Abstract
We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features. Our method extracts a set of features from the timelines of news Twitter accounts by reading their posts as chunks, rather than dealing with each tweet independently. We show the experimental benefits of modeling latent stylistic signatures of mixed fake and real news with a sequential model over a wide range of strong baselines.
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Notes
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Later on, the authors created an online API for the system called Botometer in: https://botometer.iuni.iu.edu.
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- 3.
NRC has also two sentiment categories, positive and negative.
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We considered 2 or more consecutive characters, and 3 or more consecutive letters.
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Experimentally, we found that the GloVe model achieves better results than Google News word2vec or fastText models.
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Many of the accounts were deactivated during the collecting process, consequently only 144 accounts were used.
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Acknowledgment
The work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31).
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Ghanem, B., Ponzetto, S.P., Rosso, P. (2020). FacTweet: Profiling Fake News Twitter Accounts. In: Espinosa-Anke, L., Martín-Vide, C., Spasić, I. (eds) Statistical Language and Speech Processing. SLSP 2020. Lecture Notes in Computer Science(), vol 12379. Springer, Cham. https://doi.org/10.1007/978-3-030-59430-5_3
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