Just wanted to share a quick pointer to the First International Workshop on Modeling, Mining and Managing Evolving Social Networks (M3SN) to be co-located with IEEE ICDE 2009 in Shanghai. Following is the CFP via Christian König
Contextual advertising relies on matching an advertisement with a page based on its content. Most often advertisers bid on keywords and the ad platform finds the appropriate pages on which these ads can be displayed by matching keywords and phrases with the content. There have been a number of situations where such an approach may fail. A few real world examples are discussed in the paper by Broder et al. [2]:
a page about a famous golfer named “John Maytag” might trigger an ad for “Maytag dishwashers” since Maytag is a popular brand. Another example could be a page describing the Chevy Tahoe truck (a popular vehicle in US) triggering an ad about “Lake Tahoe vacations”. Polysemy is not the only culprit: there is a (maybe apocryphal) story about a lurid news item about a headless body found in a suitcase triggering an ad for Samsonite luggage! In all these examples the mismatch arises from the fact that the ads are not appropriate for the context.
These examples highlight the need for moving from a contextual to conceptual/semantic advertising models. The paper by Broder suggest mapping the pages as well as ads into a common ontology/taxonomy and thereby finding the appropriate higher level concept (Like politics/sports etc) that relates the two. There are very few papers on advertising models since this is such a closely guarded secret and a search companies' substantial revenue is tied to their ad platform's performance.
I believe that advertising is in it's infancy and more interesting approaches would soon replace the current state of the art. One problem is that due to the lack of datasets it becomes quite difficult to be in academia and make a significant contribution towards this area.
The next avenue for advertising seems to be Social advertising. While Facebook has its own approach to social advertising. I think the general idea of Social Advertising is to utilize not just the context of the page but also the social information to better place the advertisement. One question here is whether the ad placement is done to target the user or his/her audience. These might require slightly different models. For example, if my friends are all clikcing on the iphone ad on a social network, the platform might decide to also target ME personally for the marketing the iPhone. On the other hand if it can identify that a lot of users come to visit my profile due to the social media posts I write -- then perhaps the advertising could target them instead.
One potential market for advertising that I think is completely untapped is the referral. Companies are ready to pay huge sums of money to get new clients. Often cell phone companies, stock trading sites and banks launch promotions where they pay upwards of $50 for referring a friend. But when was the last time you actually did that? I think that is the best way to alienate your friends -- by hoarding corporate America's products and services or spamming their inboxes with unwanted referrals. But still, this is a huge market and worth billions -- if we can crack it! One approach I am thinking of is to build a referral platform (perhaps there are some out there -- I just dont know?) -- one which would benefit publishers and advertisers alike. I as a publisher have a (sort of) general sense of what my audience would like. I can for example even decide that I might be willing to share the $50 I receive from the advertiser and pass on the benefit to my readers (since my payoff is in having the readers come to my blog!) -- thus subsidizing that iPhone you wanted to buy. In the current model, there is'nt much incentive for me to share (a few cents???) /pass on the benefit with the final consumer. But for higher valued products, my guess is that It might just as well work right. Moreover, the referral platform manages the entire process thereby making it easier on the advertiser to launch new schemes and manage their inventory of referral programs.
I had intended to write a brief note on some of the recent papers [1-4] on this topic but turned out sharing my thoughts on the advertising instead -- which is perhaps more fun anyway :-).
[1] http://www.cs.cmu.edu/~deepay/
[2] http://portal.acm.org/
[3] www.csulb.edu/web/journals/
[4] http://www2008.org/papers/
Many folks are really excited about FriendFeed. Personally, I have found that there are a lot more comments when something gets posted on FriendFeed. Recently Yuval Atzmon's User21 blog released a list of most followed users on FriendFeed. Since I too had a crawl of FriendFeed running in much the same way as Yuval, I decided to look at the complementary question: "How many users do people follow on FriendFeed"? While the crawl is not yet complete (and complete statistics would have to wait), the numbers are really striking! Some users follow more than a 1000 "friends":
| sthayden | 3190 |
| scobleizer | 3087 |
| juliomedina | 2760 |
| thomashawk | 2557 |
| jasoncalacanis | 2447 |
| theillife | 2045 |
| mrsth | 1961 |
| pookakoo | 1814 |
| czarphanguye | 1736 |
| brynyoungblut | 1716 |
| eposter | 1562 |
| susangrisantiguitarist | 1550 |
I find this really amazing. Unlike Twitter, FriendFeed posts are accompanied with longer conversations so it can be more involved. I can barely keep up with all the information flying past me everywhere right now! I guess, 1500+ "friends" would be way too much for me!
Sociologists often talk about the Dunbar's Number which
is the supposed cognitive limit to the number of individuals with whom any one person can maintain stable social relationships.
In human contact network the Dunbar's number is said to be around 150. It might as well be the case that social tools and especially, microblogging is pushing this limit further. Studies on Twitter, Livejournal and other social networking sites seem to support this observation. I wonder then: what would be the Dunbar's number on social networks? 300? 500??? Any guesses? Perhaps some comparison across all the published papers that have studied different social networks might have some clues.
[BTW, I am akshayjava on FriendFeed]
I've been tinkering with Google's new Virtual World Lively. It seems a bit flaky at the moment and works only on Windows right now... but I was able to set it up. The neat thing about it is that the rooms can be embedded anywhere; like in this blog, for example. Like MyBloglog, Lively can also serve as a visitor log and unlike SecondLife, it isnt a walled garden. It is a fun thing to try out, but IMO, at its utility seems low. What it lacks right now is the stickiness factor...a few of the 2D Facebook app games are more addictive than this. Usually in such situations, an occasional flash mob like the one on Huddle is a lot more fun!
By far, the most prevalent data available in social media is tagging information. For example, in del.icio.us a user may tag a URL or in Flickr she may tag an image. One of the questions that comes up is how to then cluster social data that is rich in tags. Some techniques available ignore the user information and use only a bipartite graph consisting of tags and URLs. Another method is to represent two pieces of evidence (user-tag;tag-blog) in a tripartite graph (where nodes are of three different types: users, tags and urls). However,
even this type of structure actually
misses the higher order relation between the three nodes. Note that the information available is really in triples of the type <user, tag, url>. This information is not captured by the tripartite graph model. In particular, two users may be connected via a common tag even if the actual URL they bookmarked is vastly different.
There are some techniques using Tensor Matrix Factorization that can handle such data. However, the question of how to deal with triple (or higher) information from social data is quite interesting. Moreover, being able to do so efficiently and in an online fashion would also be important. I believe that this topic may be of significant interest in the upcoming social media and data mining conferences. The implications of these techniques would be in building better recommendation systems and personalization algorithms.
[Thanks Vlad Korolev for some of the discussions related to this post]
I have started a Google Calendar to keep track of events and conferences in Social Media. Some of you may have already subscribed to it. However there does not seem to be any way for me to tell exactly how many people are using it!
Feature Request #1: Show Number of Subscribers for a Calendar This would certainly be quite a useful feature to have and as it turns out I am not the first person to request for this.
Feature Request #2: Allow Tagging; Sharing Calendars a User has Added Calendar as a shared resource for planning and organizing events in a community is an important tool. However, the calendar is still not as social as it can be! You can easily find new calendars to add. But what about tags? How about sharing? I would love to be able to create a tag cloud of events and a public list of calendars I have added.
Feature Request #3: Social Event Notification If a user has made his or her events public, then why not show that users friends an update on the event she plans to attend? I think that Dopplr does this at some level but given that Google Calendar is a good place to consolidate all the events and schedules and GMail is our universal contact list -- why not combine it to make it more social?
Anyways, these are just a few quick thoughts I had about Google Calendar. It is a great tool and has made my life much easier. To be fair, I have never used outlook so perhaps my view may be a bit skewed.
The organizers of CIKM 2008 Workshop on Search in Social Media have extended their paper submission deadline from July 20, 2008 to July 27, 2008. For more information please visit the homepage of the workshop. Below is an excerpt from the CFP:
Social applications are the fastest growing segment of the web. Social media are a fascinating phenomenon because they establish new forums for content creation, allow people to connect to each other and share information, and permit novel applications at the intersection of people and information. However, whereas in the general web search is a critical application that drives usability, social media has been primarily popular for connecting people, not for finding information. While there has been progress on searching particular kinds of social media, such as blogs, search in others (facebook/myspace/flickr) are not as well understood. The purpose of this workshop is to focus the attention of the research community on this emerging topic, and to bring together information retrieval and social media researchers to consider the following questions: How should we search in social media? What are the needs of users, and models of those needs, specific to social media search? What models make the most sense? How does search interact with existing uses of social media? What works and what doesn't?
This workshop is chaired by
Note that the event has been updated in the Social Media Calendar as well. To keep track of social media research conferences and venues please subscribe via the calendar.
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I wanted to share a quick book recommendation. I picked up Sarah Lacy's recent book "Once You're Lucky. Twice You're Good." for a quick read during my flight. It is a fantastic account of Web 2.0 and profiles successful social media startups, Silicon Valley entrepreneurs, Venture capitalists and angel investors. The book takes you down a nostalgic road starting with the dotcom bubble all the way to the current buzz around social media and Web 2.0 (and why this is not a bubble!). It is an inspiring story and one that every entrepreneur (/wannabe/fanboy) would love to read. ;-) But I recommend it mostly due to the nuggets of wisdom in this book. If you are an entrepreneur, there is much to learn from the experiences and insights of those who have spent years building successful companies. Sarah does a great job at taking us through their voyage and writes in a unique and refreshing style of her own. Indeed, an enjoyable book to read! |
Via Marc Pickett I
Here is a cool script that Marc wrote to help improve productivity. It takes a screenshot every minute, so you can view a "timelapse" of your day (or week even). See how much time you spend on wikipedia, email, video games, and writing time management scripts vs. doing actual work.
The video is 6.8 hours crammed into 17 seconds. Marc says he was on the train from Cambridge, so he wasted less time on the internet than normal. So the apparent productivity was'nt perhaps cuz big bother was watching, eh?
But this is a really neat way to summarize how you spent your time during the day. Thanks Marc, for sharing the script and the video with us!
Great news! Our WebKDD 2008 submission titled "Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies" was accepted. I received a number of very helpful comments and suggestions from the reviewers and I am working on updating the paper. I will make the pre-print version available on the ebiquity site as soon as it is ready. In the mean time here is the abstract for this paper:
We present a simple technique for detecting communities by utilizing both the link structure and folksonomy (or tag) information that is readily available in most social media systems. A simple way to describe our approach is by defining a community as a set of nodes in a graph that link more frequently to within this set than outside it and they share similar tags. Our technique is based on the Normalized Cut (NCut) algorithm and can be easily and efficiently implemented. We validate our method by using a real network of blogs and tag information obtained from a social bookmarking site. We also verify our results on a citation network for which we have access to ground truth cluster information. Our method, Simultaneous Cut (SimCut), has the advantage that it can group related tags and cluster the nodes simultaneously.
Interestingly, I had discussed some of the basic idea behind this paper in my earlier blog post "Clustering Tags and Links Simultaneously."
