting papers for the Social Mobile Web 2009 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.

Call for Paper:

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.

Topics:
Novel social interactions on mobile devices.
Social mobile content sharing and distribution services.
Context aware mobile services - beyond location based services.
Social mobile search and social mobile browsing.
User evaluations of social mobile services.
Mobile user interfaces that incorporate social elements.
Mobility and social networks.
Models of mobile social behavior and mobile traces.
Urban gaming, mobile mixed reality, etc.
Innovative social mobile applications.

Deadline: May 11th 2009

Plotting Venn diagrams

I'm having fun with this marvelous program Twitter Venn: 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 Jeff Clark's blog.

Twitter makes the search possible online (search.twitter.com), 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. 

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?  

Crawling YouTube

I started crawling YouTube site (again!) to get video comments.  This time, I'm using Google Data API 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.

comment_feed=yt_service.GetYouTubeVideoCommentFeed(video_id=video_id)
for comment_entry in comment_feed.entry:
print comment_entry.ToString()

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. 

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: cat massage.

Summary of the ACM SNS workshop

Here is my summary of the recent ACM Social Network Systems 2009

* Security at a Large Social Network, Tao Stein (Facebook) 

Tao's talk started with the "The Road to 200 Million" 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.

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.   

The #1 problem is at account takeovers. Here are a few example attacks:

(a) photo/video scam (e.g., "This applet will show you which friends viewed your photo")
(b) 419 attack by Nigirian spammers (e.g., "I am lost in London, please send me $1000 to Western Union") 
(c) koobface (a botnet that sends spam URLs)
(d) fake chain letter (e.g., "Facebook is overpopulated")

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.   

  

How is the network security different in online social network? (a) education and (b) coefficient (= strength of ties) in the social graph. 

*Botnets vs. Social Networks, Elie Bursztein (Stanford)
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. 


* Eight Friends Are Enough: Social Graph Approximation via Public Listings, Joseph Bonneau (University of Cambridge)
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. 

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 got popular in media.  I also saw BBC interview with the authors.