Social Media Research Blog

Twitter Election 2008

Check out Twitter Election 2008

It is a fantastic site that Twitter has put out! According to Twitter:

We’re filtering hundreds of Twitter updates per minute to create a new source for gathering public opinion about the presidential election and a new way for you to share your thoughts.

I love it! Unfortunately, there are so many updates that an RSS feed would be pretty much pointless. So I wish that there was some better way to provide an API to the election site with a  live stream so that data junkies like us could totally geek out and apps could be developed around the real time election twitter updates!

CFP: Workshop on Location and the Web (LocWeb 2009)

With the popularity of geolocation, mobile search, iphone/Andrioid apps, local advertising, location information is playing an important role in social media today. We have started sharing our location information on the web and via mobile devices through a number of services -- FireEagle, Brightkite, twitter, loopt, dopplr, Whrrl, geohash, citysense -- just to name a few. (phew!)

The "Second International Workshop on Location and the Web (LocWeb 2009)" to be held in conjunction with CHI 2009 (Boston, MA) looks like a very interesting venue to discuss ideas and research related to this topic.

From the CFP

Workshop Website: http://ifgi.uni-muenster.de/locweb2009/
Position Paper Submission Deadline: October 23, 2008

Overview

The goal of the Second International Workshop on Location and the Web
(LocWeb 2009) is to bring together researchers to discuss the ways in which
location as a first-class concept in Web services and Web applications will
transform the Web into a location-aware information system. We are
particularly interested in how location concepts can be shared across
applications to provide adequate support for an evolving ecosystem of
location-aware services and applications.

This event has been updated in the Social Media events calendar.

DAPD special issue on Data Management in Social Media

[Reposted from eBiquity Blog. Please contact me or Prof. Anupam Joshi for further details.]

We (Tim Finin, Anupam Joshi, and Akshay Java) are editing a special issue of Distributed and Parallel Databases on Data Management in Social Media. Manuscripts must be submitted by January 15, 2009 and should not exceed 25 pages in length. Authors will be notified by April 15 and camera ready copy will be due May 15. Submit papers online specifying article type S.I.: Data Management for the Social Web. For more information, see the call for papers (pdf) or contact Anupam Joshi at joshi@cs.umbc.edu.

Social Media tools like blogs, wikis and social networking sites are providing new opportunities for us to connect and interact with each other. Many social theories that could once be researched only by conducting expensive surveys can now be studied and modeled due to the easy availability of large scale social annotations and explicit description of social relationships online. The rate at which blogs, videos, bookmarks and many other user generated content is growing presents several interesting research and data management questions. The opportunity to mine social media content for analyzing opinions, sentiments and trend identification has several applications in Web search, personalization, business intelligence and national security. This special issue of the International Journal of Distributed and Parallel Databases invites original research contributions on data management in social media. Topics include but are not restricted to the following.

• community detection and evolution in social media
• recommendation systems
• search in social media
• event detection, trend identification and tracking in social media
• influence, trust and reputation in social media
• opinion/sentiment analysis, polarity identification
• feed distillation and ranking blogs
• mining microblogging and real time data
• folksonomy, tag semantics, clustering and usage
• advertising models for the social web
• indexing social media content, index freshness
• visualizing social network data
• spam detection, social network spam and profile spam

Cfp - Upload a Document to Scribd

Crowdsourcing Academic Paper Reviews?

The book crowdsourcing has given me inspirations for a number of ideas. There are many examples in this book of how the crowds are collaborating, participating and sometimes even competing to make things better. The part that I find most exciting is to do with citizen science and citizen journalism.

• Take the patent review process for example. Peer-to-patent is a great example of how the crowds can work together with experts to speed up the broken patent system.
• The InnoCentive project that uses the diversity of knowledge from crowds to find solutions to tough research problems.
• How Talking points memo asked its readers to help parse through pages of documentation in the DoJ scandals.
• Assignmentzero and current TV are changing the way we think about reporting and journalism.

These examples make me wonder if we might be able to do the same thing with the academic review process. Academic reviews can be time consuming and lets just say --- "have their own problems". Academic research is built on the idea of sharing knowledge, tools and insights. To ensure their quality, results are scrutinized by a peer-review process. Ultimately, if approved, it leads to a publication. In theory, everything should work the way it is supposed to. 

Like any system there are some practical problems. Conferences can adopt a double blind or a single blind review process. In either case, reviewer incentive and motivations vary drastically -- some are motivated by a sense of giving back to the community, others are just helping organize an important event in their research field, some might just like to see their name on the conference PC list, some like the privilege of reading interesting papers first, others like the privilege of having a say in how the field progresses, some (grad students) are volunteered by their professors -- to list just a few.
Reviewers are never "paid" and often have to find time outside their normal work commitments to fulfill these responsibilities, which may not be easy at times. Well I could write a whole post on this topic alone but that is for some other time....

Following are few ways in which I think crowdsourcing can help reform this system:

• Completely public: One approach is to make the review process completely public. Identity of the authors and the reviewers would be public and this could cause HUGE problems and for practical reasons this might be impossible to implement.
• Revealed Reviews: Once the papers are accepted, only those papers that made it and the corresponding reviews for these papers can be made public --without revealing the identity of the reviewer directly. Readers can comment on the reviewers' review and rate them. They could even write a review themselves if they like.
• Crowdsourced Reviews: The problem with revealing the reviews is the same as that of making the identity of the reviewer public. Subtle points mentioned in the reviews can betray the anonymity of the reviewer. The alternative, which I think is most practical, would be to conceal the reviews but allow readers to rate the paper and write their own reviews. Other users can read the reviews and rate the reviews themselves. The crowd is usually good at reaching a consensus in such systems. This would mean that for all accepted papers we have a pretty accurate judgment of what the community as a whole thinks of the paper. Now the scores of the reviewers can be matched against these ratings.The neat thing about this model is that not just academics, but also folks from industry and other walks of life can read academic papers and write reviews about them. And as the book Crowdsourcing mentions "diversity often trumps ability". More people reading and scrutinizing academic work is good for progress of research.

A point to note here is that compensating a reviewer's time in terms of money is not going to work.I do not believe that money is the motivation for reviewers (the same reason why it did not work with Google Q&A). Free conference registrations in lue of their time would be one way to go. Most often though registrations are paid for by the company or the grant. Finally, some conferences have started inviting PC members for a special lunch. Ofcourse, there is no such thing as a free lunch! :-)

Book Recommendation: Crowdsourcing

I have already raved about this book in my previous posts. Crowdsourcing, by Jeff Howe is a book that came out of a wired magazine article (just like Long Tail) and a blog by the author. I picked up this book almost on an impulse on my return trip from KDD conference in Vegas last month. At first, I thought it would be a lot of the same -- examples seen over and over again..Well, this book was far from that! The book is just fabulous and I really enjoyed reading every page. Jeff Howe does a great job presenting crowdsourcing in three parts: "how we got here", "where we are" and "where we are going". In essence, crowdsourcing is about the idea that "diversity trumps ability". Bring enough people together to take a crack at a really difficult problem and the diverse backgrounds and knowledge that the crowd brings to the table will inevitably outsmart the smartest people who, in some cases, may have been working on the problem for a long time. The author gives several examples from InnoCentive, where research problems are posed for the crowd to predictive markets (like IEM for politics and Marketocracy for stocks) which are much more effective than election polls and fund managers. To understand how some of these work you really should grab a copy of the book. Crowdsourcing is also about how we are now both producers and consumers of information. Prosuming news and media is part of our online activity and be it editing wikipedia, commenting on a blog, uploading or rating a video we are all participating in it (sometimes in surprising ways -- without even noticing that we are!). The book is full of several interesting examples from Threadless.com a crowdsourced tshirt design site to how a punk rock band that marketed itself on myspace. Counterintuitive the way large Newspapers have been thinking, those news sites that have adapted to this new trend have actually benefited and increased online readership. Crowdsourcing makes a big leap in how a billion people connect and someday might even help solve difficult, big problems like cancer and Lou gehrig's disease. This book is really about how crowdsourcing is changing human communication, content production, the way we share ideas thoughts and solve problems and ultimately our culture. Finally, as Jeff puts it... the spin on Robert Putnam's classic work is that we are no longer bowling alone, we are now just bowling online.

Crowdsourcing PC ads

Bill Gates, Seinfeld ads barely match up to any of the user-generated ads I have seen over at YouTube. IMHO, $300 Million would be better spent on crowdsourcing Microsoft's ad campaign. There are some really cool ads out there, here are a few:

Why would you pay $300 Million? Why bother if the whole Seinfeld ad is pulled off or not! -- when you can let your users champion your cause. 

You're No One If You're Not On Twitter

too cool! ROTFL! :-)

Bacon + Biscuits =?

If you were still wondering what this whole internet obsession with bacon was all about -- here is the explanation of the "Bacon Mania" over at Salon (via John Scalzi's blog):

"Blogger Sadie Fox, who also goes by the name Miss Cellania, wrote about the online bacon bonanza in a post for Mental Floss last summer. She dates the burgeoning phenomenon back to September 2006, when blogger John Scalzi momentarily captivated the blogosphere by taping bacon to his cat. If there is a better example of the sublime pointlessness of Internet memes, I cannot think of one. Oh, wait: Yes I can."

(rdfs:seeAlso Twitter, Flickr and on the Web -- almost a certain way to get comments!)

Well now its all about the biscuit. Over at friendfeed people are changing their names and posting nutty biscuitty comments. It is being described as a "Mini Meme", although I am not sure how this one started. Marketing and advertising companies would pay a ton for starting such memes. Apparently, by the end of 2007, Bacon sales were significantly up in UK -- hmm, wonder if it had anything to do with the meme? ;-) Well, that is how the cookie crumbles! oh wait -- the biscuit crumbles.

Care for a Bacon Biscuit anyone?

Oh well.... nevermind!

[Image credits: http://www.recipezaar.com]

Share the Love

What makes social media so compelling is the ability to share information with others. ShareThis is a cool widget I use on my blog to enable users to share the posts with others and to easily post it on different sites like digg, StubleUpon, FriendFeed, etc or via email. Recently ShareThis commissioned a study conducted by Forrester Research, that analyzed the sharing patterns across a number of users. Following is an excerpt from their blog post:

High level findings:

• Email is still primary channel for sharing -- 69 percent of adults cite email as the primary source of receiving information
• Less than one-third of online adults said they learned about the new content from generally shared content sources such as YouTube, a wiki, or social networking sites
• 64 percent of adults and 60 percent of youth still use the traditional cut and paste  method to share a URL or information
  • However this is not trackable by publishers, making sharing by email tough to measure
• Though the primary motivation for sharing differs, 81 percent of adults claim that, when cutting and pasting content to share, they share to help others - believing that a person will benefit from the information they share

Surprisingly, email is still the primary channel for sharing and what was more interesting is that people still do it the old fashioned cut-and-paste way. The neat thing about using a tool like ShareThis is that it gives you a "one-click" way to share something with your network via email, IM or on any social media site. I somehow think that email is not quite the right format for sharing links and interesting articles. I like email to be more about actionable items: but then that is just me. I think FriendFeed, Twitter or Facebook is more suited for sharing links and other cool stuff.

There are a number of interesting nuggets on sharing demographics and differences in sharing behavior based on gender. From the post:

• The motivation for sharing amongst men differs than those of women
  • In general, men are more likely to share something to appear cool or to look like an expert
  • Men more likely to share product recommendations and video than women; 77 percent of adult males and 74 of younger males shared news and web links
• Women are more likely to share products or ideas they like via easy or direct sharing methods
  • Women strongly favor send to a friend feature; more than 60 percent of adult woman use the send to a friend feature on websites
  • Women are more likely to share directly, especially via cell phone texting
  • Adult women tend to face more obstacles when trying to share content online, with 40 percent saying they could find no ways to share the information directly from the Web page

I read through parts of the report I had received from ShareThis -- lots of nice stats there. The full report can be requested from bizdev@sharethis.com (Unfortunately, I cannot post it directly here.)

Ranking Nodes via Random Walks

A few days ago, we were discussing a research problem in the lab. It reminded me of two papers I had recently read. The problem that we were talking about involved a graph where we knew certain nodes of interest and wanted to identify and rank other nodes that might be relevant. This is a classic example of a situation where random walks can be quite effective. Following are brief summaries of two papers on this topic:

The first paper titled "Ranking on Data Manifolds" is by Dr. Dengyong Zhou. It is discusses a neat interpretation of a random walk. Following excerpt from the paper describes the basic intuition:

"We firstly define a weighted network on the data and assign an authoritative score to every query. The query points act as source nodes that continually pump their authoritative scores to the remaining points via the weighted network, and the remaining points further spread the authoritative score they received to their neighbors. This spreading process is repeated until convergence, and the points are ranked according to the amounts of authoritative score they received."

Manifold is defined by Wikipedia as:

an abstract mathematical space in which every point has a neighborhood which resembles Euclidean space, but in which the global structure may be more complicated.

(rdfs:seeAlso Wolfram page on manifold)

So basically, one can think of a graph or a social network as data that lies on a manifold. In order to learn and capture the structure of the manifold a the Laplacian Matrix is used to represent the original graph. The advantage here is that the Laplacian is capable of capturing the long range relations between the nodes and ensures overall smoothness when propagating the scores on the manifold.

As the scores are iteratively spread from the nodes of interest to the neighbors (via the graph laplacian), the algorithm converges and corresponds to the random walks in the graph. The final scores provide the overall ranking of the nodes, with respect to the initial nodes of interest. The applications of this method range from image processing to web search and recommendation.

I really like this paper for its simplicity and clarity in explaining the connection between random walks and node ranking.

The second paper I would like to mention here is "Fast Random Walks with Restart and its Applications" by Hanghang Tong et al. It is a really nice paper that talks about how random walks can be used to identify how close two nodes are in a graph. Moreover, the paper discusses some really neat mathematical tricks that can be applied for efficient, "on the fly" computation of the random walks in large graphs.  The basic intuition is to use the Sherman-Morisson Lemma to avoid directly computing the inverse of a large graph (which is prohibitive). This is actually a superb trick to use, especially when doing rank one updates. A number of applications are discussed in this paper, including image retrieval and neighborhood formation etc.

There is a more recent work by Alekh Agarwal and Soumen Chakrabarti on this topic, "Learning Random Walks to Rank Nodes in Graphs". There are many more papers and I have probably not read them all -- these are just a few of my favorites on this subject.