Hate speech harms: a social justice discussion of disabled Norwegians’ experiences

Vedeler, Janikke Solstad; Olsen, Terje; Eriksen, John

doi:10.1080/09687599.2018.1515723

Cited by 20 publications

(13 citation statements)

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“…Nejedná se o výběr reprezentativní; pokrytí všech relevantních témat přesahuje rozsah věnovaný časopisecké studii, a tak např. důvody k oznamování viktimizace [Alhaboby et al 2021;Sin 2013] či dopady násilí na napadené [Burch 2021;Vedeler, Olsen, Eriksen 2019;Williams, Tregidga 2014] zůstaly stranou.…”

Section: Závěrunclassified

“…Převážná část výzkumu předsudečného násilí pro postižení je realizována ve Velké Británii, Spojených státech amerických a Austrálii. Mimo anglosaské země je tento fenomén zkoumán jen výjimečně, přičemž se orientuje zejména na zjišťování prevalence viktimizace a reflexi právní úpravy[Vedeler, Olsen, Eriksen 2019;Holness 2021;Poposka 2017;Šumskienė 2017]. Zajímavým příspěvkem je interpretování institucionalizace lidí s disabilitou v socialistickém Polsku jako hate speech[Borowska-Beszta 2020].…”

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Violence, Bias and People with Disabilities: An Introduction to Disablist Hate Crime Research

Walach¹

2024

Czech Sociological Review

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Violence against disabled people has recently been recognised as an important, albeit neglected research topic. Drawing attention to violence motivated by anti-disability bias, this article offers an introduction to research on disablist hate crime. First, it presents a conceptualisation of hate crime and explains such concepts as disablism and ableism. Second, it discusses the concept of disability/disablist hate crime and analyses four areas that reflect the multifaceted nature of disability hate crime research and the social applications of the concept, ranging from conceptual issues to empirical findings, to political implications. These issues are:(1) vulnerability (a strongly criticised belief that people with disabilities are susceptible to victimisation because of 'their' disabilities); ( 2) invisibility (the under-representation of disablist violence in police statistics); (3) risk (individual and social factors that produce situational vulnerabilities to disablist hate crime); and (4) intervention (measures suggested to prevent disablist hate crime, including the hate crime legislation). Possible future directions of research are outlined in the conclusion.

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Violence, Bias and People with Disabilities: An Introduction to Disablist Hate Crime Research

Walach¹

2024

Czech Sociological Review

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“…During planning for HASOC 2020, the organizers searched for tweets collections and iden-tified archive.org 1 . We have downloaded the entire archive for month May 2019.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The large quantity of posts on social media has led to a growth in problematic content. Such content is often considered harmful for a rationale and constructive debate [1]. Many countries have defined more and more detailed rules for dealing with offensive posts [2,3].…”

Section: Introduction: Hate Speech and Its Identificationmentioning

confidence: 99%

Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German

Mandl

Modha²,

et al. 2020

Forum for Information Retrieval Evaluation

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With the growth of social media, the spread of hate speech is also increasing rapidly. Social media are widely used in many countries. Also Hate Speech is spreading in these countries. This brings a need for multilingual Hate Speech detection algorithms. Much research in this area is dedicated to English at the moment. The HASOC track intends to provide a platform to develop and optimize Hate Speech detection algorithms for Hindi, German and English. The dataset is collected from a Twitter archive and pre-classified by a machine learning system. HASOC has two sub-task for all three languages: task A is a binary classification problem (Hate and Not Offensive) while task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY. Overall, 252 runs were submitted by 40 teams. The performance of the best classification algorithms for task A are F1 measures of 0.51, 0.53 and 0.52 for English, Hindi, and German, respectively. For task B, the best classification algorithms achieved F1 measures of 0.26, 0.33 and 0.29 for English, Hindi, and German, respectively. This article presents the tasks and the data development as well as the results. The best performing algorithms were mainly variants of the transformer architecture BERT. However, also other systems were applied with good success.

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“…In some cases, it has been reported that online incidents have already turned into crimes against minorities [1] with some of them leading to severe incidents such as the genocide of the Rohingya community in Myanmar 2 , the anti-Muslim mob violence in Sri Lanka 3 , and the Pittsburg shooting 4 . Targeted community members may further feel emotional and psycho-logical problems [2]. To mitigate the detrimental effectiveness of such posts, the social media platforms, such as Twitter 5 and Facebook 6 have laid down moderation policies and employ moderators [3] for maintaining civility in their platforms.…”

Section: Introductionmentioning

confidence: 99%

Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach

Das¹,

Banerjee²,

Saha³

2021

Preprint

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Online hatred is a growing concern on many social media platforms. To address this issue, different social media platforms have introduced moderation policies for such content. They also employ moderators who can check the posts violating moderation policies and take appropriate action. Academicians in the abusive language research domain also perform various studies to detect such content better. Although there is extensive research in abusive language detection in English, there is a lacuna in abusive language detection in low resource languages like Hindi, Urdu etc. In this FIRE 2021 shared task -"HASOC -Abusive and Threatening language detection in Urdu" the organisers propose an abusive language detection dataset in Urdu along with threatening language detection.In this paper, we explored several machine learning models such as XGboost, LGBM, m-BERT based models for abusive and threatening content detection in Urdu based on the shared task. We observed the Transformer model specifically trained on abusive language dataset in Arabic helps in getting the best performance. Our model came First for both abusive and threatening content detection with an F1score of 0.88 and 0.54, respectively. We have made our code public 1 .

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Hate speech harms: a social justice discussion of disabled Norwegians’ experiences

Cited by 20 publications

References 7 publications

Violence, Bias and People with Disabilities: An Introduction to Disablist Hate Crime Research

Violence, Bias and People with Disabilities: An Introduction to Disablist Hate Crime Research

Overview of the HASOC Track at FIRE 2020: Hate Speech and Offensive Language Identification in Tamil, Malayalam, Hindi, English and German

Abusive and Threatening Language Detection in Urdu using Boosting based and BERT based models: A Comparative Approach

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