Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims

Abstract

One key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact-checking won't suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions visible to us. To better understand controversial issues, one needs to view them from a diverse yet comprehensive set of perspectives.

MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims

“…Additional labels can then be added to the datasets to better predict veracity, for instance by jointly training stance and veracity prediction models. Methods not shown in the table, but related to fact checking, are stance detection for claims (Ferreira and Vlachos, 2016;Pomerleau and Rao, 2017;Augenstein et al, 2016a;Kochkina et al, 2017;Augenstein et al, 2016b;Zubiaga et al, 2018;Riedel et al, 2017), satire detection (Rubin et al, 2016), clickbait detection (Karadzhov et al, 2017), conspiracy news detection (Tacchini et al, 2017), rumour cascade detection (Vosoughi et al, 2018) and claim perspectives detection (Chen et al, 2019).…”

STANCY: Stance Classification Based on Consistency Cues

“…The goal of the stance classification task is to determine the stance of the user Perspective (P ) with respect to the Claim (C). Since this task involves a pair of sentences (C and P ), we follow the approach for sentence pair classification task as proposed in Devlin et al (2019); Chen et al (2019). In order to obtain the representation X P |C of P with respect to C, this sentence pair is fused into a single input sequence by using a special classification token ([CLS]) and a separator token…”

“…), positive/negative sentiment words (Hu and Liu, 2004), MPQA subjective lexicon (Wilson et al, 2005) and bias lexicon (Recasens et al, 2013) along with sentiment scores as features. BERT BASE : Approach proposed in Chen et al (2019) (as described in Section 2.1). Human: Human performance on this task as reported in Chen et al (2019).…”

“…BERT BASE : Approach proposed in Chen et al (2019) (as described in Section 2.1). Human: Human performance on this task as reported in Chen et al (2019).…”

“…To achieve a deeper understanding of these claims, we need to understand users' perspectives and stance towards the claims. Recent research (FNC-1, 2016;Chen et al, 2019) has shown stance classification to be a critical step for information credibility and automated fact-checking.…”