Keeping Track of Evolution of Trendy Topics in Social Media
DOI:
https://doi.org/10.26438/ijcse/v8i5.132138Keywords:
Social Context, Temporal Features, Incremental ClusteringAbstract
Keeping Track of Evolution of Trendy Topics in Social MediaSocial media platforms like Twitter facilitate interaction among people on topics of their interest which may vary with time. Hence identification of trendy topics from tweet streams should be a dynamic process. Topic identification with text clustering algorithms that focus on the content of the tweets do not suffice as tweets involves three types of features namely content, social context and temporal features. In this paper the author proposed a frame work that employs incremental clustering involving all the three types of features for clustering stream of tweets to produce set of clusters representing trendy topics at a series of time stamps. The proposed framework provides programmable selection / screening of interesting topics streaming on the Tweeter dynamically through proper parameter setting. Experimentation is done on real world data collected from Twitter on different domains.
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