| Challenge: | Prior work focused on detecting specific types of offensive content, such as hate speech, cyberbullying, or cyber-aggression. |
| Approach: | They propose to use a dataset to identify offensive content in social media . they compare the performance of different machine learning models to OLID . |
| Outcome: | The proposed dataset contains tweets annotated for offensive content using a fine-grained three-layer annotation scheme. |
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SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification (2021.findings-acl)
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| Challenge: | toxicity, hate speech, cyberbullying, and cyber-aggression are common themes in social media . authors present a dataset that is limited in size and biased towards offensive language . |
| Approach: | They present an expanded dataset that uses a taxonomy for offensive language identification . they show that using SOLID and OLID yields sizable performance gains . |
| Outcome: | The proposed dataset shows that it performs better than the OLID dataset for two different models. |
Offensive Language Identification in Greek (2020.lrec-1)
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| Challenge: | a gap in the literature on offensive language has been addressed with studies on Spanish, Hindi, and German. |
| Approach: | They present a Greek annotated dataset for offensive language identification . it contains 4,779 tweets annotating offensive and not offensive posts from Twitter . they evaluate several computational models trained and tested on the dataset . |
| Outcome: | The proposed dataset contains 4,779 tweets annotated as offensive and not offensive . the authors show that the proposed dataset is similar to the OLID dataset for English . |
A Dataset of Offensive Language in Kosovo Social Media (2022.lrec-1)
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| Challenge: | Social media are a central part of people’s lives but are rife with bullying and offensive language, creating an unsafe environment for their users. |
| Approach: | They propose to use user-generated comments on Facebook and YouTube from selected Kosovo news platforms to annotate offensive language in Albanian. |
| Outcome: | The proposed system improves on Danish but not Albanian, on offensive language recognition and distinguishing targeted and untargeted offence. |
Target-Based Offensive Language Identification (2023.acl-short)
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Marcos Zampieri, Skye Morgan, Kai North, Tharindu Ranasinghe, Austin Simmmons, Paridhi Khandelwal, Sara Rosenthal, Preslav Nakov
| Challenge: | Popular social media annotation taxonomies focus on the post level and token-level annotations are not available. |
| Approach: | They propose a new dataset for Target-based Offensive language identification that uses post-level and token-level annotations to identify offensive language on Twitter. |
| Outcome: | The proposed taxonomy can be used to annotate offensive language on English Twitter posts. |
Offensive Language and Hate Speech Detection for Danish (2020.lrec-1)
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| Challenge: | a growing number of social media platforms are detecting and dealing with offensive language . a recent study found that the best performing system for English is best for Danish . |
| Approach: | They propose automatic methods to detect offensive language on social media platforms . they use user-generated comments from various social media sites to find offensive language . |
| Outcome: | The proposed system performs best for both English and Danish language . it achieves a macro averaged F1-score of 0.74 and a best for Danish achieves 0.73 . |
Detecting and Reducing Bias in a High Stakes Domain (D19-1)
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| Challenge: | Existing research shows that a deep learning model can predict aggression and loss in posts by focusing on stop words such as “a” or “on”. |
| Approach: | They developed an approach to interpret a deep learning model that often bases its predictions on stop words such as "a" or "on" to tackle bias, they annotated the rationales and built models that drastically reduce bias. |
| Outcome: | The proposed model can predict aggression and loss in posts by using stop words such as "a" or "on" the new annotations enable us to quantitatively measure how justified the model predictions are, and build models that drastically reduce bias. |
Multilingual Offensive Language Identification with Cross-lingual Embeddings (2020.emnlp-main)
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| Challenge: | Several studies investigating methods to detect offensive content in social media use English data. |
| Approach: | They apply cross-lingual contextual embeddings and transfer learning to make predictions in languages with less resources. |
| Outcome: | The proposed method compares favorably to the best systems submitted to recent shared tasks on Bengali, Hindi, and Spanish. |
So Hateful! Building a Multi-Label Hate Speech Annotated Arabic Dataset (2024.lrec-main)
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| Challenge: | Social media enables widespread propagation of hate speech targeting groups based on ethnicity, religion, or other characteristics. |
| Approach: | They analyze 70,000 Arabic tweets to identify hate speech patterns and train models . 15% of tweets contain offensive language while 6% have hate speech . authors hope to prevent spread of hateful content on social media platforms . |
| Outcome: | The analysis of 70,000 Arabic tweets shows that 15% of tweets contain offensive language while 6% have hate speech . 10% of tweet provide verifiable factual claims, and 7% are deemed important . |
Offensive Content Detection via Synthetic Code-Switched Text (2022.coling-1)
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| Challenge: | Existing methods to detect offensive content in social media platforms are limited by the availability of labeled code-switched data. |
| Approach: | They propose a method for generating synthetic code-switched offensive content data using human-generated data and a keyword classification baseline. |
| Outcome: | The proposed algorithm can be used to generate synthetic code-switched offensive content data and train it on human-generated data. |
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. |