When does a compliment become sexist? Analysis and classification
            of ambivalent sexism using twitter data

Jha, Akshita; Mamidi, Radhika

doi:10.18653/v1/w17-2902

Cited by 117 publications

(108 citation statements)

References 37 publications

(20 reference statements)

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“…2 The Attributive theme covers instances where a microaggression attributes a stereotype to an individual based on their identity. These stereotypes may have inherently negative connotations ("lazy"), but may also be neutral ("liking pink") or positive ("strong"), which complements recent work on benevolent sexism (Jha and Mamidi, 2017). The Institutionalized theme reflects larger institutionalized biases, such as in employment or law enforcement.…”

Section: A New Typology Of Microaggressionsmentioning

confidence: 78%

Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts

Breitfeller¹,

Ahn²,

Jurgens

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

103

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Microaggressions are subtle, often veiled, manifestations of human biases. These uncivil interactions can have a powerful negative impact on people by marginalizing minorities and disadvantaged groups. The linguistic subtlety of microaggressions in communication has made it difficult for researchers to analyze their exact nature, and to quantify and extract microaggressions automatically. Specifically, the lack of a corpus of real-world microaggressions and well-defined criteria for annotating them have prevented researchers from addressing these problems at scale. In this paper, we devise a general but nuanced, computationally operationalizable typology of microaggressions based on a small subset of microaggression data that we have. We then create two datasets: one with examples of diverse types of microaggressions recollected by their targets, and another with gender-based microaggressions in public conversations on social media. We introduce a new, more objective criterion for annotation and an activelearning based procedure that increases the likelihood of surfacing posts containing microaggressions. Finally, we analyze the trends that emerge from these new datasets.

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Section: A New Typology Of Microaggressionsmentioning

confidence: 78%

Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts

Breitfeller¹,

Ahn²,

Jurgens

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

103

View full text Add to dashboard Cite

show abstract

“…Also it includes any form of misogyny. Moreover, Jha and Mamidi [9] clarify that sexism may come in two forms: Hostile (which is an explicit negative attitude) and Benevolent (which is more subtle).…”

Section: Gendered Hate Speechmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

114

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In social media platforms, hate speech can be a reason of "cyber conflict" which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented.

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“…First, although recognized within the larger NLP abuse typology (Waseem et al, 2017), only a handful of approaches have attempted these prob-lems, such as identifying benevolent sexism (Jha and Mamidi, 2017), and new methods must be developed to identify the implicit signals. Successful approaches will likely require advances in natural language understanding, as the abuse requires reasoning about the implications of the propositions.…”

Section: Subtle Abusementioning

confidence: 99%

A Just and Comprehensive Strategy for Using NLP to Address Online Abuse

Jurgens¹,

Hemphill²,

Chandrasekharan³

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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Online abusive behavior affects millions and the NLP community has attempted to mitigate this problem by developing technologies to detect abuse. However, current methods have largely focused on a narrow definition of abuse to detriment of victims who seek both validation and solutions. In this position paper, we argue that the community needs to make three substantive changes: (1) expanding our scope of problems to tackle both more subtle and more serious forms of abuse, (2) developing proactive technologies that counter or inhibit abuse before it harms, and (3) reframing our effort within a framework of justice to promote healthy communities.

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When does a compliment become sexist? Analysis and classification of ambivalent sexism using twitter data

Cited by 117 publications

References 37 publications

Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts

Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

A Just and Comprehensive Strategy for Using NLP to Address Online Abuse

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