Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media (2020.lrec-1)
Copied to clipboard
| 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. |
Similar Papers
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. |
Detecting context abusiveness using hierarchical deep learning (D19-50)
Copied to clipboard
| 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. |
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. |
Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | In this paper, we examine the role of conversational context in abusive language detection . prior studies have ignored the contextual nature of abusive language, ignoring this aspect . toxicity, hate speech, harmful stereotypes are among the forms of harmful language . |
| Approach: | They propose to use conversational context to analyze abusive language detection using two methods . they use "abusive language" as an umbrella term to refer to various forms of harmful language . |
| Outcome: | The proposed approach is based on two datasets in English and a new dataset of French tweets annotated for hate speech and stereotypes. |
Why Swear? Analyzing and Inferring the Intentions of Vulgar Expressions (D18-1)
Copied to clipboard
| Challenge: | Vulgar words are employed in language use for several different functions, including expressing aggression, signaling group identity or the informality of the communication. |
| Approach: | They present a dataset of 7,800 tweets with six categories of vulgarity in which all instances of vulgar words are annotated with one of the six categories. |
| Outcome: | The proposed model can predict the category of a vulgar word based on the immediate context it appears in with 67.4 macro F1 across six classes. |
Exploiting Emojis for Abusive Language Detection (2021.eacl-main)
Copied to clipboard
| Challenge: | emojis can be used as a proxy for learning a lexicon of abusive words . eliot safina and samuel khan are the authors of this paper . |
| Approach: | They propose to use abusive emojis as a proxy for learning a lexicon of abusive words. |
| Outcome: | The proposed approach generates a lexicon that performs as well as the most advanced lexical induction method. |
I Feel Offended, Don’t Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language (2020.lrec-1)
Copied to clipboard
| Challenge: | Recent literature suggests different approaches to identify abusive language phenomena . however, there is a lack of data sets that take into account the degree of explicitness . |
| Approach: | They propose to use annotation guidelines to distinguish between explicit and implicit abuse in English and apply them to OLID/OffensEval. |
| Outcome: | The proposed tool distinguishes between explicit and implicit abuse in English and takes into account the degree of explicitness. |
AbuseAnalyzer: Abuse Detection, Severity and Target Prediction for Gab Posts (2020.coling-main)
Copied to clipboard
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. |
Joint Modelling of Emotion and Abusive Language Detection (2020.acl-main)
Copied to clipboard
| 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. |
Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language (2023.emnlp-main)
Copied to clipboard
| Challenge: | Currently, only explicit abuse can be reliably detected due to the increasing amount of abusive language on the Web. |
| Approach: | They propose a crowdsourced dataset that can detect euphemistic abuse by paraphrasing simple explicit utterances. |
| Outcome: | The proposed classifier augments training data with automatically-generated GPT-3 completions. |