Omar Alonso
ABSTRACT
Over the past couple of years, social networks such as Twitter and Facebook have become the primary source for consuming information on the Internet. One of the main differentiators of this content from traditional information sources available on the Web is the fact that these social networks surface individuals' perspectives. When social media users post and share updates with friends and followers, some of those short fragments of text contain a link and a personal comment about the web page, image or video. We are interested in mining the text around those links for a better understanding of what people are saying about the object they are referring to. Capturing the salient keywords from the crowd is rich metadata that we can use to augment a web page. This metadata can be used for many applications like ranking signals, query augmentation, indexing, and for organizing and categorizing content. In this paper, we present a technique called social signatures that given a link to a web page, pulls the most important keywords from the social chatter around it. That is, a high level representation of the web page from a social media perspective. Our findings indicate that the content of social signatures differs compared to those from a web page and therefore provides new insights. This difference is more prominent as the number of link shares increase. To showcase our work, we present the results of processing a dataset that contains around 1 Billion unique URLs shared in Twitter and Facebook over a two month period. We also provide data points that shed some light on the dynamics of content sharing in social media.
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Index Terms
- The World Conversation: Web Page Metadata Generation From Social Sources
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