Inferring social behavior and interaction on twitter by combining metadata about users & messages

Marc Cheong
Social media - in particular microblogging - is fast becoming important in today's world. A good example is Twitter, which is a rich source of readily-available information by, and about, people. Real-life happenings are constantly reported on Twitter; thus, it functions as a 'mirror' to the real world. These happenings range from the banal (individual thoughts, opinions, and observations), to the dramatic (celebrity announcements, scandals, and Internet memes), to real-world events with
more » ... consequences (riots, coordination during natural disasters, response to terrorism, and political dissent). Most extant literature treats the message and user domains on Twitter independently of one another. Current research focuses only on a single domain, but rarely on both. Research consists mostly of specialized techniques, such as opinion and sentiment mining, community detection, social network analysis, and trend mining which are merely applied to Twitter data. Rarely are metadata from both the user and message domains analyzed in tandem with each other. My thesis combines metadata from both domains and transforms them into useful inferences for detecting hidden patterns. The basis of my research is the use of metadata from both Twitter users and messages as the raw material, from which we can discover hidden patterns and inferences. Such patterns and inferences, in turn, can be combined with data mining techniques to unearth a wealth of knowledge about Twitter users in particular, and people in general. In this thesis, I investigate two aspects. First, I introduce a new framework for the large-scale gathering and collation of Twitter user and message metadata. Secondly, I introduce and investigate new inference algorithms that combines metadata from both domains, inspired by current literature, which are hitherto absent in research. In doing so, I contributed to the development of novel inference algorithms, and frameworks to harvest raw metadata from Twitter for the provision of ample data for the evaluation of my [...]
doi:10.4225/03/58b5009d3726a fatcat:x7xi2espbbd73jlg4s45hifclu