Rumor Detection in Tweets Using Graph Convolutional Networks

Takumi Takei, Yuichi Sei, Yasuyuki Tahara, Akihiko Ohsuga

2023

Abstract

The recent development of social networking services has made it easier for anyone to get information. On the other hand, rumors which are information whose truth is unverified are not only easy to spread but also can cause damage such as flames, incitement, and slander. Accurate identification of rumors is effective against such problems and may prevent the spread of misinformation. Based on previous research, this study created a dataset of rumors including replies to fact-checked Japanese tweets. Using a GCN-based deep learning classifier, we performed binary classification of whether a tweet is a False rumor or not, and multinomial classification of True rumor, False rumor, and Unclear rumor, varying the amount of propagation information used. The result of binary classification shows that the maximum accuracy is 0.637, and the maximum F value is 0.641, while the result of multinomial classification shows that the maximum accuracy is 0.547, and the maximum F value is 0.460. We discussed the effectiveness of propagation information and deep learning for detecting Japanese rumors.

Paper Citation

in Harvard Style

Takei T., Sei Y., Tahara Y. and Ohsuga A. (2023). Rumor Detection in Tweets Using Graph Convolutional Networks. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 397-404. DOI: 10.5220/0011676600003393

in Bibtex Style

@conference{icaart23,
author={Takumi Takei and Yuichi Sei and Yasuyuki Tahara and Akihiko Ohsuga},
title={Rumor Detection in Tweets Using Graph Convolutional Networks},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={397-404},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011676600003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Rumor Detection in Tweets Using Graph Convolutional Networks
SN - 978-989-758-623-1
AU - Takei T.
AU - Sei Y.
AU - Tahara Y.
AU - Ohsuga A.
PY - 2023
SP - 397
EP - 404
DO - 10.5220/0011676600003393