Secondly, I introduce and investigate new inference algorithms that combines metadata from both domains, inspired by current literature, which are hitherto absent in research. First, I introduce a new framework for the large-scale gathering and collation of Twitter user and message metadata. In this thesis, I investigate two aspects. 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. 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. My thesis combines metadata from both domains and transforms them into useful inferences for detecting hidden patterns. Rarely are metadata from both the user and message domains analyzed in tandem with each other. 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.
Current research focuses only on a single domain, but rarely on both. Most extant literature treats the message and user domains on Twitter independently of one another. 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 serious consequences (riots, coordination during natural disasters, response to terrorism, and political dissent). Real-life happenings are constantly reported on Twitter; thus, it functions as a 'mirror' to the real world. A good example is Twitter, which is a rich source of readily-available information by, and about, people. Social media - in particular microblogging - is fast becoming important in today's world.
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