Social Media Research Blog

Book Review: Tribes by Seth Godin

I was quite impressed by Seth Godin's small book called "The Dip" so I decided to get a copy of Tribes. The book describes a tribe as: any group of people, large or small, who are connected to one another, a leader and an idea. It almost sounds like the definition of a tribe is identical to that of a community in the social network literature. The powerful idea in this book is that anyone and yes, YOU! can lead a tribe. Godin describes leaders as heretics who challenge the status quo in organizations. The internet and social media tools today make it far easier for all of us to be leaders and inspire others to do the same in their own workplace, organization, school or non profit. Blogs, social networking sites, self publishing tools and twitter are only a few examples of new tools that are at your disposal. This book isnt a "how-to" manual or a dummies guide to leadership but it presents plenty of examples of how individuals have brought about change. In all the examples one thing that is striking is how true leadership is a lot about communication. Leadership requires us to listen and to facilitate communication in a tribe. Overall it was an average read and I must say the book fell short of my expectations. One problem with the style of writing is that it was more like a series of blog posts on this topic rather than a book. The sections are just too hotchpotch and lack depth. Perhaps it would have served much better as a blog since there were several paragraphs and references where individuals and examples seemed to have been mentioned in passing without much detail. I think the author assumed a lot from the reader in terms of familiarity with both his previous works and the references made. Not having internet connectivity while reading this book was equally frustrating since I could not research further on these examples. If you are already a fan of Godin's work you may enjoy it.

ICWSM 2009 Data Challenge

A quick note:

We have worked with Spinn3r to create a dataset for this year's ICWSM conference. More information is available on the site. We look forward to active participation by the social media research community and hope that you would take advantage of this fabulous resource. Also stay tuned for another awesome dataset to be released shortly.

Continuing the ICWSM tradition, ICWSM 2009 is making a dataset available to researchers in the blog and social media fields. We invite you to download the dataset, explore it, learn something interesting about it, and submit a paper about it to ICWSM 2009.

Good research topics might include...

• link analysis
• social network extraction
• tracing the evolution of news
• blog search and filtering
• psychological, socialogical, ethnographic, or personality-based studies
• analysis of influence among bloggers
• blog summarization and discource analysis

But you should feel free to explore any aspect of the data that you feel would be of interest to the ICWSM community.

Instructions for submitting papers to ICWSM may be found here. When submitting your paper, indicate that it makes use of the dataset. Dataset papers will be reviewed for the main conference, and additionally for presentation at the data challenge workshop to take place on May 20th, 2009 (the last day of the conference). While we anticipate that several dataset papers may appear in the main conference, the data challenge workshop will provide an opportunity for in-depth discussion of the dataset in a more focused forum.

Hypertext 2009

via Pranam Kolari

HYPERTEXT 2009, will be held at Torino, Italy between June 29th and July 1st next year. Perhaps, the evolution of HYPERTEXT conference reflects the increasing scope and influence of the Web over the past decade.

The Web, the Semantic Web, the Web 2.0, and Social Networks are all manifestations of the success of the link. The Hypertext Conference provides the forum for all research concerning links: their semantics, their presentation, the applications they have been put to, the knowledge that can be derived from their analysis, and their effect on society.

Papers due Feb 2, 2009. Event has been added to the Social Media Events Calendar.

Ph.D. Dissertation Defended!

Yep, its true! I just defended my Ph.D. dissertation yesterday! w00t! w00t! The defense presentation went quite well and I had all the paper work turned in today. A few minor updates to my document and I should be done. I was back in the lab today and attended the CSEE colloquium talks and then the Hi Tea. Doesn't feel like I am no longer a grad student. But it is time for me to move on.

I am real excited about my new position: I will be joining Microsoft Live Labs and moving to Seattle next month!! w00t! Live Labs is a wonderful place and I am absolutely thrilled at the opportunity to work with so many smart folks there. When I interviewed at Live Labs I just knew for sure that this is where I wanted to go. It is a fun place and a perfect match especially for the type of stuff I am passionate about.    

Everyone has been asking me about how I feel now that I am done? It hasn't sunk in yet, but all I can say is that I just feel glad to have finally completed my Ph.D.  I feel honored that I am Prof. Finin's protege. There is so much I have learned from my advisor and many qualities that I still aspire for. Prof. Joshi has been equally instrumental in guiding my research and ensuring my progress, for which I feel grateful. There are many colleagues at UMBC who have enriched my skills and I feel my school (and definitely the lab!) has been a second home. A special shout out to my good friend and academic sibling Pranam Dr. Kolari. Also, through various internships during my academic years, I have been fortunate to have collaborated with many brilliant researchers who have been inspirational to me.  I think I was also lucky to find myself doing research at just the right point of time in Web's history where things are changing so quickly and becoming richer and more social. This has been incredibly exciting for my research. Most of all I feel thankful to my parents, my family, friends and so many people who totally believed in me. Thank you all for always being there for me.

On Monday, I will be heading home to India for a few days to spend some time with my family. Blogging might be intermittent for a while. :-)

Live Labs Social Streams

Very cool! Live Labs releases the Political Streams project. It is a lense into the people and places that are receiving a lot of attention on blogs and news.

You can read the post snippet and see neat graphs of how the attention changes over time:

Also browse through the related names and places as you explore different topics and blog posts/news stories. Furthermore there are profiles of people and places in news that are generated using Freebase.

 

This is really cool! If you looked at search trends, many of the search terms are in fact about places and people in News. Users go to a search engine to find out both why they are in news? and often who the person is? Social streams (right now, for political streams) let you explore this in a real time, interactive manner. 

Nice!

Mean Time to Update Feeds

tIn the State of the Blogosphere report posted by Technorati recently, they describe how the top bloggers update their blogs more frequently than the rest of the population. Given the data I have been gathering on the Bloglines subscriptions, I was curious to see what how most subscribed blogs behaved? For the top 100K feeds ranked by the number of subscribers, I fetched their RSS/ATOM files (some failed, some were filtered due to few posts etc). Using this handy PERL module, I parsed all the pubDates (sorry, RSS only for now) and converted them to their corresponding Unix timestamps. Now for each of these feeds, the Mean Time to Update (ie the avg of the number of days between consequent posts) can be obtained. Interestingly, some fraction of the posts were updated after they had been published (hence were out-of-sequence in the RSS feed). For this analysis, I do not consider these updates -- it would be interesting to dig further into it.

Following graph F(x) is defined as the proportion of X values less than or equal to x. Thus you can observe that for the top 10K feeds 40% of the feeds updated in a day or less. For the rest of the feeds it was 30%. I believe as we go lower in the ranking, this would keep decreasing even further. Interestingly, overall, for the highly subscribed feeds, almost 60% update at least once a week. 

For a feed reader keeping an index fresh and up-to-date is then a function of how many users subscribe to a feed, how often the feed is updated, when was the last time the feed was requested (by any user in the system) and perhaps even when the last ping was registered for a particular feed in the ping stream. I believe in Google reader's case, clicking "show details" displays the last time when the feed was refreshed -- which I have never found to be over 3 hours for any feed that I subscribe to.

ACK: The above plot was made in MATLAB using this script for cdfplot.

Jon Kleinberg's Talk on Information Flow and Privacy in Social Networks

Yesterday, I had the opportunity to visit JHU to attend Dr. Jon Kleinberg's talk "Social process, Information Flow, Anonymized Network Data". Following are some running notes from this talk.

Dr. Kleinberg (aka the rebel king) talks about how the convergence of social and technological network now lets us observe information at a much larger scale. We have come a long way in social network analysis. From the initial days of 34 node karate club datasets to analysis of almost 240M users of Instant Messengers. But Dr. Kleinberg points out that it isn't just a matter of scale. Some phenomena that is not visible at smaller scales can become observable when you look at a large number of users. However, this also comes at a cost: often, we understand little about each node or link means. Compare the large scale studies to those of the karate club dataset where we know the roles of individuals in the two splits. 

Given the large, networked social data that we can observe, the question Dr. Kleinberg asks is "what can be said about the diffusion of information?". It has several implications like studies of epidemic propagation, information cascades, viral marketing, how rumors, opinions and fads diffuse in these networks. Recently, there has been a great deal of interest in these topics from both computer science and social sciences. One interesting and innovative approach by which Jon and his students study this is using chain letters -- those pesky emails that keep getting forwarded! And that is exactly what makes it possible to analyze how the information spreads.

D. Liben-Nowell, J. Kleinberg. Tracing Information Flow on a Global Scale Using Internet Chain-Letter Data. Proc. National Academy of Sciences, March 2008

They use two instances of widely circulated chain letter: petition against the Iraq war and a petition for increased funding for NPR. The neat trick they use is to mine the snippets from search engine results and crawl public mailing lists for these forwarded messages -- which gives them a clear picture of how the chain spreads in the network. I was really fascinated by this statement that Jon made:

Rather than thinking of it as people browsing through a network of documents, we should think of the chain letters as "documents blazing through a network of people".

Indeed, the analogy presented here was that of a document going through mutations as it was being forwarded by people. Some folks would append names to the list, some would delete them and some would change names to humorous variants like 'Donald Rumsfeld' or 'Karl Rove'. Dr. Kleinberg's slide containing the structure of this chain letter propagation was rather surprising. It consisted of long chains which is counter intuitive. One would normally have expected it to be a large number of short chains. Rather, it was like performing a depth first search traversal of the network.

There were two models suggested by the authors: one was based on temporal ideas and the other spatial homophily in networks:

• Temporal model: chain letters are like DFS and the model is based on timing. The basic idea is that people do not act at the same time. Unlike several epidemic models (which are based on the assumption of synchronization), in this model a node v participates in the chain with some probability and forwards the letter to her neighbors as some function f(t) = t^c (where c is typically -1.5 in human networks).
• Spatial model: is based on homophily, the idea that people tend to associate with similar individuals in networks. This similarity may be spatial, geographical or demographical. The cool insight that this model offers is that long range links create awareness but for participation it is the dense local structures that really motivate people to act.

The second part of this talk focused on privacy and anonymization in social networks. This is an important research problem. As computer scientists, we are always looking for more data but at the same time we have to take the privacy of a user very seriously. From personal experience, after the AOL search query log debacle, it has become almost impossible to get companies to release their datasets -- and for pretty good reasons. Given this deadlock, as researchers it becomes even more important for us to understand the type of attacks and what we can do to prevent these and ensure anonymity of the users when a dataset is released. Dr. Kleinberg points us to a few recent research papers in this direction:

• On anonymizing query logs via token-based hashing Ravi Kumar, Jasmine Novak, Bo Pang, Andrew Tomkins
• A. Narayanan, V. Shmatikov. How to Break Anonymity of the Netflix Prize Dataset. Oakland 2008. (online)

The basic attack strategy is that you can easily add yourself to the system. For example if you were to know that the email network would be released, you would signup for a new account and then add a few edges (by sending an email to known set of individuals). This way you can identify yourself in the 'anonymized' network by looking for these signatures or subgraph patters.

One would expect this problem to be NP-Hard and for very large graphs almost intractable, but in the following paper Dr. Kleinberg shows that under certain conditions this can be solved easily, thus breaching the network's privacy.

L. Backstrom, C. Dwork, J. Kleinberg. Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography. Proc. 16th Intl. World Wide Web Conference, 2007.

The basic intuition was to build a subgraph and attach it to the network. If the subgraph is small and randomly generated, it will be likely that it is unique and thus efficiently findable. Furthermore, they show that in real networks, these types of subgraphs are often quite readily available in the form of small networks or cliques. The paper presents interesting bounds on the problem and proves that you indeed need a subgraph H of the size sqrt(log n) which is reasonably small.

I thoroughly enjoyed the talk, especially since the last conference I attended, Dr. Kleinberg's talk was jam packed and I was hardly able to enter the room. It is always inspiring to hear such speakers and learn from their talks.

This post is from my running notes during the talk and hopefully benefits folks who were unable to attend.

Obama vs. McCain

Frankly, as an outsider who can not vote, my opinion would not matter much but I am hoping Barack Obama wins!

This election calls for Americans to make a very important decision, one that impacts not just their lives but the World economy and political stability. This election is also important and interesting event from a different standpoint. Never before have there been so many people using the Web. Social Media tools, blogs, twitter, facebook and virtually every imaginable resource online is now a platform for political activism. In the coming months and years, researchers will be collecting, scrutinizing and analyzing these datasets (some folks already are!)d.

A more immediate feel for the current political pulse of the population can be obtained by looking at search results and online electronic prediction markets -- both of which seem to indicate a tight race with a slight lead by Obama in the recent few days. GOBAMA! check out the Google insight graphs below:

 

Note: The bump on 27th Sept due to the Presidential debate and 3rd Oct after the VP debate.

Social Network Visualization Eyecandy

Visualization of graph data is incredibly challenging, particularly when it comes to extremely large, scale-free graphs and social networks. A few simple searches on the Web and you will find some mesmerizing and very cool images. Perhaps the most cited and fascinating of these are Matt's "Mapping the Blogosphere" series.

Some of my favorite tools for graph visualization are LGL (see Twitter visualization using LGL), Prefuse, Guess and UCINet (no specific ranked order). I have also been waiting to get a chance to try out AT&T's large network visualization software. Two other promising tools that I have not played with yet are Tulip (TOTH Eytan!) and yWorks.  Following is an image I had generated a year ago (more here) using UCINet. This is a community of political blogs discovered using an implementation of Dr. Mark Newman's community detection algorithm:

One thing that I think would be exciting is visualizing evolving networks. Current tools force you to map blogs, social networks etc. into static graphs. This is a serious limitation. Their real meaning and value comes from seeing the temporal interactions between members, evolution of community structures,  changes in strength of ties over a period and detecting bursts. As far as my knowledge goes, this feature is not supported by any of the open source/freely available tools that I have explored. I would love to hear about other graph visualization tools that I may be unaware of.

When Did You Last Update Your Blog?

Think of all the blogs you have started till date. How many are still active? Yep! We have all been there: started a blog and never kept up with it, got busy with other interests or simply do not find enough time to keep up with blogging. 

I wanted to see what is the overall distribution of the date when a post was made. From a random sample of 50,000 feeds that were extracted from the Bloglines feed subscriptions, following is the distribution of the date when the post was made.

From the random sample of feeds downloaded (~42K successfully; approx between 10-15 posts per feed) , 66% of the posts were made in 2008. As for the rest, some even date back to 2001. This indicates that from a random sample, a sizable number of feeds have posts that have been updated within the last year.

This also comes back to my previous point about efficient feed crawling strategies. How do you dedicate resources for keeping a feed index fresh and ensure a good coverage/quality? It would be interesting to see how the following two metrics change as we move from most subscribed to least subscribed feeds:

1. Mean Time To Update Between Posts: Do highly subscribed feeds generally update more frequently?
2. lastBuildDate: When was the feed last (re)published?