You might have bought something on eBay and left a short feedback posting, summarizing your interaction with the seller, such as “Lightning fast delivery! Sloppy packaging, though.” Similarly, you might have visited Amazon and written a review for the latest digital camera that you bought, such as “The picture quality is fantastic, but the shutter speed lags badly.” While reading an online review, you may have also come across identity descriptive social information disclosed by reviewers about themselves such as their ‘Real name’, ‘Geographical location’, ‘Hobbies’, ‘Nick name’, etc. Or while searching for a used product in electronic second-hand markets such as those hosted by Amazon, you might have come across the description posted by the seller such as “Brand new device with original packaging! Factory authorized dealer! Full manufacturer’s warranty.”
What is the economic value of these comments? How can we monetize such content on the Internet? Increasingly these information exchanges are having some business impact that is being reflected in one or more economic variables (for example, product sales, pricing premiums, profits) that can be measured to examine the effect of a particular information exchange. The comment about “lightning fast delivery” can enhance a seller’s reputation and thus allow the seller to increase the price of the listed items by a few cents, without losing any sales. On the other hand the feedback about “sloppy packaging” can have the opposite effect on a seller’s pricing power. Similarly, characteristics of user-generated reviews and reviewers can affect ecommerce demand; feedback in blogs can affect firms pricing policies and the nature of competition; the attributes of user-generated search queries can affect the performance of search engine advertising, and the content of customer support dialogs can affect product design. Given the high volume of transactions that are completed on Internet based electronic markets, such user-generated content can have a substantial impact on firms’ profitability. Hence, it becomes important to examine various mechanisms for monetizing such content such as search engine and contextual advertising.
Our research studies the “economic value of user generated content” in such online settings as well as the means for monetizing such content, for example, through sponsored search advertising and prediction markets. This research program combines established techniques from economics and marketing with text mining algorithms from computer science as well as theories from social psychology to measure the economic value of each text snippet, understand how user generated content in these systems influence economic exchanges between various agents in electronic markets, and empirically estimates the performance of mechanisms that are being used to monetize such online content.
While there are many potential applications, our current research focuses on the following important and varied applications:
- Reputation Systems in Electronic Markets
- Online Reviews and Product Sales
- Financial News and Stock Prices
- Used Good Descriptions in Electronic Markets and Sales
- Political News and Prediction Markets
- Sponsored Search and Contextual Advertising
The EconoMining project is funded by NSF through the CAREER awards IIS-0643846 and IIS-0643847 and by Microsoft through a Live Labs Award and a Virtual Earth Award.