A Visual Framework for Clustering Memes in Social Media

The spread of "rumours" in Online Social Networks (OSNs) has grown at an alarming rate. Consequently, there is an increasing need to improve understanding of the social and technological processes behind this trend. The first step in detecting rumours is to identify and extract memes, a unit of information that can be spread from person to person in OSNs. This paper proposes four similarity scores and two novel strategies to combine those similarity scores for detecting the spread of memes in OSNs, with the end goal of helping researchers as well as members of various OSNs to study the phenomenon. The two proposed strategies include: (1) automatically computing the similarity score weighting factors for four elements of a submission and (2) allowing users to engage in the clustering process and filter out outlier submissions, modify submission class labels, or assign different similarity score weight factors for various elements of a submission using a visualization prototype. To validate our approach, we collect submissions on Reddit about five controversial topics and demonstrate that the proposed strategies outperform the baseline.