research-article

Modeling Topics and Behavior of Microbloggers: An Integrated Approach

Authors:

Tuan-Anh Hoang,

Ee-Peng LimAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 8, Issue 3

Article No.: 44, Pages 1 - 37

https://doi.org/10.1145/2990507

Published: 20 April 2017 Publication History

Abstract

Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users’ topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user’s dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content data only. By associating user behavior with the latent background and personal topics, GBT helps to model user behavior by the two types of topics. GBT also distinguishes itself from other earlier works by modeling multiple types of behavior together. Our experiments on two Twitter datasets show that GBT can effectively mine the representative topics for each realm. We also demonstrate that GBT significantly outperforms other state-of-the-art models in modeling content topics and user profiling.

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Index Terms

Modeling Topics and Behavior of Microbloggers: An Integrated Approach
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 8, Issue 3

Special Issue: Mobile Social Multimedia Analytics in the Big Data Era and Regular Papers

May 2017

320 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3040485

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2017 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: 20 April 2017

Accepted: 01 August 2016

Revised: 01 September 2015

Received: 01 May 2015

Published in TIST Volume 8, Issue 3

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Living Analytics Research Centre, Singapore Management University

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Cited By

Shang RAra BZada INazir SUllah ZKhan S(2021)Analysis of Simple K-Mean and Parallel K-Mean Clustering for Software Products and Organizational Performance Using Education Sector DatasetScientific Programming10.1155/2021/99883182021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/9988318
Wu S(2020)An User Intention Mining Model based on Fractal Time Series PatternFractals10.1142/S0218348X20400174Online publication date: 6-May-2020
https://doi.org/10.1142/S0218348X20400174
Liu DWang ZZhang J(2020)Video Stream Distribution Scheme Based on Edge Computing Network and User Interest Content ModelIEEE Access10.1109/ACCESS.2020.29710258(30734-30744)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2971025
Mahalakshmi GHemadharsana SMuthuselvi GSendhilkumar S(2020)Learning Deep Topics of InterestNew Trends in Computational Vision and Bio-inspired Computing10.1007/978-3-030-41862-5_156(1517-1532)Online publication date: 2020
https://doi.org/10.1007/978-3-030-41862-5_156
Cimino MLazzeri APedrycz WVaglini G(2018)Using Stigmergy to Distinguish Event-Specific Topics in Social DiscussionsSensors10.3390/s1807211718:7(2117)Online publication date: 2-Jul-2018
https://doi.org/10.3390/s18072117
Liu WZhang JLiang ZPeng LCai J(2018)Content Popularity Prediction and Caching for ICN: A Deep Learning Approach With SDNIEEE Access10.1109/ACCESS.2017.27817166(5075-5089)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2017.2781716

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