Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of c-QA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.

Fact Checking in Community Forums / Mihaylova, T; Nakov, P; Marquez, L; Barron-Cedeno, A; Mohtarami, M; Karadzhov, G; Glass, J. - ELETTRONICO. - (2018), pp. 5309-5316. (Intervento presentato al convegno 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 tenutosi a New Orleans, United States nel 2018).

Fact Checking in Community Forums

Barron-Cedeno, A;
2018

Abstract

Community Question Answering (cQA) forums are very popular nowadays, as they represent effective means for communities around particular topics to share information. Unfortunately, this information is not always factual. Thus, here we explore a new dimension in the context of c-QA, which has been ignored so far: checking the veracity of answers to particular questions in cQA forums. As this is a new problem, we create a specialized dataset for it. We further propose a novel multi-faceted model, which captures information from the answer content (what is said and how), from the author profile (who says it), from the rest of the community forum (where it is said), and from external authoritative sources of information (external support). Evaluation results show a MAP value of 86.54, which is 21 points absolute above the baseline.
2018
32nd AAAI Conference on Artificial Intelligence, AAAI 2018
5309
5316
Fact Checking in Community Forums / Mihaylova, T; Nakov, P; Marquez, L; Barron-Cedeno, A; Mohtarami, M; Karadzhov, G; Glass, J. - ELETTRONICO. - (2018), pp. 5309-5316. (Intervento presentato al convegno 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 tenutosi a New Orleans, United States nel 2018).
Mihaylova, T; Nakov, P; Marquez, L; Barron-Cedeno, A; Mohtarami, M; Karadzhov, G; Glass, J
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/709164
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