Utilizing contextual information from Tweets as parameters for community detection input graphs - Lam, Alron Jan (Animo Repository, 2017)
Twitter, as a microblogging platform, has become an avenue for people to voice out their opinions online. However, to effectively utilize this source of information, the massive amount of Tweets must first be processed to quickly obtain insights. One such way to achieve this is through community detection. Through this technique, Twitter users can be grouped into different types of communities such as those who interact a lot, or those who have similar sentiments about certain topics. However, most works do not utilize tweet content and simply use directly available information like Twitter follows. Hence, this work explores the utilization of hashtags and sentiment analysis (taking into account conversational context) as parameters in the input graph for community detection. Though the modularity score does not indicate much effect, an evaluation of topic model similarity of the communities tweets through word overlap and normalized pointwise mutual information show that differing contextual information and graph construction schemes can produce different insights. It is not necessary that one is better than the other, but rather, these are multiple approaches to getting insights for the end-users goals.