Aggression Detection on Social Media Text Using Deep Neural Networks

Singh, Vinay; Varshney, Aman; Akhtar, Syed Sarfaraz; Vijay, Deepanshu; Shrivastava, Manish

doi:10.18653/v1/w18-5106

Cited by 32 publications

(19 citation statements)

References 24 publications

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“…The identification of community specific keywords or the identification of hateful speech is an essential part of the pipeline for any kind of analysis on the effect of interventions on online speech. Just as there are numerous methods for the identification of hateful speech (de Gibert et al, 2018a;Park and Fung, 2017;Singh et al, 2018;Lee et al, 2018), there are numerous related methods for the identification of community-specific keywords.…”

Section: Appendixmentioning

confidence: 99%

“…Online spaces often contain toxic behaviors such as abuse or harmful speech (Blackwell et al, 2017;Saleem et al, 2017;Jhaver et al, 2018;Saleem and Ruths, 2018;Habib et al, 2019;Ribeiro et al, 2020a;de Gibert et al, 2018a;Sprugnoli et al, 2018;Park and Fung, 2017;Singh et al, 2018;Lee et al, 2018). Such toxicity may result in platform-wide decreases in user participation and engagement which, combined with external pressure (e.g., bad press), may motivate platform managers to moderate harmful behavior (Saleem and Ruths, 2018;Habib et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban

Trujillo¹,

Rosenblatt²,

Anda-Jáuregui³

et al. 2021

Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

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Community-level bans are a common tool against groups that enable online harassment and harmful speech. Unfortunately, the efficacy of community bans has only been partially studied and with mixed results. Here, we provide a flexible unsupervised methodology to identify in-group language and track user activity on Reddit both before and after the ban of a community (subreddit). We use a simple word frequency divergence to identify uncommon words overrepresented in a given community, not as a proxy for harmful speech but as a linguistic signature of the community. We apply our method to 15 banned subreddits, and find that community response is heterogeneous between subreddits and between users of a subreddit. Top users were more likely to become less active overall, while random users often reduced use of in-group language without decreasing activity. Finally, we find some evidence that the effectiveness of bans aligns with the content of a community. Users of dark humor communities were largely unaffected by bans while users of communities organized around white supremacy and fascism were the most affected. Altogether, our results show that bans do not affect all groups or users equally, and pave the way to understanding the effect of bans across communities.

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Section: Appendixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban

Trujillo¹,

Rosenblatt²,

Anda-Jáuregui³

et al. 2021

Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

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show abstract

“…Increasing the number features increases the difficulties in the process of feature extraction and selection. No current research uses a word's meaning or semantics for cyberbullying detection [52][53][54]. Transformers, mentioned in a few papers stated above, often require high computing resources while training, especially at regular intervals.…”

Section: Related Research Workmentioning

confidence: 99%

MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content

et al. 2021

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Cyberbullying is a growing and significant problem in today’s workplace. Existing automated cyberbullying detection solutions rely on machine learning and deep learning techniques. It is proven that the deep learning-based approaches produce better accuracy for text-based classification than other existing approaches. A novel decentralized deep learning approach called MaLang is developed to detect abusive textual content. MaLang is deployed at two levels in a network: (1) the System Level and (2) the Cloud Level, to tackle the usage of toxic or abusive content on any messaging application within a company’s networks. The system-level module consists of a simple deep learning model called CASE that reads the user’s messaging data and classifies them into abusive and non-abusive categories, without sending any raw or readable data to the cloud. Identified abusive messages are sent to the cloud module with a unique identifier to keep user profiles hidden. The cloud module, called KIPP, utilizes deep learning to determine the probability of a message containing different categories of toxic content, such as: ‘Toxic’, ‘Insult’, ‘Threat’, or ‘Hate Speech’. MaLang achieves a 98.2% classification accuracy that outperforms other current cyberbullying detection systems.

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“…The identification of community specific keywords or the identification of hateful speech is an essential part of the pipeline for any kind of analysis on the effect of interventions on online speech. Just as there are numerous methods for the identification of hateful speech (de Gibert et al, 2018a;Singh et al, 2018;, there are numerous related methods for the identification of community-specific keywords.…”

Section: Context Sensitivity Estimation In Toxicity Detectionmentioning

confidence: 99%

“…Online spaces often contain toxic behaviors such as abuse or harmful speech (Blackwell et al, 2017;Saleem et al, 2017;Jhaver et al, 2018;Saleem and Ruths, 2018;Habib et al, 2019;Ribeiro et al, 2020a;de Gibert et al, 2018a;Singh et al, 2018;. Such toxicity may result in platform-wide decreases in user participation and engagement which, combined with external pressure (e.g., bad press), may motivate platform managers to moderate harmful behavior (Saleem and Ruths, 2018;Habib et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

2021

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Message from the OrganisersDigital technologies have brought myriad benefits for society, transforming how people connect, communicate and interact with each other. However, they have also enabled harmful and abusive behaviours to reach large audiences and for their negative effects to be amplified, including interpersonal aggression, bullying and hate speech. Already marginalised and vulnerable communities are often disproportionately at risk of receiving such abuse, compounding other social inequalities and injustices. The Workshop on Online Abuse and Harms (WOAH) convenes research into these issues, particularly work that develops, interrogates and applies computational methods for detecting, classifying and modelling online abuse.Technical disciplines such as machine learning and natural language processing (NLP) have made substantial advances in creating more powerful technologies to stop online abuse. Yet a growing body of work shows the limitations of many automated detection systems for tackling abusive online content, which can be biased, brittle, low performing and simplistic. These issues are magnified by the lack of explainability and transparency. And although WOAH is collocated with ACL and many of our papers are rooted firmly in the field of machine learning, these are not purely engineering challenges, but raise fundamental social questions of fairness and harm. For this reason, we continue to emphasise the need for inter-, cross-and anti-disciplinary work by inviting contributions from a range of fields, including but not limited to: NLP, machine learning, computational social sciences, law, politics, psychology, network analysis, sociology and cultural studies. In this fifth edition of WOAH we direct the conversation at the workshop through our theme: Social Bias and Unfairness in Online Abuse Detection Systems. Continuing the tradition started in WOAH 4, we have invited civil society, in particular individuals and organisations working with women and marginalised communities, to submit reports, case studies, findings, data, and to record their lived experiences through our civil society track. Our hope is that WOAH provides a platform to facilitate the interdisciplinary conversations and collaborations that are needed to effectively and ethically address online abuse.Speaking to the complex nature of the issue of online abuse, we are pleased to invite Leon Derczynski, currently an Associate Professor at ITU Copenhagen who works on a range of topics in Natural Language Processing; Deb Raji, currently a Research Fellow at Mozilla who researches AI accountability and auditing; Murali Shanmugavelan, currently a researcher at the Centre for Global Media and Communications at SOAS (London) to deliver keynotes. We are grateful to all our speakers for being available, and look forward to the dialogues that they will generate. On the day of WOAH the invited keynote speakers will give talks and then take part in a multi-disciplinary panel discussion to debate our theme and other issues in computational o...

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Aggression Detection on Social Media Text Using Deep Neural Networks

Cited by 32 publications

References 24 publications

When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban

When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban

MaLang: A Decentralized Deep Learning Approach for Detecting Abusive Textual Content

Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

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