Deep learning for misinformation detection on online social networks: A survey and new perspectives

Md Rafiqul ISLAM, Shaowu LIU, Xianzhi WANG, Guandong XU

Research output: Contribution to journalArticlespeer-review

156 Citations (Scopus)

Abstract

Recently, the use of social networks such as Facebook, Twitter, and Sina Weibo has become an inseparable part of our daily lives. It is considered as a convenient platform for users to share personal messages, pictures, and videos. However, while people enjoy social networks, many deceptive activities such as fake news or rumors can mislead users into believing misinformation. Besides, spreading the massive amount of misinformation in social networks has become a global risk. Therefore, misinformation detection (MID) in social networks has gained a great deal of attention and is considered an emerging area of research interest. We find that several studies related to MID have been studied to new research problems and techniques. While important, however, the automated detection of misinformation is difficult to accomplish as it requires the advanced model to understand how related or unrelated the reported information is when compared to real information. The existing studies have mainly focused on three broad categories of misinformation: false information, fake news, and rumor detection. Therefore, related to the previous issues, we present a comprehensive survey of automated misinformation detection on (i) false information, (ii) rumors, (iii) spam, (iv) fake news, and (v) disinformation. We provide a state-of-the-art review on MID where deep learning (DL) is used to automatically process data and create patterns to make decisions not only to extract global features but also to achieve better results. We further show that DL is an effective and scalable technique for the state-of-the-art MID. Finally, we suggest several open issues that currently limit real-world implementation and point to future directions along this dimension. Copyright © 2020 Springer-Verlag GmbH Austria, part of Springer Nature.

Original languageEnglish
Article number82
JournalSocial Network Analysis and Mining
Volume10
DOIs
Publication statusPublished - Sept 2020

Citation

Islam, M. R., Liu, S., Wang, X., & Xu, G. (2020). Deep learning for misinformation detection on online social networks: A survey and new perspectives. Social Network Analysis and Mining, 10, Article 82. https://doi.org/10.1007/s13278-020-00696-x

Keywords

  • Deep learning
  • Neural network
  • Misinformation detection
  • Decision making
  • Online social networks

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