research-article

Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning

Authors:

Ning GuAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 4

Article No.: 79, Pages 1 - 28

https://doi.org/10.1145/3495530

Published: 11 January 2022 Publication History

Abstract

User-generated contents (UGC) in social media are the direct expression of users’ interests, preferences, and opinions. User behavior prediction based on UGC has increasingly been investigated in recent years. Compared to learning a person’s behavioral patterns in each social media site separately, jointly predicting user behavior in multiple social media sites and complementing each other (cross-site user behavior prediction) can be more accurate. However, cross-site user behavior prediction based on UGC is a challenging task due to the difficulty of cross-site data sampling, the complexity of UGC modeling, and uncertainty of knowledge sharing among different sites. For these problems, we propose a Cross-Site Multi-Task (CSMT) learning method to jointly predict user behavior in multiple social media sites. CSMT mainly derives from the hierarchical attention network and multi-task learning. Using this method, the UGC in each social media site can obtain fine-grained representations in terms of words, topics, posts, hashtags, and time slices as well as the relevances among them, and prediction tasks in different social media sites can be jointly implemented and complement each other. By utilizing two cross-site datasets sampled from Weibo, Douban, Facebook, and Twitter, we validate our method’s superiority on several classification metrics compared with existing related methods.

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Index Terms

Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning
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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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 4

October 2022

812 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3501285

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 11 January 2022

Accepted: 01 November 2021

Revised: 01 June 2021

Received: 01 January 2021

Published in TOIS Volume 40, Issue 4

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Ekle OEberle W(2024)Anomaly Detection in Dynamic Graphs: A Comprehensive SurveyACM Transactions on Knowledge Discovery from Data10.1145/3669906Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1145/3669906
Li YWang SXu SYin J(2024)Trustworthy semi‐supervised anomaly detection for online‐to‐offline logistics business in merchant identificationCAAI Transactions on Intelligence Technology10.1049/cit2.123019:3(544-556)Online publication date: 14-Apr-2024
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