research-article

A Visual Framework for Clustering Memes in Social Media

Authors:

Abidalrahman Moh'd,

Anatoliy Gruzd,

Evangelos Milios,

Rosane MinghimAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 713 - 720

https://doi.org/10.1145/2808797.2808830

Published: 25 August 2015 Publication History

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.

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Cited By

Smith ATacheva JHemsley J(2022)Visual Semantics of Memes: (Re)Interpreting Memetic Content and Form for Information StudiesProceedings of the Association for Information Science and Technology10.1002/pra2.73159:1(800-802)Online publication date: 14-Oct-2022
https://doi.org/10.1002/pra2.731
Valensise CSerra AGaleazzi AEtta GCinelli MQuattrociocchi W(2021)Entropy and complexity unveil the landscape of memes evolutionScientific Reports10.1038/s41598-021-99468-611:1Online publication date: 8-Oct-2021
https://doi.org/10.1038/s41598-021-99468-6
Safari RRahmani AAlizadeh S(2019)User behavior mining on social media: a systematic literature reviewMultimedia Tools and Applications10.1007/s11042-019-08046-6Online publication date: 17-Aug-2019
https://doi.org/10.1007/s11042-019-08046-6
Show More Cited By

A Visual Framework for Clustering Memes in Social Media
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 August 2015

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Research-article
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Refereed limited

Funding Sources

Natural Sciences and Engineering Research Council of Canada
International Development Research Centre, Ottawa, Canada
CNPq
Social Sciences and Humanities Research Council of Canada
FAPESP

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ASONAM '15

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SIGKDD

ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

Paris, France

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Smith ATacheva JHemsley J(2022)Visual Semantics of Memes: (Re)Interpreting Memetic Content and Form for Information StudiesProceedings of the Association for Information Science and Technology10.1002/pra2.73159:1(800-802)Online publication date: 14-Oct-2022
https://doi.org/10.1002/pra2.731
Valensise CSerra AGaleazzi AEtta GCinelli MQuattrociocchi W(2021)Entropy and complexity unveil the landscape of memes evolutionScientific Reports10.1038/s41598-021-99468-611:1Online publication date: 8-Oct-2021
https://doi.org/10.1038/s41598-021-99468-6
Safari RRahmani AAlizadeh S(2019)User behavior mining on social media: a systematic literature reviewMultimedia Tools and Applications10.1007/s11042-019-08046-6Online publication date: 17-Aug-2019
https://doi.org/10.1007/s11042-019-08046-6
French J(2017)Image-based memes as sentiment predictors2017 International Conference on Information Society (i-Society)10.23919/i-Society.2017.8354676(80-85)Online publication date: Jul-2017
https://doi.org/10.23919/i-Society.2017.8354676
Dang AMoh’d AGruzd AMilios EMinghim R(2017)An Offline–Online Visual Framework for Clustering Memes in Social MediaFrom Social Data Mining and Analysis to Prediction and Community Detection10.1007/978-3-319-51367-6_1(1-29)Online publication date: 22-Mar-2017
https://doi.org/10.1007/978-3-319-51367-6_1
Dang ASmit MMoh'd AMinghim RMilios ESubrahmanian VRokne JKumar RCaverlee JTong H(2016)Toward understanding how users respond to rumours in social mediaProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3192424.3192572(777-784)Online publication date: 18-Aug-2016
https://dl.acm.org/doi/10.5555/3192424.3192572
Dang AMoh'd AMilios EMinghim RPapagiannakis GThalmann DTrahanias P(2016)What is in a RumourProceedings of the 33rd Computer Graphics International10.1145/2949035.2949040(17-20)Online publication date: 28-Jun-2016
https://dl.acm.org/doi/10.1145/2949035.2949040
Dang ASmit MMoh'd AMinghim RMilios E(2016)Toward understanding how users respond to rumours in social media2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2016.7752326(777-784)Online publication date: Aug-2016
https://doi.org/10.1109/ASONAM.2016.7752326

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