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Information Diffusion in Twitter Communities
Twitter is a popular social network that provides rich data for research on viral e-marketing and politics. This has driven research on detection of communities, analysis the content of tweets and understanding of user behaviour. Most of this research has focused on static structure to detect communities and categorise user behaviour (i.e. number of following and followers). The aim of my dissertation is to understand how can we effectively propagate a piece of information in Twitter Communities? I answer this question through investigating the three major properties of information diffusion: speed, scale, range. I argue that volume of dynamic interactions, represented by retweets, mentions and hashtags, is an important factor to information propagation as it can identify user behaviour, community structure and topic of interests, which could boost speed and spread of information in Twitter communities.
My topic on information diffusion in Twitter communities will benefit many different fields such as online marketing, media companies and political campaigns. Online marketing could identify their right audience for their product or service. It can also help media companies propagate a fast buzz on their new movie or music for users who are interested in that movie or music. Political campaigns could learn about the influential users in the network to spread their message as far as possible.