Abstract
Information verification is a hot topic, especially because of the fact that the rate of information generation is so high and increases every day, mainly in social networks like Twitter. This also causes social networks be invoked as a news agency for most of the people. Accordingly, information verification in social networks becomes more significant. Therefore, in this paper a method for information verification on Twitter is proposed. The proposed method for Tweet verification is going to employ textual entailment methods for enhancement of previous verification methods on Twitter. Aggregating the results of entailment methods in addition to the state-of-the-art methods, can enhance the outcomes of tweet verification. Also, as writing style of tweets is not perfect and formal enough for textual entailment, we used the language model to supplement tweets with more formal and proper texts for textual entailment. Although, singly utilizing of entailment methods for information verification may result in acceptable results, it is not possible to provide relevant and valid sources for all of the tweets, especially in early times by posting tweets. Therefore, we utilized other sources like as a User Conversational Tree (UCT) besides utilizing entailment methods for tweet information verification. The analysis of UCT is based on the pattern extraction from the UCT. Experimental results indicate that using entailment methods enhances tweet verification.
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This research was in part supported by a grant from IPM. (No. CS1397-4-98).
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Yavary, A., Sajedi, H., Abadeh, M.S. (2020). Information Verification Enhancement Using Entailment Methods. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_17
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