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 . |
| Outcome: | The proposed model outperforms existing models that rely on handcrafted stylistic features and is more accurate than generic models. |
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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. |
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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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#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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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. |
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ModelCitizens: Representing Community Voices in Online Safety (2025.emnlp-main)
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Ashima Suvarna, Christina A Chance, Karolina Naranjo, Hamid Palangi, Sophie Hao, Thomas Hartvigsen, Saadia Gabriel
| Challenge: | Existing toxic language detection models are trained on annotations that collapse diverse perspectives into a single ground truth. |
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A Semantics-based Approach to Disclosure Classification in User-Generated Online Content (2020.findings-emnlp)
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| Challenge: | Existing algorithms for self-disclosure identification and classification are challenging due to the relative anonymity of social networking sites and lack of non-verbal cues to signal thoughts or feelings. |
| Approach: | They propose an approach to detect emotional and informational self-disclosure in natural language by using frame semantics to identify lexical units and their semantic roles. |
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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. |
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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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Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms (2025.acl-long)
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| Challenge: | Social media platforms use machine learning and artificial intelligence to maximize user engagement, but can indirectly cause exposure to harmful content. |
| Approach: | They propose a re-ranking approach using Large Language Models to assess and rerank content sequences using large annotated data sets. |
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Intersectional Stereotypes in Large Language Models: Dataset and Analysis (2023.findings-emnlp)
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| Challenge: | Existing studies on intersectional stereotypes focus on broader, individual categories . current studies focus on single-group stereotypes, such as racial bias against African Americans . |
| Approach: | They propose to use a dataset of intersectional stereotypes curated with the ChatGPT model to analyze propagation in three contemporary LLMs. |
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