Papers with policymakers

16 papers
SpiritRAG: A Q&A System for Religion and Spirituality in the United Nations Archive (2025.emnlp-demos)

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Challenge: Religion and spirituality (R/S) are complex and domain-dependent concepts that have long confounded researchers and policymakers.
Approach: They propose an interactive question-answering system based on Retrieval-Augmented Generation (RAG) SpiritRAG allows researchers and policymakers to conduct complex, context-sensitive database searches of large datasets .
Outcome: SpiritRAG is an interactive Q&A system based on Retrieval-Augmented Generation (RAG) built using 7,500 UN resolution documents related to religion and spirituality in the domains of health and education.
ClinicalTrialsHub: Bridging Registries and Literature for Comprehensive Clinical Trial Access (2026.eacl-demo)

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Challenge: ClinicalTrialsHub consolidates clinical trial data from ClinicalTrial.gov and augments it by extracting and structuring trial-relevant information from PubMed.
Approach: They propose a search-focused platform that consolidates PubMed data and extracts structured trial information.
Outcome: ClinicalTrialsHub increases access to structured clinical trial data by 83.8% compared to ClinicalTrial.gov alone.
Communication as a Complex System: Modeling the Feedback Dynamics of Trust and Credibility (2026.eacl-srw)

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Challenge: A scoping literature review synthesized disparate communication models from media studies, science communication, psychology, and information science to identify a shared set of system variables.
Approach: The study synthesized disparate communication models from media studies, science communication, psychology, and information science to identify a shared set of system variables.
Outcome: The proposed framework provides a foundation for future system dynamics modeling to examine how interventions in transparency, media literacy, or platform governance may influence public trust over time.
Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model (P18-2)

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Challenge: Existing work on predicting popularity of online petitions based on initial popularity trajectory has focused on estimating the number of signatures a petition gets in the first x hours, and predicting the total number of signed petitions at the end of its lifetime.
Approach: They propose a CNN-based model to predict the popularity of a petition based on its textual content and use it to model the influence of other petition signers.
Outcome: The proposed model is based on UK and US government petition datasets and is compared with previous work on predicting popularity over time based upon initial popularity trajectory.
Analyzing the Dynamics of Climate Change Discourse on Twitter: A New Annotated Corpus and Multi-Aspect Classification (2024.lrec-main)

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Challenge: a lack of data on climate change discourse has highlighted the need for further advancement . a new study examines the discourse on social media platforms that ignores climate change .
Approach: They analyze climate change discourse on Twitter using a meticulously annotated dataset . they find relevance, stance, hate speech, direction of hate, humor and humor are key aspects .
Outcome: The proposed method combines annotated tweets with a dataset of 15,309 tweets . it reveals tweet distribution patterns, stance prevalence, and hate speech trends .
Beyond Binary: Towards Embracing Complexities in Cyberbullying Detection and Intervention - a Position Paper (2024.lrec-main)

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Challenge: Existing methods for CB detection oversimplify the problem of CB as a binary classification task.
Approach: They propose to use large language models to generate CB-related datasets . they propose to combine cognitive and linguistic models to help identify CB incidents .
Outcome: The proposed approach aims to help researchers and policymakers make informed decisions . it uses large language models such as Claude-2 and Llama2-Chat to generate CB-related datasets .
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination (2022.lrec-1)

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Challenge: Social media are integrated with our daily life and are used to circulate information.
Approach: They develop and publicly release the first largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign.
Outcome: The proposed dataset is the largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign, covering many countries in the Arab region.
Privacy by Design and Language Resources (2020.lrec-1)

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Challenge: Privacy by Design is an approach in which privacy and data protection are embedded throughout the project lifecycle . the principle of Privacy by design was first mentioned in the 1995 EU Data Protection Directive .
Approach: a paper proposes to analyze the practical meaning of Privacy by Design in the context of Language Resources . the paper propose measures and safeguards that can be implemented by the community to ensure respect of this principle.
Outcome: The proposed paper analyzes the practical meaning of Privacy by Design in the context of Language Resources . proposed safeguards can be implemented by the community to ensure respect of this principle.
GPT Deciphering Fedspeak: Quantifying Dissent Among Hawks and Doves (2023.findings-emnlp)

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Challenge: We use GPT-4 to quantify dissent among members on the topic of inflation . transcripts and minutes reflect the diversity of member views in a way that is lost or omitted from the public statements.
Approach: They use transcripts and minutes to quantify dissent among FOMC members . they find that transcripts reflect diversity of member views in a way that is lost or omitted .
Outcome: The proposed method better captures extremes, which mirror human annotations, and suggests that Large Language Models can avoid noise in this nuanced context.
The TalkMoves Dataset: K-12 Mathematics Lesson Transcripts Annotated for Teacher and Student Discursive Moves (2022.lrec-1)

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Challenge: Currently, classroom recordings are limited due to practical and privacy concerns and sharing is restricted due to limited access to valuable resources and data sets.
Approach: They propose to use the TalkMoves dataset to analyze the nature of teacher and student discourse in K-12 math classrooms.
Outcome: The TalkMoves dataset contains 567 human-annotated K-12 mathematics lesson transcripts derived from video recordings.
Examining Temporalities on Stance Detection towards COVID-19 Vaccination (2024.lrec-main)

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Challenge: Existing studies have highlighted the importance of vaccination as an effective strategy to control the transmission of the COVID-19 virus.
Approach: They evaluate a range of transformer-based models using chronological and random splits of social media data to examine the impact of temporal concept drift on stance detection towards COVID-19 vaccination.
Outcome: The proposed models show that the models performed better with chronological and random splits than with random split models.
From Laughter to Inequality: Annotated Dataset for Misogyny Detection in Tamil and Malayalam Memes (2024.lrec-main)

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Challenge: a new form of memes has emerged to combat misogyny and harmful stereotypes . authors present a dataset to analyze online misogamy in Tamil and Malayalam communities .
Approach: They propose to create an annotated dataset with detailed annotation guidelines to analyze online misogyny within Tamil and Malayalam-speaking communities.
Outcome: The proposed dataset reveals the world of gender bias and stereotypes in Tamil and Malayalam-speaking communities.
Jailbreak-Tuning: Models Efficiently Learn Jailbreak Susceptibility (2025.emnlp-main)

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Challenge: a recent study shows that fine-tuning can produce helpful-only models with safeguards destroyed.
Approach: They propose a method for fine-tuning models to generate detailed, high-quality responses to harmful requests.
Outcome: The proposed method produces helpful-only models with safeguards destroyed . OpenAI, Google, and Anthropic models will fully comply with requests for CBRN assistance .
Argument-based Detection and Classification of Fallacies in Political Debates (2023.emnlp-main)

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Challenge: Fallacies are arguments that employ faulty reasoning, causing inaccurate conclusions and invalid inferences . ad hominem fallacy is one of the most common fallacy labels used in political debates despite its use in many scenarios .
Approach: They extend the ElecDeb60To16 dataset of U.S. presidential debates annotated with fallacious arguments by incorporating the most recent Trump-Biden debate.
Outcome: The proposed method extends the ElecDeb60To16 dataset of U.S. presidential debates annotated with fallacious arguments .
Foveate, Attribute, and Rationalize: Towards Physically Safe and Trustworthy AI (2023.findings-acl)

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Challenge: Covertly unsafe text is an area of particular interest as it is difficult to detect as harmful . previous work focused on explicit violent text and typically expressed through violent keywords.
Approach: They propose a framework that leverages external knowledge for trustworthy rationale generation in the context of safety.
Outcome: The proposed framework improves safety classification accuracy by 5.9% on the SafeText dataset, and shows that it is more accurate than previous frameworks.
ArMeme: Propagandistic Content in Arabic Memes (2024.emnlp-main)

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Challenge: a lack of media literacy is a major factor contributing to the spread of misleading information on social media.
Approach: They analyze a dataset of 6K Arabic memes with manual annotations . they propose to develop computational tools for their detection .
Outcome: The proposed dataset is a first resource for Arabic multimodal research.

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