Authors: Anh Dang, Abidalrahman Moh’d, Anatoliy Gruzd, Evangelos Milios, and Rosane Minghim
Abstract—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.
The proposed framework
The proposed visualization
Anh Dang, Abidalrahman Moh’d, Anatoliy Gruzd, Evangelos Milios and Rosane Minghim. A Visual Framework for Clustering Memes in Social Media. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (Industrial track), 2015.