Authors:
Takumi Takei
;
Yuichi Sei
;
Yasuyuki Tahara
and
Akihiko Ohsuga
Affiliation:
Department of Informatics, The University of Electro-Communications, Chofu, Tokyo, Japan
Keyword(s):
Rumor Detection, Deep Learning, Natural Language Processing, Twitter.
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 di
scussed the effectiveness of propagation information and deep learning for detecting Japanese rumors.
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