#NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media (2020.acl-srw)
Copied to clipboard
| 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. |
| Outcome: | The proposed method is compared with various deep learning and machine learning models on a manually annotated domain-specific dataset. |
Similar Papers
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment (N19-3)
Copied to clipboard
| 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. |
#YouToo? Detection of Personal Recollections of Sexual Harassment on Social Media (P19-1)
Copied to clipboard
| 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. |
Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures (2021.naacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed model leverages emotional attributes of textual conversations to identify narratives related to sexual abuse disclosures in homogeneous and heterogeneously settings. |
Author Profiling for Abuse Detection (C18-1)
Copied to clipboard
| 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. |
Cross-domain and Cross-lingual Abusive Language Detection: A Hybrid Approach with Deep Learning and a Multilingual Lexicon (P19-2)
Copied to clipboard
| Challenge: | Detecting online abusive language in social media messages is gaining increasing attention from scholars and stakeholders. |
| Approach: | They propose a hybrid approach with deep learning and a multilingual lexicon to cross-domain and cross-lingual detection of abusive content. |
| Outcome: | The proposed system can detect abusive content across domains and languages using a multilingual lexicon and a domain-independent lexical. |
Introducing CAD: the Contextual Abuse Dataset (2021.naacl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed dataset contains six distinct primary and secondary categories and uses an expert-driven group-adjudication process for high quality annotations. |
How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing datasets for abusive language detection are expensive and lack of knowledge about the target is a challenge. |
| Approach: | They propose to build models cheaply for a new target label set and/or language, using only a few training examples of the target domain. |
| Outcome: | The proposed model improves monolingually and across languages using existing datasets and only a few-shots of the target domain. |
CoRAL: a Context-aware Croatian Abusive Language Dataset (2022.findings-aacl)
Copied to clipboard
| Challenge: | Semi-automated comment moderation systems can greatly aid human moderators by either automatically classifying the examples or allowing the moderator to prioritize which comments to consider first. |
| Approach: | They propose to use a language and culturally aware Croatian Abusive dataset to analyze inappropriate comments in a context-based manner. |
| Outcome: | The proposed dataset shows that current models degrade when comments are not explicit and further degrades when language skill and context knowledge are required to interpret the comment. |
The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Psychological trauma can manifest following various distressing events, but studies focus on a single aspect of trauma, often neglecting the transferability of findings across different scenarios. |
| Approach: | They propose a language model that fine-tunes a single aspect of trauma to better predict traumatic events across domains. |
| Outcome: | The proposed model outperforms large language models on trauma-related datasets . it also outperformed models on court data, counseling conversations, and forum posts . |
A Computational Exploration of Pejorative Language in Social Media (2021.findings-emnlp)
Copied to clipboard
| Challenge: | In this paper, we examine the problem of pejorative language, an under-explored topic in computational linguistics. |
| Approach: | They propose to automatically disambiguate pejorative usage in social media . they leverage online dictionaries to build a multilingual lexicon of pejorativ terms . |
| Outcome: | The proposed model can automatically disambiguate pejorative usage in social media posts . the proposed model is based on dictionaries and tweets . |