Fake News Detection Through Multi-Perspective Speaker Profiles

Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, Chu-Ren Huang


Abstract
Automatic fake news detection is an important, yet very challenging topic. Traditional methods using lexical features have only very limited success. This paper proposes a novel method to incorporate speaker profiles into an attention based LSTM model for fake news detection. Speaker profiles contribute to the model in two ways. One is to include them in the attention model. The other includes them as additional input data. By adding speaker profiles such as party affiliation, speaker title, location and credit history, our model outperforms the state-of-the-art method by 14.5% in accuracy using a benchmark fake news detection dataset. This proves that speaker profiles provide valuable information to validate the credibility of news articles.
Anthology ID:
I17-2043
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Editors:
Greg Kondrak, Taro Watanabe
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
252–256
Language:
URL:
https://aclanthology.org/I17-2043
DOI:
Bibkey:
Cite (ACL):
Yunfei Long, Qin Lu, Rong Xiang, Minglei Li, and Chu-Ren Huang. 2017. Fake News Detection Through Multi-Perspective Speaker Profiles. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 252–256, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
Fake News Detection Through Multi-Perspective Speaker Profiles (Long et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-2043.pdf
Data
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