Social Media Research Blog

Wordle: Create Beautiful Tag Clouds

Briefly; via Harry Chen:

Worldle is a neat tool to create pretty looking tag clouds. Enter text or your del.icio.us username.

Here is my tag cloud from del.icio.us

looks awesome, no?
Thanks Harry!

Its Getting Crowded Out Here!

Yep! Thats what my Firefox toolbar looks like these days. Bookmarklets are a convenient way to post stuff to various sites. I use it for sharing links with the ebiquity blog via del.icio.us, post something to friendfeed, check news every now and then and right now I am testing this bookmarking site called Gyzork.  A few other plugins I have installed are del.icio.us, socialbrowse and stumbleupon. I used to have a lot more but couldnt keep up with all the stuff going on in my browser on a single monitor! :-(

Perhaps its time to rethink how we use bookmarklets. Some thoughts on this:

• It would be great to have icons for bookmarklets
• I need a bookmarklet 'stack feature' -- a place where I can organize bookmarklets.
• A listing of all the bookmarklets for different categories (GTD, News, Links, Blogs, etc...) would be handy for power users.
• Maybe we even need a simple way to post a link on one place and have it pushed to other sites

Its getting crowded out here! I am wondering if there is a better way to manage this somehow?

[Thanks Anthony Vito, for some of the discussions that led to this post]

Evri: Search Less, Understand More

I just received the beta invite to Evri.com (Yaaayy!). It is a really cool site that aims to help people find information. Right now they just have a browse interface. You can see what are the top concepts and named entities (primarily from News sources) and navigate through semantically related terms. The main idea behind their approach is that you can construct the graph of all the concepts and entities by analyzing the text. Here is an example of the top names in the news. Clicking the terms (from the graph) "Barack Obama" and "Ralph Nader" for example, would pull up all the stories related to recent controversies.

One can browse through the graph or the popular terms. I checked out what they found on Obama. Here is a snapshot on the left. I think that a really neat trick that Evri is using is the idea that working on sentence level semantics can provide sufficient meaning to help organize information. Constructing a complete parse tree that is both syntactically and semantically accurate is a difficult problem. There are many vagaries of natural language text that make this challenging. Evri, at least for now, bypasses some of these problems by organizing information around simple questions like "what is Obama doing?" which can have easy to identify clues directly accessible from the text (critisizing, leading, denying, facing....). Similarly for other entities like organizations one can ask "What is happening with Yahoo?" (bidding, reject, acquire, etc.). 

This is a fascinating approach to organizing information and I think that Evri has a great potential. Lets think about it for a minute. One of my favorite passtime is to go to Wikipedia, pull up a random article and then browse through related articles. It is this serendipity and the feeling of chance discovery of something interesting that is so compelling about Evri.

Evri also reminded me about the way I had hoped to implement SemNews, a semantic search engine, that analyzed RSS snippets of News articles and processed it through OntoSem, an ontological semantics based Natural Langugage Processing system. Once the semantics/meaning representations were extracted, I would store the meanings in an OWL store so that RDQL queries could be performed to find relevant news items. I believe that the way we can accomplish Dr. Tim Berners-Lee's vision of Semantic Web is by advancing both information extraction (web scraping, entity annotation etc) and NLP techniques that would automatically annotate text and make it available in machine readable format.

Although, the founder claims that they are not a search engine, they surely join the group of NLP-based startups like Powerset and Hakia. Another powerful tool is Freebase which uses primarily Wikipedia as its source of information. Finally, it is also worth mentioning that Kosmix is yet another startup that aims to "Organize the Web so that you can explore, learn and discover".

The next obvious question that comes up is regarding the monetization and business model of these startups. Ofcourse, the story goes... the information is more focussed so ads would be more relevant... and no surprise that is indeed so TRUE. Just check out some of the advertisements on Evri. On the left, is a screenshot of an advertisement on Barack Obama's info page. But I think there is an opportunity beyond simply relevant advertising!

Many companies have huge websites with lots of information -- some organized and most not quite as much. If you wanted to ensure that your customers are able to get to the exact information they need -- Evri like approach can be ideal to help them browse through the various facets to get to what they really need. The applications to Enterprises and Enterprise search can be another monetization platform for Evri. 

Finally, IMHO, some hurdles that Evri faces could be dealing with noisy text, especially with Social Media. Many approaches that rely on linguistic or gramatical correctness of sentences simply fail miserably when dealing with social media content. The second problem might be esuring coverage of information. Right now, it seems to me like the News soruces Evri relies on are primarily US centric. As they aim to capture more audience outside US as well, they would have to concentrate on foreign languages, disambiguating named entities and location names. These are all interesting research problems and fun stuff to work on!

The Microblogging "Lucha libre"

OK, since the release of Google trends for websites I guess, this was a graph waiting to be posted ... and perhaps it already has been(?), somewhere. In any case, here is a graph of Microblogging "Lucha Libre" (free fight).

Interestingly, despite the frustrating downtimes Twitter has been facing lately, it still reigns supreme. Whats more, others are no where close! One thing to note is that for the last few weeks twitter's trend has been downwards. As much as I love Twitter, I am afraid that it may be losing its momentum, unless they turn around quickly. Personally, it seems like there are fewer conversations happening there right now.

What appeals me these days is FreindFeed. I find that there is a lot more to discover there than on Twitter. Plus the rooms feature is absolutely fantastic and the whole system is built around conversations. No downtime and a quick turn around time for development and new features makes the site exciting and fun. However, any social network is ofcourse only as attractive as the number of active friends you have on it. And despite its nifty toys, friendfeed still has a much smaller early adopter audience. Ditto with pownce and jaiku. Plurk seems like the new kid on the block.

The following graph shows the fight between all the other players, trying to dislodge Twitters dominating position.

FriendFeed looks like it is going strong! But, I guess only time will tell who will be the microblogging champion!

Searching for Math Documents on the Web

There are a number of vertical search engines and several specialized tools like Google Scholar that help academics search for scholarly works on the Web. While searching for some documents on an optimization problem, I was curious if there was a good way to search for Mathematical documents on the Web. Most search engines do not have any special means to search for equations or support for special mathematical symbols (like summation or exponent). Many a times one knows the exact term that they are looking for, however sometimes you might be more familiar with an equation or might try to find special terms in a document. I wonder how Mathematicians and Physicists find what they are looking for?

LaTeX is a widely used authoring tool in academia and I believe that support for LaTeX queries would be extremely beneficial for search. At the very least, it would be great if Google Scholar could support this. There are a few prototypes online, but none that I am aware of have the coverage or are free or currently functional.

I see many challenges in parsing and representing mathematical data and it seems like an interesting problem that researchers in information retrieval would have definitely looked into. I would appreciate if anyone familiar with this topic can share some good references. 

(Photo Courtessey darkmatter)

Some things are just Semi-Social

Social Media is a lot about sharing. Prior to the growth of social software, it wasn't that people did not share stuff -- they just did it offline or via email. Now we share at a massive scale and a lot more easily. 

Some things we are willing to share "openly"

• Music playlists (Last.fm)
• Books we read (iread, shelfari)
• Calendars and Travel plans (google calendar)
• Status updates (via Twitter and Microblogging)
• Restaurant recommendations (yelp)
• Knowledge and expertise (via Wikipedia)

As we start to experiment with social software we realize that sharing is good and soon become open to sharing a lot more. There are some things though, that just seem semi-social. What I mean by Semi-Social is roughly "Thing I would not mind sharing with a small group of trusted friends and family members".

Until just a few years back there would have been a lot more people squirming if they were asked to share such 'sensitive data' with others. I see this perception slowly eroding away. There is a small, albeit enthusiastic bunch experimenting with new tools that fall into the category of Semi-Social. 

Some cases that I can think of are as follows:

• Investment portfolio: One example is Covestor. I have an account there but it is under pseudonym. I would not be that enthusiastic to reveal my pathetic attempt to bet on the stock market by watching (mostly tech) blogs. sigh!
• TV watching habits: I think Television as we know it today is completely broken. There is no social aspect to it whatsoever. At ICWSM, Noor Ali-Hassan presented a paper on "Social Media Scenarios for Television". What struck me about this talk was her statement that "Despite its social nature, there is a private aspect of TV that people want to preserve".
• Income and financial information: This is something we had least anticipated. How did we get to a point where I am actually not that scared while putting all my bank details and credit card information into a site like Mint? Mint is not a social site as such. But it reflects how we are now willing to part with some really sensitive data. In contrast, there are other examples of recruitment sites like SimplyHired where people reveal their salary information and can search for companies by salary. A more recent startup that is quite similar is Glassdoor.
• Location: Location can be an extremely sensitive piece of information. Fortunately, Yahoo's fireeagle provides access control for various applications and one can set the privilege that each app has to access location information (latlong, zip, state, country etc).

There will always be some who are at the extreme end of the spectrum and are quite comfortable with being completely (publicly) transparent about "sensitive data". However, most would still only dare to share some of this data with close friends and select people -- i.e. if there is enough value proposition in it for them. Some would be comfortable with aggregate analysis over the data as long as they are not personally identified or targeted in some way (advertising or otherwise).

Although it requires a great deal of courage (to work with privacy sensitive data), the opportunity to invent in the semi-social space may be quite a bit.

Email Interview: Nihaar Gupta, Youlicit

Nihaar Gupta, VP of Product development at Youlicit has kindly obliged to have an email interview with SocialMedia Research Blog. Following are the responses to some of the questions I had for him:

1) Please describe Youlicit to us?

Youlicit at its core is a discovery engine (http://blog.youlicit.com/?p=23). We want to connect you to the most relevant and recommended information as effortlessly as possible. As of now, we are building a technology that allows a user to find the most recommended sites (recommended by people around the web) related to a given URL. We believe that people are the best judges of content and more often than not, the information you are looking for has been found by someone before. Our goal is to aggregate that information and allow the user to access it with the click of a button.

2) Tell Us about your background, the team behind Youlicit and how started it?

Youlicit came about as a result of trying to solve our own frustrations with trying to find information on the web. With the enormous amount of user-generated content and annotations on the web, we saw a huge amount of valuable data that was inaccessible and fragmented. For the sake of brevity, the team bios & background are here http://blog.youlicit.com/?page_id=6

3) Please give us a brief overview of the technology behind Youlicit?

Youlicit aggregates user annotations of websites and other user generated content and analyzes it to create a URL-URL mapping of websites based on relevance and quality. Using this mapping we are able to deliver related and recommended sites to a user with a click of a button.

4) While using Youlicit plugin, I felt that one of the challenges is the coverage -- how do you plan to address this and build your current index?

We are constantly working on improving our coverage. There are two metrics we strive to maximize for our results, quality and relevance. In regards to quality, we’re always looking to increase our database of “quality sites” by tapping into the various kinds of user annotations (denoting quality content) that exist on the web (bookmarks, tags, votes, comments). In regards to relevance, we’re always researching novel ways to extrapolate connections between websites and map URL’s back to our database of “quality sites”.

5) How do you ranking the 'Enhanced Links' in the plugin? Do you also take into account how many users actually click through the suggested links?

Each result in the Youlicit More widget (and on Youlicit’s site) has a score based on the metrics above, quality of the site and relevance to the item being queried. We are looking into ways of scoring the results from implicit/explicit feedback that we get from users (clicks, recommends).

6) How do you ensure that the Enhanced links feature is non-intrusive?

The current version has manifested itself after a few weeks of alpha testing with a handful of bloggers. That said, we are still looking for feedback on the user interface and would love to hear opinions on how to make it more useful and less intrusive for bloggers/blog readers.

7) How would you compare the plugin to sphere's related blog posts?

While Sphere focuses related & recent blogosphere content, we, at Youlicit, are trying to provide the blog reader with more seminal information related to the blogger’s topic of conversation. For instance, if you are reading a blog entry on global warming, you are more likely to receive the most recommended articles (blogs, sites, essays) on Global warming from around the web rather than  recent blog entries on that topic.

8) What are the other features on Youlicit?

Youlicit’s primary product is a Firefox extension to access that allows a user to access our results during his/her browsing experience. We are in the process of redesigning our website and streamlining the current offering to focus on this button. Down the road we would like to be able to deliver personalized recommendations for users as well as connect users to people based on transient and long-terms interests (ideally using a person’s interests to enhance his/her social graph).

9) Would the plugin be adverting supported?

We do see advertising as a very possible source of monetization. Given the fact that we are providing contextually relevant information, the search model of advertising applies nicely. We are also exploring other possible means of monetization but as of right now the priority is to build something that people find useful.

10) What are the next things to look out for at Youlicit?

As I mentioned above, we are stripping down Youlicit to bring the focus back to its core; the Youlicit More functionality via the Firefox extension and blog widget. We expect to release a new designed website very soon. And as always, we love to hear feedback on what you think so far and how we can improve.

Youlicit: Search Less and Find More!

Youlicit is a new tool that helps you "Search less and find more". Often we forget that search is only one means to find what we are looking for. Even search by itself is not the endpoint of an information need or a query. This tool reminds me of the "berry picking model" of Information Retrieval that I had read about first in my IR Class. The model basically says that:

Information need is not satisfied by a single set of documents but by bits and pieces found along the way.

The paper  titled "The Design of Browsing and Berrypciking Technique for Online Search Interface" describes a searcher as

Moving through many actions towards a general goal of satisfactory completion of research related to an information need.

What Youlicit does is provide this ability implicitly, without the reader (or more generally a searcher) having to go through the trouble of navigating and mentally processing through hyperlinks or firing search queries to find related content. Youlicit takes care of all that on your behalf. By providing a simple plugin, the Youlicit widget automatically highlights some of the related, relevant links and provides useful suggestions -- all without your audience ever leaving your blog. I love the idea and the neat implementation that these guys have built. (The very same need was what lead me to hack this Wikipedia related widget a few weeks earlier.)

On the Youlicit site, you have lots more interesting tools. You can discover new content that is relevant to your interests, find related users and share links with them or follow their interests. Youlicit is paving the way for social browsing tools and is a neat concept that is well implemented. Their index does not seem to be very large at the moment and I feel that it would get better as they start to seriously scale up. In the interim, I feel that there might be stopgap solutions that they could be employ -- for example the Alexa related URLs for the links that are not currently in Youlicit's index.

In relation to this plugin, one tool that is similar is the  Sphere plugin that shows related blog posts. I feel that sphere serves a complementary need. From what I understand Youlicit aims to find the interesting blogs and Web URLs one might want to look into in relation to a given hyperlink.

Another plugin is the Snap plugin -- which shows a screenshot of the outlink. However, in my opinion snap does not really serve much purpose and is a bad tool from a usability perspective.

Youlicit is non-intrusive and you are gonna enjoy the serendipity of finding interesting new links! Give it a spin!

These Tweets are from Mars

I am really enjoying the tweets from @MarsPhoenix. Ofcourse this isn't the actual robot sending Twitter updates from millions of miles away, the researchers tweeting on it's behalf are definitely engaging in some interesting conversations. This is one fantastic example of how large organizations can engage in Social Media.

The thought that we are having a conversation with a tiny bot makes the whole experience rather exciting. It wouldn't have been half as much fun if it were for a human persona at the other end. This "bot persona" is more lovable, in part due to our collective imagination and desire of being able to have an intelligent conversation with machines -- our R2-D2s and WALL*Es.

This has been a fantastic experiment in social psychology as well as a superb publicity approach.  MarsPhoenix has about 20K followers  making it one of the most popular Twitter users. Accolades to JPL researcher Veronica McGregor, for this terrific idea and posting interesting updates.

I think that there is a lot more to this story. I imagine that soon we will have more devices that we can talk to directly on Twitter and IM. One idea I had recently was to rig up our lab's coffee machine, Mr. Capresso, with a temperature sensor so that he can automatically inform us when fresh coffee is brewed in the lab.

And at the cost of sounding much like Eliza, I think that for a limited domain, we might even have the capabilities to build Natural Language Generation tools that could automatically post Tweets. I am aware that there are many bots on Twitter. But the tools I would like to see are the ones that can do more than just post a message (like a new video on qik, etc) -- true interaction would come only from conversations. A really wacky (but simple) example would be a poetic bot (yep! people have researched on that too! ;-) )that would send intelligible rhymes in response to @ messages. Might be quite hilarious to follow it!!

The Echo Chamber Model

The blogosphere if often described as an echo chamber. Ideas, comments, controversies and discussions are all reverberate in this online space. In the paper titled "Information Diffusion in the Blogspace", Daniel Gruhl et al. studied the dynamics of topics propagate in the Blogosphere. They say that resonance is a rare phenomenon that can be described as

fascinating phenomenon in which a massive response in the community is triggered
by a minute event in the real world.

While few topics reach the state where they "resonate" through out the community/blogosphere, the echo chamber is still active with millions of posts and discussions in the form of trackbacks, comments and twitter posts, etc. The blogosphere is a huge graph and the echo chamber phenomena can be easily modeled in terms of random walks in this graph. Imagine that your post is read by your immediate neighbors and their posts (in reply to yours) is read by their neighbors and so on. Now what is the probability that this sequence would terminate with a post or a comment back on your blog? This is nothing but the commute time or the round trip time for the random walk to start on a given node (say A), traverse to a node (say B) and return back to the node A.

Commute time embedding is a way to map the original graph (which lies in a high dimensional space) into a two (or any low) dimensional space in such a way that the euclidean distance in the low dimensional embedding preserves (i.e proportional to) the commute times in the original graph. Interestingly, this can be quite useful in describing how ideas or information might flow in the network. For more details about the exact method to compute the embedding please refer to the paper. It is a neat technique that can be described in terms of the eigenvectors of the graph Laplacian (and can also be related to heat diffusion in the graph).

Consider the classic Karate club example. The original graph and the corresponding embedding in a two dimensional space is shown below. Notice how the nodes 33 and 34 are very close to each other. Compare this to node 3 which is almost equidistant to the central nodes (33/34 and 1) of both the communities.

I think that this might be an interesting way to model the echo chamber of the blogosphere. In this representation, blogs that are closer to each other in Euclidean space are also closer in terms of social distance (another term used in the literature that approximately means the same thing as commute time).