SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories (D18-1)
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| Challenge: | With the recent rise of #MeToo, an increasing number of personal stories about sexual harassment and sexual abuse have been shared online. |
| Approach: | They propose to use CNN-RNN model to automatically categorize and analyze sexual harassment data from SafeCity forums. |
| Outcome: | The proposed model achieves an accuracy of 86.5% for groping, ogling, and commenting, and 82.5% in multi-label models. |
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| Challenge: | Sexual harassment is a pervasive, worldwide problem with a long history . statistics show that girls and women are put at high risk of experiencing harassment. |
| Approach: | They manually annotated sexual harassment stories with labels in dimensions of location, time, and harassers’ characteristics and applied natural language processing techniques to extract key elements at the same time. |
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| Challenge: | a recent study has found that the disclosure of sexual abuse has positive psychological im- pacts. |
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Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment (N19-3)
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| Challenge: | #MeToo movement provides platform to narrate personal experiences of sexual harassment. |
| Approach: | They propose a three-part ULMFiT architecture to tackle text subtleties in a classification task . they propose to annotate a manually annotated real-world dataset to test their approach . |
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Introducing CAD: the Contextual Abuse Dataset (2021.naacl-main)
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| Challenge: | Detecting and classifying online abuse is a complex and nuanced task, despite many advances in the power and availability of computational tools. |
| Approach: | They propose to annotate a reddit conversation thread with six distinct primary and secondary categories and an expert-driven group-adjudication process for high quality annotations. |
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Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures (2021.naacl-main)
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| Challenge: | Prior work on identifying narratives related to sexual abuse disclosures did not consider this as an independent task. |
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#NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media (2020.acl-srw)
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| Challenge: | Recent surge in online forums and movements supporting sexual assault survivors has led to the emergence of a ‘virtual bubble’ where survivors can recount their stories. |
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A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (P19-1)
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| Challenge: | Current methods to detect online abuse focus on a narrow definition of abuse to detriment of victims seeking validation and solutions. |
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SafeConv: Explaining and Correcting Conversational Unsafe Behavior (2023.acl-long)
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| Challenge: | Existing datasets do not provide enough annotation to explain unsafe behavior . current chatbots generate toxic and offensive responses, which can be dangerous . |
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An Expert Annotated Dataset for the Detection of Online Misogyny (2021.eacl-main)
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| Challenge: | Existing studies have found that misogynistic content is pervasive on some Reddit communities, but a training dataset for misogorical classification has not been created with the data. |
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Author Profiling for Abuse Detection (C18-1)
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| Challenge: | Existing methods for detecting abusive content rely on textual cues and lexical cue information. |
| Approach: | They propose a method that incorporates community-based profiling features of Twitter users to detect abusive content by using a dataset of 16k tweets. |
| Outcome: | The proposed approach outperforms the current state-of-the-art in abuse detection on a dataset of 16k tweets. |