| 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. |
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| Challenge: | Abuse on the Internet is an important societal problem of our time. |
| Approach: | They propose to use user and community information to enhance detection of abusive language . they propose to propose properties that an explainable method should aim to exhibit . |
| Outcome: | The proposed methods leverage user and community information to enhance detection of abusive language. |
Abusive Language Detection with Graph Convolutional Networks (N19-1)
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| Challenge: | Existing approaches to abusive language detection only capture shallow properties of online communities . a new approach captures both the structure of online community and linguistic behavior of users . |
| Approach: | They propose a graph convolutional network approach that captures the linguistic behavior of users . they propose to model homophily by embeddings for authors that encode the structure of their communities . |
| Outcome: | The proposed approach captures both the structure and linguistic behavior of users in online communities . authors show that the proposed approach significantly advances the current state of the art . |
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. |
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts (2020.coling-main)
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Mohit Chandra, Ashwin Pathak, Eesha Dutta, Paryul Jain, Manish Gupta, Manish Shrivastava, Ponnurangam Kumaraguru
| Challenge: | Existing studies on estimating the severity of abuse and the target of online abuse have focused on detecting and curtailment of such types of abuse. |
| Approach: | They propose to analyze online abuse from the perspective of presence, severity and target of abusive behavior from 7,601 posts from Gab and to estimate the severity of abuse. |
| Outcome: | The proposed system achieves 80% accuracy for abuse presence, 82% accuracy for abusive target prediction, and 65% accuracy for severity prediction. |
Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media (2020.lrec-1)
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| Challenge: | Swearing is a common form of verbal communication and occurs in social media and online forums . a study by a team of researchers has investigated the phenomenon of swearing in Twitter . |
| Approach: | They analyze tweets to determine abusive swearing using models that automatically predict it . they also investigate lexical, syntactic, and affective features that are more informative . |
| Outcome: | The proposed model can predict abusive swearing in a tweet context and provide an intrinsic evaluation of the model. |
Joint Modelling of Emotion and Abusive Language Detection (2020.acl-main)
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| Challenge: | Existing methods for abuse detection focus on linguistic properties of comments and online communities of users, disregarding the emotional state of the users and how this might affect their language. |
| Approach: | They propose to combine emotion and abusive language detection to create a multi-task learning framework that allows one task to inform the other. |
| Outcome: | The proposed model improves on the previous models, incorporating affective features into the learning framework. |
On the Effectiveness of Adversarial Robustness for Abuse Mitigation with Counterspeech (2024.naacl-long)
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| Challenge: | Recent work on automated counterspeech systems focused on synthetic data but rarely looked into how the public deals with abuse. |
| Approach: | They propose to curate a new dataset of abuse and replies from footballers for study of public figure abuse and use it to examine how models can handle adversarial attacks. |
| Outcome: | The proposed model is robust against adversarial attacks across domains and can handle abuse in the real world. |
Detecting context abusiveness using hierarchical deep learning (D19-50)
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| Challenge: | Abusive text is a serious problem in social media and causes many issues among users . a model that detects text abusiveness in context without explicit abusive words is challenging . |
| Approach: | They propose to use an abusive lexicon to determine the existence of an abusive word in text . they combine local and global features to evaluate the model using benchmark data . |
| Outcome: | The proposed model outperforms all previous models for detecting abusiveness in text without abusive words. |
Cross-domain and Cross-lingual Abusive Language Detection: A Hybrid Approach with Deep Learning and a Multilingual Lexicon (P19-2)
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| 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. |
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition (D19-50)
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| Challenge: | Existing methods to detect abusive language only train one classifier for the whole variety of offending . a new method can support a moderator with explicit unraveled explanations for why something was flagged as abusive . |
| Approach: | a new method is proposed to distinguish explicitly abusive cases from the more "shadowed" ones . the researchers extend a lexicon of abusive terms to include new obfuscations of abusive words . |
| Outcome: | a new method can distinguish explicitly abusive cases from the more "shadowed" ones . the method can support a moderator with explicit unraveled explanations for why something was flagged as abusive . |