What Is Offensive about Offensive Jokes?

Bicknell, Jeanette

doi:10.5840/philtoday200751430

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…The responses also reveal the nature of sexist humor to exclude and discriminate against the target group as espoused by social identity theory where members of the in-group discriminate against members of the out-group. This is in line with Bicknell (2007), Thomae and Pina (2015) and Ferguson and Ford’s (2008) findings that sexist humor is characterized by malice, negative attitudes and chauvinism that is communicated in a hidden way. Chauvinism appears to validate and enhance personal identity in which perpetrators feel superior to their targets which is a key concept of social identity theory.…”

Section: Resultssupporting

confidence: 89%

Nature and Perception of Sexist Humor at Great Zimbabwe University

Kanyemba

Naidu

2019

The Oriental Anthropologist: A Bi-annual International Journal

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For the majority of women, university represents a time of hopefulness and opportunities such that gendered incidences questioning their academic merit poses a serious setback. Sexist humor is one such incident which communicates a message that females are irrelevant and insignificant. This article discusses the nature and perceptions of sexist humor on University campuses. The views on how students on campus perceive sexist humor are crucial for understanding students’ response and offer a clear understanding of what justifies and normalizes sexist humor. The paper analyzes how the use of language can be connected to sexism and violence. Using a mixed methodology for data collection at Great Zimbabwe University, the paper attempts to link language, sexual violence, misogyny, and sexism as well as chronicle the overall pattern of exclusion and marginalization of women in higher education settings. The findings of the paper present evidence that the institutional and intellectual cultures of educational institutions are permeated with sexual and gender dynamics that have become embedded and naturalized in popular thought. Normalization of verbal harassment contributes to muting victimized women, thus perpetuating a culture in which violence against women becomes part of the social milieu. Thus, this study concludes that while one may consider higher education institutions in Africa as safer spaces for women, these are highly contested terrains as misogyny through sexist humor, among other hindrances, has created an obstacle for women’s equal participation in higher education.

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Section: Resultssupporting

confidence: 89%

Nature and Perception of Sexist Humor at Great Zimbabwe University

Kanyemba

Naidu

2019

The Oriental Anthropologist: A Bi-annual International Journal

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“…Since online toxicity can be relatively scarce (Founta et al, 2018), 3 we start by annotating English Reddit posts, specifically three intentionally offensive subReddits and a corpus of potential microaggressions from Breitfeller et al (2019). By nature, the three offensive subreddits are very likely to have harmful implications, as posts are often made with intents to deride adversity or social inequality (Bicknell, 2007). Microaggressions, on the other hand, are likely to contain subtle biased implications-a natural fit for SOCIAL BIAS FRAMES.…”

Section: Data Selectionmentioning

confidence: 99%

“…We draw from various sources of potentially biased online content, shown in (2019). By nature, the three offensive subreddits are very likely to have harmful implications, as posts are often made with intents to deride adversity or social inequality (Bicknell, 2007). Microaggressions, on the other hand, are likely to contain subtle biased implications-a natural fit for SOCIAL BIAS FRAMES.…”

Section: Data Selectionmentioning

confidence: 99%

Social Bias Frames: Reasoning about Social and Power Implications of Language

Sap

Gabriel

Qin

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

194

190

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Warning: this paper contains content that may be offensive or upsetting. We then establish baseline approaches that learn to recover SOCIAL BIAS FRAMES from unstructured text. We find that while stateof-the-art neural models are effective at highlevel categorization of whether a given statement projects unwanted social bias (80% F 1), they are not effective at spelling out more detailed explanations in terms of SOCIAL BIAS FRAMES. Our study motivates future work that combines structured pragmatic inference with commonsense reasoning on social implications.

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“…To mitigate the challenge of scarcity of online toxicity (Founta et al, 2018), 3 we start by annotating posts made in three intentionally offensive English subReddits (see Table 2). By nature, these are very likely to have harmful implications as they are often posted with intents to deride adversity or social inequality (Bicknell, 2007). Additionally, we include posts from three existing English datasets annotated for toxic or abusive language, filtering out @-replies, retweets, and links.…”

Section: Data Selectionmentioning

confidence: 99%

Social Bias Frames: Reasoning about Social and Power Implications of Language

Sap¹,

Gabriel²,

Qin³

et al. 2019

Preprint

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Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but all the implied meanings that frame people's judgements about others. For example, given a seemingly innocuous statement "we shouldn't lower our standards to hire more women," most listeners will infer the implicature intended by the speaker -that "women (candidates) are less qualified." Most frame semantic formalisms, to date, do not capture such pragmatic frames in which people express social biases and power differentials in language.We introduce SOCIAL BIAS FRAMES, a new conceptual formalism that aims to model the pragmatic frames in which people project social biases and stereotypes on others. In addition, we introduce the Social Bias Inference Corpus, to support large-scale modelling and evaluation with 100k structured annotations of social media posts, covering over 26k implications about a thousand demographic groups.We then establish baseline approaches that learn to recover SOCIAL BIAS FRAMES from unstructured text. We find that while stateof-the-art neural models are effective at highlevel categorization of whether a given statement projects unwanted social bias (86% F 1 ), they are not effective at spelling out more detailed explanations by accurately decoding out SOCIAL BIAS FRAMES. Our study motivates future research that combines structured pragmatic inference with commonsense reasoning on social implications.

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What Is Offensive about Offensive Jokes?

Cited by 7 publications

References 3 publications

Nature and Perception of Sexist Humor at Great Zimbabwe University

Nature and Perception of Sexist Humor at Great Zimbabwe University

Social Bias Frames: Reasoning about Social and Power Implications of Language

Social Bias Frames: Reasoning about Social and Power Implications of Language

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