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.

翻译：摘要： “那又怎么说”（Whataboutism）作为一种破坏叙事、播撒不信任的有力工具，在定量自然语言处理（NLP）研究中仍未得到充分探索。此外，以往的研究未能区分其作为误导信息与宣传策略的应用，以及作为语用与语义框架工具的应用。我们引入了来自推特和YouTube的新数据集，揭示了“那又怎么说”、宣传以及“你也一样”（tu quoque）谬误之间的重叠与区别。此外，借鉴语言语义学的最新研究，我们将“那怎么说”（'what about'）这一词汇构式与“那又怎么说”策略区分开来。我们的实验揭示了其准确检测面临的独特挑战，进而提出了一种利用注意力权重进行负样本挖掘的新方法。据报告，在推特和YouTube数据集中，该方法较以往最先进方法分别取得了4%和10%的显著改进。