News environment-knowledge perception for fake news detection

Chenxu Jia; Lin Zhang

doi:10.1117/12.2681613

1 June 2023 News environment-knowledge perception for fake news detection

Chenxu Jia, Lin Zhang

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127181M (2023) https://doi.org/10.1117/12.2681613
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

Automatic fake news detection is crucial for society. Existing methods mainly focus on the post's content or taking advantage of external sources to make a decision. Recently a new approach called NEP has been proposed, it constructed out news environment for each news to capture popularity and novelty as other evidence. However, NEP neglects the changeable environment and it is weak when news breaks out or is published several times. Commonsense knowledge related to a post has sufficient reliability compared with the news environment or external news evidence, and it can bring a stable benefit for distinguishing fake news. Based on this, we search out commonsense knowledge for each post and propose the News Environment-Knowledge Perception (NEKP) based on NEP. For each post, we search out the related knowledge items on Wikimedia. Then we fuse this knowledge through an existing fake news detector–DeClarE. Finally, we fuse the news environment, knowledge, and the news itself to make a detection. Experiments on NEP datasets show that commonsense knowledge is another helpful piece of evidence.

Citation Download Citation

Chenxu Jia and Lin Zhang "News environment-knowledge perception for fake news detection", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127181M (1 June 2023); https://doi.org/10.1117/12.2681613

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KEYWORDS

Environmental sensing

Feature extraction

Information visualization

Target detection

Education and training

Adversarial training

Visualization

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