Challenge: Existing methods to detect online abuse focus on the more explicit forms of abuse . existing methods focus on detecting subtler forms of online abuse leaving them unnoticed .
Approach: They propose a task to detect unpalatable questions using reddit data to implement a context-aware dataset and implement 'learning models' they hope future research will address subtle forms of abuse since harm passes unnoticed through existing detection systems.
Outcome: The proposed task is based on a dataset of reddit users and a conversational context.

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Introducing CAD: the Contextual Abuse Dataset (2021.naacl-main)

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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.
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.
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.
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 .
Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection (2024.lrec-main)

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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.
CoRAL: a Context-aware Croatian Abusive Language Dataset (2022.findings-aacl)

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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.
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Implicitly Abusive Language – What does it actually look like and why are we not getting there? (2021.naacl-main)

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Challenge: Existing datasets make learning implicit abuse difficult, argues a new position paper . a lack of work on implicit abuse has limited the effectiveness of automatic detection .
Approach: They argue that existing datasets make learning implicit abuse difficult . they propose a divide-and-conquer strategy to detect implicit abuse .
Outcome: The proposed model could be improved to detect implicit abuse in a dataset with a standardized model.
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI (2021.emnlp-main)

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Challenge: Existing studies on abusive language towards conversational AI systems are not conclusive as they are not performed with live systems nor with real users due to the lack of reliable abuse detection tools.
Approach: They propose to use a convAI dataset to account for the complexity of the task and to bench-mark existing models against this data.
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Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language (2023.emnlp-main)

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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.
Implicitly Abusive Comparisons – A New Dataset and Linguistic Analysis (2021.eacl-main)

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Challenge: Using crowdsourcing, we can detect implicitly abusive comparisons . Abusive language is defined as hurtful, derogatory or obscene utterances made by one person to another .
Approach: They propose to use crowdsourcing to generate a dataset for detecting implicitly abusive comparisons . they also use a range of linguistic features to better understand abusive comparison mechanisms .
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Oddballs and Misfits: Detecting Implicit Abuse in Which Identity Groups are Depicted as Deviating from the Norm (2024.emnlp-main)

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Challenge: Abusive language is often defined as hurtful, derogatory or obscene utterances made by one person to another.
Approach: They propose to use a dataset to detect abusive sentences in identity groups . they also report on classification experiments.
Outcome: The proposed dataset includes 7 identity groups and includes classification experiments.

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