Challenge: Covid-19 infodemic has led to low quality information leading to poor health decisions . authors propose a framework for analyzing false claims and reasoning about the decisions a person makes .
Approach: They propose a framework linking stance and reason analysis and moral sentiment analysis.
Outcome: The proposed framework provides reliable predictions even in low-supervision settings.

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Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society (2021.findings-emnlp)

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Challenge: a dataset of 16K manually annotated tweets is used to analyze disinformation . the democratic nature of social media has raised questions about the quality and the factuality of the information that is shared on these platforms.
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Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy (2023.findings-eacl)

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Challenge: As COVID-19 vaccines were rolled out, they were met with widespread hesitancy.
Approach: They propose a new framework for intent discovery that leverages existing intent classifiers to provide a real-world conversational dataset of conversations conducted by actual users with VIRA.
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VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines (2022.lrec-1)

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Challenge: VaccineLies can detect misinformation about vaccines on Twitter without using language resources.
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Outcome: VaccineLies can detect misinformation on Twitter and identify the stance towards it.
Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature (2023.acl-short)

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Challenge: During times of pandemic, treatment options are limited, and developing new drug treatments is infeasible in the short-term.
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Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments (2023.acl-long)

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Challenge: Existing evaluations of human-in-the-loop systems to combat misinformation are often set up automatically using datasets that were retrospectively constructed.
Approach: They propose a human-in-the-loop evaluation framework for fact-checking novel misinformation claims and identifying social media messages that support them.
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Annotating Perspectives on Vaccination (2020.lrec-1)

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Challenge: Vaccination corpus is a corpus of texts related to the online vaccination debate . it contains documents from the Internet which reflect different views on vaccinations .
Approach: They present a corpus of texts related to the online vaccination debate annotated with perspectives about attribution, claims and opinions.
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COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter (2021.acl-srw)

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Challenge: Social media is used by individuals and organisations as a platform to spread misinformation.
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Stakeholder Suite: A Unified AI Framework for Mapping Actors, Topics and Arguments in Public Debates (2026.eacl-demo)

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Challenge: Existing media intelligence tools rely on descriptive analytics with limited transparency.
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COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

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Challenge: a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
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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.
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