Social Media Research Blog

Advertising Models: From Contextual to Conceptual/Semantic to Social

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/mywww/papers/www08-interaction.pdf
[2] http://portal.acm.org/citation.cfm?id=1277837
[3] www.csulb.edu/web/journals/jecr/issues/20081/Paper1.pdf
[4] http://www2008.org/papers/pp231.html

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!

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!!

My First FireEagle App: Pizza Coupons Search

Yesterday, I discussed an idea around the FireEagle geolocation API. I was envisioning an app where you could have a mobile phone and as you walk down the Mall or any location, it would pre-fetch relevant coupons and offers from the local restaurants. Being a grad student, we always learn to find good Pizza deals online. So I decided to use the FireEagle API to develop a Pizza  coupon finder. The way it works is that it authenticates with FireEagle to access your current location and then fetches the coupons from Google Maps and then parses the output to display on your mobile phone or a browser. You can try it at the following URL  http://wikimatix.com/coupon/pizza.php
if you have a FireEagle account already. First the application will try to authenticate with FireEagle and request the appropriate permission to access the exact or approximate location information and then passes this to the Google Coupon Finder.

Finally you have all the coupons you need to order your fresh pizza. The Documentation and example walkthrough code on FireEagle's developer area is excellent. It took hardly any time to put together this demo!

I think that the possibilities that this opens up for mobile advertising are exciting. We should also keep an eye on Android -- this space is gonna be fun to watch. [Update: Fixed the broken link. Sorry]

Yahoo FireEagle: Geolocation made simple

This service is currently in alpha but thanks to Pranam Kolari I was able to get an invitation to Yahoo!'s FireEagle platform. FireEagle is an easy way to manage and share location information across many applications. Currently, I publish my location information across many different sites and applications and it is rare that I put in the actual effort to update it everywhere. For example I use Dopplr to publish my travel plans, twitter and Brightkite to update my current location and Facebook to indicate my home address and other details. I was impressed with how easy it was (using OAuth) to allow Dopplr and others to share and access information with FireEagle. If you have a GPS enabled phone you can even update the geolocation on the go! Damn! Thats is neat!

One really compelling application is Wikinear.com -- it shows you the nearest places of interest by matching the location information obtained from FireEagle with Wikipedia entries. This is great especially if you are traveling to a new location or a tourist spot and would like to know the places of interest nearby.

Another very cool application is Metosphere. (PS: I wish I had an iPhone!). With this app, you can leave a digital message for a given location, see places and events of interest and even report Graffiti and City Repair! This gives me a reason to believe that the next big thing is going to be mobile advertising. The advantage of easy availability of geolocation information specific to a user is immense. This reminds me of a project at eBiquity research group a few years back, called Agents2go,  that talked about a very similar concept. Imagine that you were walking down the during lunch and the agent on your iPhone would automatically collect coupons or find deals at the nearest restaurants as you walk by. The idea that we can have a query free, geographically relevant search is really exciting. Yahoo! is innovating and pushing hard on the open initiative. With the availability of an API it would be fun to integrate Google Coupons! (OK here is one more fascinating idea and little time at hand!)

Location is a very sensitive piece of information and the best part of FireEagle is that you can manage permissions and privacy settings or even temporarily stop sharing your location. You can allow a specific application to only access location information at a certain granularity: exact, zip, neighborhood, state or even country.  More at Techcrunch.

Samsung Viral Video Based Advertising

Check this out! It is a really cool video -- a two minute clip of 10 really cool optical illusions.

It is apparently sponsored by Samsung to promote their new soul phone model. Perhaps, they are trying to suggest "look at the kind of cool things you can do if you could take videos with our phone". In any case it is a really neat idea and definitely getting a lot of attention on the Blogosphere. If this is the new approach to advertising then I think it is something we will see more of. User generated advertising trend is on the rise. Check out this other crazy/funky video that could just as well be a levis ad

One thing is for sure - the Samsung video did grab by eyeballs and it was entertaining enough that I watched it a couple of times! Whoever said ads had to be boring 15 second "hooks" (avg length/increments in which TV advertising is sold) ?

"Personal Brand" Monitoring Tools

Dr. Finin pointed to this interesting post on "branding yourself with a blog":

“… Certainly personal branding isn’t a new concept, but the future of personal branding could be in at your fingertips—with a blog. One of the first steps in creating a brand for yourself is to make your blog visible. Post meaningful entries, comment on your industry’s top blogs, or simply gain a regular readership. “Visibility creates opportunities,” says Schawbel, a social media specialist at EMC Corporation. He believes that when you brand yourself, the competition becomes irrelevant. “The goal of personal branding is to be recruited based on your brand, not applying for jobs,” Schawbel says. …”

Many brand monitoring startups are helping big companies keep track of what their (potential) customers have to say about them or their products. While the space of corporate brand monitoring is  fiercely competed, one area that is overlooked is that of "personal branding" tools. Most of us are highly interested in knowing what is said about us online. As the TechCareers blog points out:

“You are the chief marketing officer for the brand called you, but what others say about your brand is more impactful than what you say about yourself,” says Schawbel.

Keeping an eye on what others have to say about you is not always easy. I started thinking about these issues and outlined how I try to keep up with this information. Here is my "Personal Brand Monitoring Toolbox":

1. Search Engines: The typical way for me to keep tabs on this is by setting up Google alerts for my name, projects, organization (University/workplace) etc. In addition, I frequently perform "ego searches" to forage for mentions of my name.
2. Statistics and Tools: One very interesting tool that I have found useful is Lijit. It provides you stats on who is searching for you, what keywords were used to reach your blog, etc. In addition I use Google Analytics to know more information about my visitors, most visited pages and time they spent on my site. If you are an academic like me, you would like to know who has cited your papers recently (Google Scholar) and the number of downloads, who has linked to your paper (Google link: search) and/or your blog posts (Technorati searches). Yessss! I admit! I have become a total statoholic! :-)
   
3. Comments and Scraps: Twitter is another important tool in our arsenal for personal branding and your replies say something interesting about you. Finally, the comments on my blog, Facebook messages, scraps and photos are all part of my "brand" and I take interest in replying to them just like I would to an email.

As our information spaces diversify, monitoring "your brand" becomes a part of the everyday online activity. I dont think we have exactly cracked the nut yet -- keeping track of your profile and "your brand " is a highly addictive activity and I think that the tool(s) that make it fun and exciting will enjoy a great deal of popularity.

Leaveraging Web and Social Media for Recommendations

Both Amazon and Netflix's business models rely on effective recommendation systems. The recommendations provided by such systems are based on the purchasing habits of millions of customers. As such, these systems are non-trivial and have evolved out of years of research in both academia and industry.

In addition to mining millions of customer transaction records, for many products there is a vast amount of information available online. While I do not have a lot of familiarity with recommendation systems literature, it seems obvious that the Web and Social Media is a great source of information that could be useful when building such systems.  Bloggers' profile pages, wishlists, netflix queues, book lists and the blog posts themselves are potential clues to learn which two items may be related to each other.

As a simple example, consider the movie "Pulp Fiction", by querying Google for all the inlinks to the IMDB homepage of Sin City Pulp Fiction and counting which are the other movies that are "co-cited" here is a list of five movies that are most likely to be related to "Pulp Fiction":

Most of these look quite relevant. Some critics have claimed similarities between Pulp Fiction and Snatch. One surprise though was LOTR, I wouldn't have expected it to be grouped with Pulp Fiction, but I guess I like them both very much -- so it seems reasonable in my case atleast.

Just for fun, here is another example with "Sin City" another one of my favorite movies.

Unless you have a large index of the Blogosphere or the Web, it would be quite inefficient to mine for such correlations (by passing queries to search engines) on a large scale. I do not know how much of the search engine information is leveraged in recommendation systems built by Amazon or Netflix.  It might also be worth looking into differences in the recommendations produced on the basis of "how people co-cite two products" vs. "how people purchase two products".

Notes from DEBSM Workshop: Inferring Privacy Information via Social Relations

Inferring Privacy Information via Social Relations
Wanhong Xu, Xi Zhou Lei Li (presenter)

The presentation started with a neat quote: "Your social activities tell who you are". Social Networking are part of everyday life and for many of us a primary way in which we keep in touch with friends and family. Privacy is a huge problem in such networks. Li provided some statistics from a recent survey of British social network users: about  62 percent are concerned about the security of their personal data. 31% of the users falsify information to protect identity. This is a huge figure and definitely shows the level of concern.

This paper was motivated with applications for social advertising (which is a 2.2B US market). One can target users based on location, age gender etc. and social advertising allows advertisers to choose their audience. The problem comes up where users do not wish to disclose too much of their information online. The proposed solution is to automatically infer such missing information.

The authors use an undisclosed dataset for this study. The assumption is users fake personal information but not their activities. It is well known in offline-social networks that gender preference exist in friendship. however in online social networks this is not true (Jure Leskovec, WWW,2008). The key question that authors of this paper ask is : "Users may fake their personal information...But what about social activities?"

The insight that this paper offers is that certain group membership gives hints of user gender information. Joining groups has a gender preference. So the way in which one can infer the gender is to use a bipartite graph of users->groups with missing gender information using relation between users and groups. One approach is to use the User*Group matrix and build a classifier. However, they found that Naive Bayes does not work very well. Many social groups in fact dont have any gender preference. This can really hurt the classifier accuracy. So one approach they propose is to choose the discriminating social groups. i.e. groups that dominated by males or females. One major disadvantage that this technique suffers from is that the membership for users that dont join any of these groups can not be predicted. Once you restrict to the set of discriminating groups, now Naive Bayes performs well.

For users that cant be predicted they propose the use of an iterative algorithm to combine discriminative social groups and results from Naive Bayes classifier. Testing is performed by removing or making some data missing and then predicting the missing gender information.

My concern here is that the authors might have been able to avoid this disadvantage if they had used SVD to map the Groups to a set of lower dimensions that way it would automatically "cluster" the groups based on whatever the discriminating factor is (in their case the gender information). Secondly, while the authors had access to "verifiable" ground truth data, in real world how do we know the influence of fake profiles on these discriminative groups?

Cartoonist 2.0 and "Dancin Dubya"!

When returning a book at the library this week, I noticed an interesting talk titled "The Art of Political Satire". The speaker was none other than Kevin Kallaugher (KAL) who is the cartoonist with "The Economist "magazine, London. But more significantly, he is also the artist-in-residence and cartoonist at the Imaging Research Center at UMBC. Kal talks about his life experiences as a cartoonist both in the United States and U.K. His sense of humor is world-class and through a montage of his work over the years, Kal touches upon some delicate issues (911, war, world politics and the struggle to end of apartheid in SA) all while engaging his audience and making them smile.

I think Kal is what I would describe as a Cartoonist 2.0. He really gets the new media and realizes its potential. He talks about the decline in newspaper readership and the print advertising. How the new generation is more about flickr and YouTube and less about buying a newspaper or a magazine. So along with UMBC's IRC, Kal has set out on a journey to explore what it means like to be a cartoonist/journalist/political satirist in a Web 2.0 world. He is planning to take the essential elements of a cartoon strip and blend it with 3D animation and video. Starting from a clay caricature model of our very own Dubya and using motion capture technology, Kal can actually hold real-time conferences with the audience members asking Pres. Dubya Bush some serious questions! Well rest assured, you will atleast be get answers that make some sense! You can read more about it here and also check out the really cool video of "dubya press briefing"!  Oh! and here is Kal's fantastic version of "dancin dubya" ... quite close to the real version!
 

Well he is a real puppet arite!

Kal also plans to take dubya to live audiences and host a show soon. Soon we should expect to have syndicated, interactive 3D cartoon strips on many online newspapers! Good luck Kal, the maverick cartoonist 2.0!