{"title":"Bullshit, Pragmatic Deception, and Natural Language Processing","authors":"Oliver Deck","doi":"10.5210/dad.2023.103","DOIUrl":null,"url":null,"abstract":"Fact checking and fake news detection has garnered increasing interest within the natural language processing (NLP) community in recent years, yet other aspects of misinformation remain unexplored. One such phenomenon is `bullshit', which different disciplines have tried to define since it first entered academic discussion nearly four decades ago. Fact checking bullshitters is useless, because factual reality typically plays no part in their assertions: Where liars deceive about content, bullshitters deceive about their goals. Bullshitting is misleading about language itself, which necessitates identifying the points at which pragmatic conventions are broken with deceptive intent. This paper aims to introduce bullshitology into the field of NLP by tying it to questions in a QUD-based definition, providing two approaches to bullshit annotation, and finally outlining which combinations of NLP methods will be helpful to classify which kinds of linguistic bullshit.","PeriodicalId":37604,"journal":{"name":"Dialogue and Discourse","volume":"77 1","pages":"56-87"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Dialogue and Discourse","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5210/dad.2023.103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Arts and Humanities","Score":null,"Total":0}
引用次数: 0
Abstract
Fact checking and fake news detection has garnered increasing interest within the natural language processing (NLP) community in recent years, yet other aspects of misinformation remain unexplored. One such phenomenon is `bullshit', which different disciplines have tried to define since it first entered academic discussion nearly four decades ago. Fact checking bullshitters is useless, because factual reality typically plays no part in their assertions: Where liars deceive about content, bullshitters deceive about their goals. Bullshitting is misleading about language itself, which necessitates identifying the points at which pragmatic conventions are broken with deceptive intent. This paper aims to introduce bullshitology into the field of NLP by tying it to questions in a QUD-based definition, providing two approaches to bullshit annotation, and finally outlining which combinations of NLP methods will be helpful to classify which kinds of linguistic bullshit.
期刊介绍：
D&D seeks previously unpublished, high quality articles on the analysis of discourse and dialogue that contain -experimental and/or theoretical studies related to the construction, representation, and maintenance of (linguistic) context -linguistic analysis of phenomena characteristic of discourse and/or dialogue (including, but not limited to: reference and anaphora, presupposition and accommodation, topicality and salience, implicature, ---discourse structure and rhetorical relations, discourse markers and particles, the semantics and -pragmatics of dialogue acts, questions, imperatives, non-sentential utterances, intonation, and meta--communicative phenomena such as repair and grounding) -experimental and/or theoretical studies of agents'' information states and their dynamics in conversational interaction -new analytical frameworks that advance theoretical studies of discourse and dialogue -research on systems performing coreference resolution, discourse structure parsing, event and temporal -structure, and reference resolution in multimodal communication -experimental and/or theoretical results yielding new insight into non-linguistic interaction in -communication -work on natural language understanding (including spoken language understanding), dialogue management, -reasoning, and natural language generation (including text-to-speech) in dialogue systems -work related to the design and engineering of dialogue systems (including, but not limited to: -evaluation, usability design and testing, rapid application deployment, embodied agents, affect detection, -mixed-initiative, adaptation, and user modeling). -extremely well-written surveys of existing work. Highest priority is given to research reports that are specifically written for a multidisciplinary audience. The audience is primarily researchers on discourse and dialogue and its associated fields, including computer scientists, linguists, psychologists, philosophers, roboticists, sociologists.
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