Scalable Fact-checking with Human-in-the-Loop

Autor(es)

Yang, Jing ; Vega-Oliveros, Didier ; Seibt, Tais ; Rocha, Anderson ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2021 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS); v. N/A, p. 6-pg., 2021-01-01.

Resumo

Researchers have been investigating automated solutions for fact-checking in various fronts. However, current approaches often overlook the fact that information released every day is escalating, and a large amount of them overlap. Intending to accelerate fact-checking, we bridge this gap by proposing a new pipeline - grouping similar messages and summarizing them into aggregated claims. Specifically, we first clean a set of social media posts (e.g., tweets) and build a graph of all posts based on their semantics; Then, we perform two clustering methods to group the messages for further claim summarization. We evaluate the summaries both quantitatively with ROUGE scores and qualitatively with human evaluation. We also generate a graph of summaries to verify that there is no significant overlap among them. The results reduced 28,818 original messages to 700 summary claims, showing the potential to speed up the fact-checking process by organizing and selecting representative claims from massive disorganized and redundant messages. (AU)

• Processo FAPESP: 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade
• Beneficiário: Anderson de Rezende Rocha
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 19/26283-5 - Aprendendo pistas visuais da passagem do tempo
• Beneficiário: Didier Augusto Vega Oliveros
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 19/04053-8 - Reconstrução de eventos a partir de dados visuais heterogêneos
• Beneficiário: Jing Yang
• Modalidade de apoio: Bolsas no Brasil - Doutorado