Slurs, roles and power

Popa-Wyatt, Mihaela; Wyatt, Jeremy L.

doi:10.1007/s11098-017-0986-2

Cited by 54 publications

(22 citation statements)

References 48 publications

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“…The aforementioned items of Thurlow (2001), Flores (2015) or Popa-Wyatt and Wyatt (2018) are quite similar to each other. The findings may show how slurs express disparagement.…”

Section: Introductionmentioning

confidence: 55%

The Identification of Slurs and Swear Words in Bronte Sisters’ Novels

Suryanovika

Negara

2019

Lingua Cultura

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This research aimed at identifying the categories of slurs, presenting how swear words expressed in male or female characters of Bronte sisters’ novels, and examining the social status scale in presenting slurs. The research was a qualitative content analysis of which process was categorizing, comparing, and concluding. The researchers employed MAXQDA 2018.1 (the data analysis tool) for analyzing the samples of five female and male main characters of the novel of Emily Bronte (Wuthering Heights), Charlotte Bronte (Jane Eyre), and Anne Bronte (The Tenant of Wildfell Hall). The research has shown three out of nine Thurlow’s pejorative items (social personality, phallocentric, and sexist), the possible formation of social personality slurs, the identification of swear words for showing speakers’ emotional states, and the influence of social status scale on the expression of slurs. It proves that slurs and swear words are used to deliver a derogatory attitude. The sexist slurs are not only delivered from male characters to female characters, but it is also found in Catherine Earnshaw targeting Nelly although they have similar gender background (female). Slurs are found in the characters from both high and low social rank since the plot develops the relationship amongst the characters. One unexpected finding is the different swear words between the characters. Swear words found in the novel are not only dominated by the word devil, damn, or by hell, but also the word deuce and humbug. The varied swear words proves that the male characters do not dominantly produce swear words, but also euphemistic expression.

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“…The aforementioned items of Thurlow (2001), Flores (2015) or Popa-Wyatt and Wyatt (2018) are quite similar to each other. The findings may show how slurs express disparagement.…”

Section: Introductionmentioning

confidence: 55%

The Identification of Slurs and Swear Words in Bronte Sisters’ Novels

Suryanovika

Negara

2019

Lingua Cultura

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“…Furthermore, there are many expressions that are not inherently offensive, however they can be so in the right context [70,81]. But even in the case of slurs, not only different slurs hold a different degree of offense [69], the offense can also vary based on different time (as previously innocuous words may become slurs in time), as well as different use of the same word, different users, and different audience members [35]. One example of this is the difference in the use of slurs by in-group speakers, and out-group speakers [11].…”

Section: Challenges Of Detecting Hateful and Offensive Speechmentioning

confidence: 99%

Challenges of Hate Speech Detection in Social Media

2021

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The detection of hate speech in social media is a crucial task. The uncontrolled spread of hate has the potential to gravely damage our society, and severely harm marginalized people or groups. A major arena for spreading hate speech online is social media. This significantly contributes to the difficulty of automatic detection, as social media posts include paralinguistic signals (e.g. emoticons, and hashtags), and their linguistic content contains plenty of poorly written text. Another difficulty is presented by the context-dependent nature of the task, and the lack of consensus on what constitutes as hate speech, which makes the task difficult even for humans. This makes the task of creating large labeled corpora difficult, and resource consuming. The problem posed by ungrammatical text has been largely mitigated by the recent emergence of deep neural network (DNN) architectures that have the capacity to efficiently learn various features. For this reason, we proposed a deep natural language processing (NLP) model—combining convolutional and recurrent layers—for the automatic detection of hate speech in social media data. We have applied our model on the HASOC2019 corpus, and attained a macro F1 score of 0.63 in hate speech detection on the test set of HASOC. The capacity of DNNs for efficient learning, however, also means an increased risk of overfitting. Particularly, with limited training data available (as was the case for HASOC). For this reason, we investigated different methods for expanding resources used. We have explored various opportunities, such as leveraging unlabeled data, similarly labeled corpora, as well as the use of novel models. Our results showed that by doing so, it was possible to significantly increase the classification score attained.

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“…22 When one has 21 We might also interpret this situation as an instance of accommodation. The notion of accommodation has received a lot of attention in socio-political theorising (e.g., Langton and West 1999;Popa-Wyatt andWyatt 2017, Langton 2018). Roughly, accommodation occurs when a speaker says something that requires a hearer to accept that what was said is correct play.…”

Section: Metalinguistic Power M-peerhood and M-agencymentioning

confidence: 99%

Agency, Power, and Injustice in Metalinguistic Disagreement

Podosky

2021

The Philosophical Quarterly

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In this paper, I explain the kinematics of non-ideal metalinguistic disagreement. This occurs when one speaker has greater control in the joint activity of pairing contents with words in a context. I argue that some forms of non-ideal metalinguistic disagreement are deeply worrying, namely those that involves certain power imbalances. In such cases, a speaker possesses illegitimate control in metalinguistic disagreement owing to the operation of identity prejudice. I call this metalinguistic injustice. The wrong involves restricting a speaker from participating in the processes that determine the epistemic/linguistic resources of a conversation, and/or undermining a speaker's ability to affect metalinguistic outcomes.

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Slurs, roles and power

Cited by 54 publications

References 48 publications

The Identification of Slurs and Swear Words in Bronte Sisters’ Novels

The Identification of Slurs and Swear Words in Bronte Sisters’ Novels

Challenges of Hate Speech Detection in Social Media

Agency, Power, and Injustice in Metalinguistic Disagreement

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