Tracking Influence and Opinions in Social Media

These days Social Media such as forums, wikis and blogs in par ticular are playing a notable role in influencing the buying patterns of consumers . Often a buyer looks for opinions, user experiences and reviews on such sources befo re purchasing a product. Detecting influential nodes and opinion leaders and underst anding their role in how people perceive and adopt a product or service provides a pow erful tool for marketing, advertising and business intelligence. This requires new a lgorithms that build on social network analysis, community detection and opinion extract ion. Studies on influence in social networks and collaboration gr aphs have considered a static view of the network and are based purely on link analys is. These techniques have been found to be effective in performing analysis at an aggre gate level and to identify key individuals who play an important role in propagating in formation. However, influence on the Web is often a function of topic. A blog like Dail y Kos that is influential in politics is less likely to have an impact on the technology related blogs. Similarly, Techcrunch, an extremely popular technology blog might not be influential when it comes to politics. We propose the notion of ‘topical influenc ’ and extend existing techniques to make them topic sensitive. An important component in understanding influence is to dete ct s ntiment and opinions. Changes in opinions, aggregated over many users, can be a predictor for an interesting trend in a community. Sufficient adoption of t his rend could lead to a ’tipping point’ and consequently influencing the rest of the community. Strong opinions over time indicate biases. It is important to model bias to target the right audience for marketing and advertising. A community of ipod fanatics for example, needs little or no convincing about the product. Influencing an opinion le ader in such already positively biased communities is going to have less significant i mpact for the product. Here we also describe BlogVox, a blog analytics system that we dev loped for TREC opinion retrieval task. This system finds opinionated blog posts about a topic. We propose to extend this system to track aggregated opinions over many users. Finally, we propose to model influence as a temporal phenomen n. The Blogosphere, being a buzzy and dynamic environment, has new top ics emerging constantly and blogs rising and falling in popularity. Tracking these c hanges over time allows us to find blogs that are influential versus something that is jus t briefly popular. For example, many thousands of sites linking to a ‘Coke Mentos’ vid eo in one day indicates popularity. But thousands of links accumulated consistent ly over time by a blog indicate that it is influential. Additionally, by studying how a meme spreads, we can identify key individuals that are either buzz generators or early adopters who create sufficient interest on the topic. In this thesis we develop an improved influence model that inc ludes components for community structure, topic categorization, representati on of key beliefs and opinions, and a temporal analysis of how these change. We show that his m odel outperforms existing, simpler models by evaluating its precision and re call for appropriate tasks.