#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media (P19-1)
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| Challenge: | a recent study has found that the disclosure of sexual abuse has positive psychological im- pacts. |
| Approach: | They propose to aggregate personal experiences of sexual harassment from Twitter posts to facilitate a better understanding of social media constructs and bring about social change. |
| Outcome: | The proposed model is compared with state-of-the-art models and is based on a three part Twitter-Specific Social Media Language Model. |
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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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Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization (D19-1)
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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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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. |
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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. |
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Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts (D19-1)
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| Challenge: | Existing tools for hate speech detection and sentiment analysis cannot detect veiled offensiveness of microaggressions . linguistic subtlety of micro-aggressives has made it difficult to analyze their exact nature . |
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He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist (2020.acl-main)
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Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi, Marlène Coulomb-Gully
| Challenge: | Sexism is prejudice or discrimination based on a person's gender. |
| Approach: | They propose to use a French dataset annotated for sexism detection to characterize sexist content and to train deep learning experiments on tweets. |
| Outcome: | The proposed dataset is the first to be used for sexism detection in France and constitutes a first step towards offensive content moderation. |
#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. |
| Approach: | They propose a transfer-learning based method to identify victim blaming language on Twitter and a single step transfer-based classification method to classify it. |
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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. |
| Approach: | They propose to identify narratives related to sexual abuse disclosures as a joint modeling task that leverages their emotional attributes through multitask learning. |
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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. |
| Approach: | They argue that the NLP community needs to make three substantive changes to tackle both more subtle and more serious forms of abuse. |
| Outcome: | The proposed approach would address the problem of abuse in a more inclusive and productive way. |
It’s going to be okay: Measuring Access to Support in Online Communities (D18-1)
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| Challenge: | Despite substantial efforts to reduce gender disparities in online social contexts, gender gaps persist and negatively affect women through online harassment. |
| Approach: | They propose a new dataset and method for identifying supportive replies and new methods for inferring gender from text and name to examine the disparity in support across millions of online interactions. |
| Outcome: | The proposed model shows that identifying as a woman is associated with higher rates of support, but also higher rates disparagement. |