An Analysis of Domestic Abuse Discourse on Reddit

Schrading, Nicolas; Alm, Cecilia Ovesdotter; Ptucha, Ray; Homan, Christopher M.

doi:10.18653/v1/d15-1309

Cited by 77 publications

(65 citation statements)

References 13 publications

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“…Analyzing personal sexual harassment stories from online social forums is fairly unexplored, to the best of our knowledge. However, recent works in a similar vein include detecting the presence of domestic abuse stories on social media sites (Schrading et al, 2015a;Schrading, 2015;Schrading et al, 2015b). In more distantly related work, NLP has been used for various sociallydriven tasks, such as detecting the presence of cyberbullying or incivility (Ziegele et al, 2018;Founta et al, 2018;Chen et al, 2012;Zhao et al, 2016;Agrawal and Awekar, 2018;Van Hee et al, 2018), and detecting and providing aid for signs of depression or suicidal thoughts (Pestian et al, 2010;Yazdavar et al, 2017;Stepanov et al, 2017;Fitzpatrick et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories

Karlekar¹,

Bansal²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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With the recent rise of #MeToo, an increasing number of personal stories about sexual harassment and sexual abuse have been shared online. In order to push forward the fight against such harassment and abuse, we present the task of automatically categorizing and analyzing various forms of sexual harassment, based on stories shared on the online forum SafeCity. For the labels of groping, ogling, and commenting, our single-label CNN-RNN model achieves an accuracy of 86.5%, and our multi-label model achieves a Hamming score of 82.5%. Furthermore, we present analysis using LIME, first-derivative saliency heatmaps, activation clustering, and embedding visualization to interpret neural model predictions and demonstrate how this extracts features that can help automatically fill out incident reports, identify unsafe areas, avoid unsafe practices, and 'pin the creeps'.

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Section: Related Workmentioning

confidence: 99%

SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories

Karlekar¹,

Bansal²

2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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“…De Choudhury and De (2014) studied mental health discourse and concluded that Reddit users openly share their experiences and challenges with mental illnesses in their personal and professional lives. Schrading et al (2015) studied domestic abuse and found that abuse-related discussion groups have more tightknit communities, longer posts and comments, and less discourse than non-abusive groups. Wallace et al (2014) tackled irony detection and concluded that Reddit provides a lot of context, which can help in dealing with the ambiguous cases.…”

Section: Background and Related Workmentioning

confidence: 99%

Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

2018

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The idea of organizing PEOPLES stemmed from two related observations, namely the availability of large amounts of spontaneous data covering a range of personal aspects and the fact that such aspects are usually studied in isolation. Social media users nowadays freely express what is on their mind at any moment in time, at any location, and about virtually anything. These large amounts of spontaneously produced texts open up a unique opportunity to learn more about such users, e.g., predicting demographic variables (age, gender), but also personality types, as well as emotions and opinion expressions. This observation is not new, of course, and this opportunity has largely been exploited in the recent years, with abundant works on sentiment analysis, emotion detection, and personality. However, such traits of human personality and behavior have indeed attracted a substantial amount of attention but have been mostly studied in isolation, often in different -but related -communities, such as NLP, CL, AI. Therefore, we thought that the time was ripe to bring these communities a step closer to study people's traits and expressions jointly and in their interplay on such large volumes of available data.The communities' response, with 25 received submissions coming from 11 different countries and going well beyond typical NLP topics, proves again this year that there is wide interest at this intersection, and we are happy to be able to provide a context for exchanging ideas.Following the reviewers's advice, 14 papers were selected for inclusion in the proceedings. They cover a wide range of topics related to the three main PEOPLES themes (personality, emotion and opinion), their interaction and the impact of their modeling on social aspects like well-being, political preferences, humor and language use.To further enrich this volume, we additionally invited our keynote speakers to submit position papers that accompany their talks, and are excited that both of our keynotes submitted excellent papers touching upon issues of making NLP models more demographically aware and how researchers from related fields such as demography can benefit from NLP techniques.We hope that this is just the second edition of what will become series of workshops bringing together researchers in Computational Linguistics, Natural Language Processing and Computational Social Science, who share an interest in personality, opinion and emotion detection, and especially in researching the intertwining of such traits and expressions.We would like to thank our program committee consisting of 33 researchers from a variety of backgrounds for their insightful and constructive reviews. Without their support, this workshop would not have been possible. In addition, we thank all authors for submitting papers and making PEOPLES a big success. Also thanks to our two invited speakers, Dirk Hovy and Letizia Mencarini (Bocconi University, Italy), for having accepted to come to the workshop and share their expertise and ideas on PEOPLES' topics. We thank NAACL for...

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“…It divides violence into physical, sexual, psychological, and deprivational/neglect categories. The Duluth model provides another established categorization of types of violence, but was originally developed for therapy treating men who abuse women, rather than for understanding IPV scientifically (Rizza, 2009 Schrading et al (2015a) developed classifiers to determine whether a Reddit post described abuse. In the study, the subreddit to which the post belonged was used to map to binary gold-standard labels: if a post came from a subreddit such as /r/survivorsofabuse, it was categorized as a post about abuse.…”

Section: Background and Related Workmentioning

confidence: 99%

Understanding the Semantics of Narratives of Interpersonal Violence through Reader Annotations and Physiological Reactions

Calderwood¹,

Pruett²,

Ptucha³

et al. 2017

Proceedings of the Workshop Computational Semantics Beyond Events and Roles

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Interpersonal violence (IPV) is a prominent sociological problem that affects people of all demographic backgrounds. By analyzing how readers interpret, perceive, and react to experiences narrated in social media posts, we explore an understudied source for discourse about abuse. We asked readers to annotate Reddit posts about relationships with vs. without IPV for stakeholder roles and emotion, while measuring their galvanic skin response (GSR), pulse, and facial expression. We map annotations to coreference resolution output to obtain a labeled coreference chain for stakeholders in texts, and apply automated semantic role labeling for analyzing IPV discourse. Findings provide insights into how readers process roles and emotion in narratives. For example, abusers tend to be linked with violent actions and certain affect states. We train classifiers to predict stakeholder categories of coreference chains. We also find that subjects' GSR noticeably changed for IPV texts, suggesting that co-collected measurement-based data about annotators can be used to support text annotation.

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An Analysis of Domestic Abuse Discourse on Reddit

Cited by 77 publications

References 13 publications

SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories

SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories

Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

Understanding the Semantics of Narratives of Interpersonal Violence through Reader Annotations and Physiological Reactions

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