Fake News Detection Based on Explicit and Implicit Signals of a Hybrid Crowd: Proposal, Impacts and Perspectives
Resumo
The problem of automatic Fake News detection in digital media of news distribution (DMND - e.g., social networks,online newspaper) has become even more relevant. Among the main detection approaches, the one based on crowd signals from DMND users has stood out by obtaining promising results. Although promising, the Crowd Signals approach has a significant limitation: it depends on the explicit user opinion (which is not always available) about the classification of news. Facing this limitation, the present work raises the hypothesis that it is possible to build models of Fake News detection with a performance comparable to the Crowd Signals based approach, avoiding the dependence on the explicit opinion of DMND users. To validate this hypothesis, the present work proposes HCS, an approach based on crowd signals that considers implicit user opinions instead of the explicit ones. The implicit opinions are inferred from the behavior of users concerning the dissemination of the news. Inspired in Meta-Learning, the HCS can also use the explicit opinions from machines (news classification models) to complement the implicit user opinions by means of hybrid Crowds. Experiments presented significant evidence that confirms the raised hypothesis.
Referências
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