research-article

Content-driven detection of campaigns in social media

Authors:

Kyumin Lee,

James Caverlee,

Zhiyuan Cheng,

Daniel Z. SuiAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 551 - 556

https://doi.org/10.1145/2063576.2063658

Published: 24 October 2011 Publication History

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Abstract

We study the problem of detecting coordinated free text campaigns in large-scale social media. These campaigns -- ranging from coordinated spam messages to promotional and advertising campaigns to political astro-turfing -- are growing in significance and reach with the commensurate rise of massive-scale social systems. Often linked by common "talking points", there has been little research in detecting these campaigns. Hence, we propose and evaluate a content-driven framework for effectively linking free text posts with common "talking points" and extracting campaigns from large-scale social media. One of the salient aspects of the framework is an investigation of graph mining techniques for isolating coherent campaigns from large message-based graphs. Through an experimental study over millions of Twitter messages we identify five major types of campaigns -- Spam, Promotion, Template, News, and Celebrity campaigns -- and we show how these campaigns may be extracted with high precision and recall.

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Cited By

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Manchanayaka IZaidi ZKarunasekera SLeckie C(2024)Identifying Coordinated Activities on Online Social Networks Using Contrast Pattern Mining2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651552(1-9)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651552
Moghaddam SAbbaspour M(2023)Friendship Preference: Scalable and Robust Category of Features for Social Bot DetectionIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.315900720:2(1516-1528)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TDSC.2022.3159007
Faroughi ABoostani RTajalizadeh HJavidan R(2023)ARD-Stream: An adaptive radius density-based stream clusteringFuture Generation Computer Systems10.1016/j.future.2023.07.027149(416-431)Online publication date: Dec-2023
https://doi.org/10.1016/j.future.2023.07.027
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Index Terms

Content-driven detection of campaigns in social media
1. Applied computing
  1. Law, social and behavioral sciences
2. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Published In

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2011

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CIKM '11

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CIKM '11: International Conference on Information and Knowledge Management

October 24 - 28, 2011

Glasgow, Scotland, UK

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all

Manchanayaka IZaidi ZKarunasekera SLeckie C(2024)Identifying Coordinated Activities on Online Social Networks Using Contrast Pattern Mining2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651552(1-9)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651552
Moghaddam SAbbaspour M(2023)Friendship Preference: Scalable and Robust Category of Features for Social Bot DetectionIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.315900720:2(1516-1528)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TDSC.2022.3159007
Faroughi ABoostani RTajalizadeh HJavidan R(2023)ARD-Stream: An adaptive radius density-based stream clusteringFuture Generation Computer Systems10.1016/j.future.2023.07.027149(416-431)Online publication date: Dec-2023
https://doi.org/10.1016/j.future.2023.07.027
Chakraborty KBhattacharyya SBag R(2020)A Survey of Sentiment Analysis from Social Media DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29569577:2(450-464)Online publication date: Apr-2020
https://doi.org/10.1109/TCSS.2019.2956957
Mueen AChavoshi NMinnich AZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Taming Social BotsProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3360315(2967-2968)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3360315
Abu-El-Rub NMueen A(2019)BotCamp: Bot-driven Interactions in Social CampaignsThe World Wide Web Conference10.1145/3308558.3313420(2529-2535)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313420
Tajalizadeh HBoostani R(2019)A Novel Stream Clustering Framework for Spam Detection in TwitterIEEE Transactions on Computational Social Systems10.1109/TCSS.2019.29108186:3(525-534)Online publication date: Jun-2019
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Liang XWang CZhao G(2019)Enhancing Content Marketing Article Detection With Graph AnalysisIEEE Access10.1109/ACCESS.2019.29280947(94869-94881)Online publication date: 2019
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Liu YLiu YZhou KZhang MMa SBarrett RCummings RAgichtein EGabrilovich E(2017)Detecting Collusive Spamming Activities in Community Question AnsweringProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052594(1073-1082)Online publication date: 3-Apr-2017
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Bhanushali ASubbanarasimha RSrinivasa S(2017)Identifying Opinion Drivers on Social MediaOn the Move to Meaningful Internet Systems. OTM 2017 Conferences10.1007/978-3-319-69459-7_17(242-253)Online publication date: 21-Oct-2017
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