research-article

Modeling user posting behavior on social media

Authors:

Qing YangAuthors Info & Claims

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Pages 545 - 554

https://doi.org/10.1145/2348283.2348358

Published: 12 August 2012 Publication History

Abstract

User generated content is the basic element of social media websites. Relatively few studies have systematically analyzed the motivation to create and share content, especially from the perspective of a common user. In this paper, we perform a comprehensive analysis of user posting behavior on a popular social media website, Twitter. Specifically, we assume that user behavior is mainly influenced by three factors: breaking news, posts from social friends and user's intrinsic interest, and propose a mixture latent topic model to combine all these factors. We evaluated our model on a large-scale Twitter dataset from three different perspectives: the perplexity of held-out content, the performance of predicting retweets and the quality of generated latent topics. The results were encouraging, our model clearly outperformed its competitors.

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Cited By

Peslak AKovalchick LMenon P(2024)Social Media Users Only Have Two Clusters: A United States AnalysisJournal of Information Systems Applied Research10.62273/MFUN297217:3(43-55)Online publication date: 2024
https://doi.org/10.62273/MFUN2972
Jin YLee YSharma KYe MSikka KDivakaran AKumar SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Predicting Information Pathways Across Online CommunitiesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599470(1044-1056)Online publication date: 4-Aug-2023
https://doi.org/10.1145/3580305.3599470
Saidi FTrabelsi ZThangaraj E(2022)A novel framework for semantic classification of cyber terrorist communities on TwitterEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105271115:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.engappai.2022.105271
Show More Cited By

Index Terms

Modeling user posting behavior on social media
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

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Published In

cover image ACM Conferences

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

August 2012

1236 pages

ISBN:9781450314725

DOI:10.1145/2348283

General Chair:
William Hersh
Oregon Health & Science University, USA
,
Program Chairs:
Jamie Callan
Carnegie Mellon University, USA
,
Yoelle Maarek
Yahoo! Research, Israel
,
Mark Sanderson
Royal Melbourne Institute of Technology, Australia

Copyright © 2012 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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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

Publication History

Published: 12 August 2012

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SIGIR '12

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SIGIR

SIGIR '12: The 35th International ACM SIGIR conference on research and development in Information Retrieval

August 12 - 16, 2012

Oregon, Portland, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Peslak AKovalchick LMenon P(2024)Social Media Users Only Have Two Clusters: A United States AnalysisJournal of Information Systems Applied Research10.62273/MFUN297217:3(43-55)Online publication date: 2024
https://doi.org/10.62273/MFUN2972
Jin YLee YSharma KYe MSikka KDivakaran AKumar SSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Predicting Information Pathways Across Online CommunitiesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599470(1044-1056)Online publication date: 4-Aug-2023
https://doi.org/10.1145/3580305.3599470
Saidi FTrabelsi ZThangaraj E(2022)A novel framework for semantic classification of cyber terrorist communities on TwitterEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105271115:COnline publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.engappai.2022.105271
Bartal AJagodnik K(2021)Role-Aware Information Spread in Online Social NetworksEntropy10.3390/e2311154223:11(1542)Online publication date: 19-Nov-2021
https://doi.org/10.3390/e23111542
Zhu ZCao JZhou TMin HLiu B(2021)Understanding User Topic Preferences across Multiple Social Networks2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671607(590-599)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671607
Hu LKearney MFrisby C(2021)Tweeting and Retweeting: Gender Discrepancies in Discursive Political Engagement and Influence on TwitterJournal of Gender Studies10.1080/09589236.2021.199534032:5(441-459)Online publication date: 24-Oct-2021
https://doi.org/10.1080/09589236.2021.1995340
Suzuki YYoneda YNadamoto A(2021)Analysis of Behavioral Facilitation Tweets for Large-Scale Natural Disasters Dataset Using Machine LearningDatabase and Expert Systems Applications10.1007/978-3-030-86475-0_16(161-169)Online publication date: 1-Sep-2021
https://doi.org/10.1007/978-3-030-86475-0_16
Wani MAgarwal NBours P(2020)Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection SystemElectronics10.3390/electronics1001000510:1(5)Online publication date: 23-Dec-2020
https://doi.org/10.3390/electronics10010005
Niu BSui LTang JDong RKou KWang Z(2020)Prediction of Microblog Users' Forwarding Behavior Based on Interactive and Active InformationProceedings of the 2020 International Conference on Aviation Safety and Information Technology10.1145/3434581.3434680(554-559)Online publication date: 14-Oct-2020
https://dl.acm.org/doi/10.1145/3434581.3434680
Arora UDutta HJoshi BChetan AChakraborty T(2020)Analyzing and Detecting Collusive Users Involved in Blackmarket Retweeting ActivitiesACM Transactions on Intelligent Systems and Technology10.1145/338053711:3(1-24)Online publication date: 18-Apr-2020
https://dl.acm.org/doi/10.1145/3380537
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