Paying Attention to Deflections: Mining Pragmatic Nuances for
  Whataboutism Detection in Online Discourse
Khiem Phi Noushin Salek Faramarzi Chenlu Wang Ritwik Banerjee
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.