research-article

Modeling a Retweet Network via an Adaptive Bayesian Approach

Authors:

Junghoo ChoAuthors Info & Claims

WWW '16: Proceedings of the 25th International Conference on World Wide Web

Pages 459 - 469

https://doi.org/10.1145/2872427.2882985

Published: 11 April 2016 Publication History

Abstract

Twitter (and similar microblogging services) has become a central nexus for discussion of the topics of the day. Twitter data contains rich content and structured information on users' topics of interest and behavior patterns. Correctly analyzing and modeling Twitter data enables the prediction of the user behavior and preference in a variety of practical applications, such as tweet recommendation and followee recommendation. Although a number of models have been developed on Twitter data in prior work, most of these only model the tweets from users, while neglecting their valuable retweet information in the data. Models would enhance their predictive power by incorporating users' retweet content as well as their retweet behavior. In this paper, we propose two novel Bayesian nonparametric models, URM and UCM, on retweet data. Both of them are able to integrate the analysis of tweet text and users' retweet behavior in the same probabilistic framework. Moreover, they both jointly model users' interest in tweet and retweet. As nonparametric models, URM and UCM can automatically determine the parameters of the models based on input data, avoiding arbitrary parameter settings. Extensive experiments on real-world Twitter data show that both URM and UCM are superior to all the baselines, while UCM further outperforms URM, confirming the appropriateness of our models in retweet modeling.

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Index Terms

Modeling a Retweet Network via an Adaptive Bayesian Approach
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools
      1. Social networking sites

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WWW '16: Proceedings of the 25th International Conference on World Wide Web

April 2016

1482 pages

ISBN:9781450341431

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Copyright © 2016 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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Published: 11 April 2016

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WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

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WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;

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Fu XCheng SZhao LLv J(2022)Retweet Prediction Based on Multidimensional FeaturesWireless Communications & Mobile Computing10.1155/2022/18635682022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1863568
Wang LLi JZeng XWong K(2022)Successful New-entry Prediction for Multi-Party Online Conversations via Latent Topics and Discourse ModelingProceedings of the ACM Web Conference 202210.1145/3485447.3512285(1663-1672)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3512285
Villalba-Diez JMolina MSchmidt D(2021)Geometric Deep Lean Learning: Evaluation Using a Twitter Social NetworkApplied Sciences10.3390/app1115677711:15(6777)Online publication date: 23-Jul-2021
https://doi.org/10.3390/app11156777
Casarin RCorrea JCamargo JDakduk Ster Horst EMolina G(2021)What makes a tweet be retweeted? A Bayesian trigram analysis of tweet propagation during the 2015 Colombian political campaignJournal of Information Science10.1177/016555151988605647:3(297-305)Online publication date: 1-Jun-2021
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Sun WLiu XShen F(2021)Learning Dynamic User Interactions for Online Forum Commenting Prediction2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00168(1342-1347)Online publication date: Dec-2021
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Li THsu WLee MChieu H(2020)Probabilistic Decision Modeling in Social Networks2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00079(466-473)Online publication date: Nov-2020
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Alothali EHayawi KAlashwal H(2020)Characteristics of Similar-Context Trending Hashtags in Twitter: A Case StudyWeb Services – ICWS 202010.1007/978-3-030-59618-7_10(150-163)Online publication date: 19-Sep-2020
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