Paying Attention to Deflections: Mining Pragmatic Nuances for Whataboutism Detection in Online Discourse

TL;DR

This paper explores the detection of whataboutism in online discourse, introducing new datasets and a novel attention-based method that improves accuracy over previous approaches.

Contribution

It distinguishes between different uses of whataboutism, introduces new datasets from Twitter and YouTube, and proposes an attention-based detection method.

Findings

Achieved 4% and 10% improvements over state-of-the-art in Twitter and YouTube datasets.

Highlighted the linguistic distinction between 'what about' constructs and whataboutism.

Identified unique challenges in detecting whataboutism accurately.

Abstract

Whataboutism, a potent tool for disrupting narratives and sowing distrust, remains under-explored in quantitative NLP research. Moreover, past work has not distinguished its use as a strategy for misinformation and propaganda from its use as a tool for pragmatic and semantic framing. We introduce new datasets from Twitter and YouTube, revealing overlaps as well as distinctions between whataboutism, propaganda, and the tu quoque fallacy. Furthermore, drawing on recent work in linguistic semantics, we differentiate the `what about' lexical construct from whataboutism. Our experiments bring to light unique challenges in its accurate detection, prompting the introduction of a novel method using attention weights for negative sample mining. We report significant improvements of 4% and 10% over previous state-of-the-art methods in our Twitter and YouTube collections, respectively.