A Visual Framework for Clustering Memes in Social Media

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


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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.