Challenge: a new study examines how users interact with LGBTQ+ news content . a corpus of 1,419,047 comments on 3,161 YouTube news videos is used to analyze the content - both positive and negative - of cable news outlets.
Approach: They analyze how users interact with LGBTQ+ news content via a corpus of 1,419,047 comments on 3,161 YouTube news videos of major US cable news outlets.
Outcome: The proposed classifier detects positive (hope speech), negative, neutral, and irrelevant content.

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BU-NEmo: an Affective Dataset of Gun Violence News (2022.lrec-1)

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Challenge: Using a dataset that contains headline and image pairings from 840 news articles, we explore the relationship between image and text influence on human emotional response.
Approach: They propose to use a U.S. gun violence news dataset that contains headline and image pairings from 840 news articles with 15K high-quality crowdsourced annotations on emotional responses.
Outcome: The proposed dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component.
When Words Wear Masks: Detecting Malicious Intents and Hostile Impacts of Online Hate Speech (2026.eacl-short)

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Challenge: Existing methods for hate speech detection treat hate speech as a monolithic phenomenon, ignoring the speaker’s motivations and potential societal consequences.
Approach: They propose a dataset with a dual taxonomy that separates Intent (why the speaker produced hate speech) and Impact (what harm it may cause to individuals and communities) they propose to use this data to enable content moderation and user safety.
Outcome: The proposed dataset captures Intent (why the speaker produced hate speech) and Impact (what harm it may cause to individuals and communities) of online hateful posts.
Untangling Hate Speech Definitions: A Semantic Componential Analysis Across Cultures and Domains (2025.findings-naacl)

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Challenge: a new framework for analyzing hate speech definitions is proposed to address cultural differences in interpretations . a dataset of 493 definitions from more than 100 cultures is used to analyze hate speech .
Approach: They propose a framework for a cross-cultural and cross-domain analysis of hate speech definitions . they use open-source LLMs to analyze the impact of different definitions on hate speech detection .
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Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021.emnlp-main)

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Challenge: Existing studies on explicit or overt hate speech have failed to address a more pervasive form based on coded or indirect language.
Approach: They propose a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication.
Outcome: The proposed dataset will serve as a useful benchmark for understanding this multifaceted issue.
Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting (2023.findings-emnlp)

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Challenge: Prior work in NLP has only studied media bias via linguistic style and word usage.
Approach: They annotate a dataset containing 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets.
Outcome: The proposed dataset contains 8,511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets.
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources (P18-2)

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Challenge: a new study examines how users react to news sources with different levels of credibility . a recent study found that 59% of bitly-URLs on Twitter are shared without ever being read .
Approach: They develop a model to classify user reactions into one of nine types . they also measure the speed and type of reaction for trusted and deceptive news sources .
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Countering Hateful and Offensive Speech Online - Open Challenges (2024.emnlp-tutorials)

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Challenge: a comprehensive understanding of the field is needed to maintain respectful and inclusive online environments.
Approach: This tutorial aims to provide attendees with a comprehensive understanding of the field by delving into essential dimensions such as multilingualism, counter-narrative generation, a hands-on session with one of the most popular APIs for detecting hate speech, fairness, and ethics in AI, and the use of recent advanced approaches.
Outcome: This tutorial aims to provide attendees with a comprehensive understanding of the field by delving into essential dimensions such as multilingualism, counter-narrative generation, a hands-on session with one of the most popular APIs for detecting hate speech, fairness, and ethics in AI, and the use of recent advanced approaches.
Explain the Flag: Contextualizing Hate Speech Beyond Censorship (2026.findings-acl)

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Challenge: a hybrid approach to detect and explain hate speech combines large language models with vocabularies to detect hate speech in three languages . authors: the spread of hate speech online has serious personal, social, and legal consequences . eu has launched initiatives to analyze, regulate, and counteract online hate speech, authors say .
Approach: They propose a hybrid approach that combines Large Language Models with vocabularies to detect hate speech in English, French, and Greek.
Outcome: The proposed approach outperforms baselines in English, French, and Greek . it uses large language models and vocabularies to detect and explain hate speech . human evaluation shows that the proposed approach is accurate and clear .
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 .
What the #?*!: Disentangling Hate Across Target Identities (2025.naacl-long)

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Challenge: Hate speech classifiers do not perform equally well in detecting hateful expressions towards different target identities.
Approach: They propose to use two recently proposed functionality test datasets to analyze the impact of different factors on HS prediction.
Outcome: The proposed classifiers do not perform equally well across different datasets and different target identities.

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