Papers by Elisa Sartori

7 papers
Insights into using temporal coordinated behaviour to explore connections between social media posts and influence (2025.findings-emnlp)

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Challenge: Political campaigns often use coordinated behaviour to identify communities of users who exhibit similar patterns.
Approach: They analysed messages users were exposed to during the UK 2019 election and compared those received by users who shifted communities with others covering the same topics.
Outcome: The results show that political campaigns often use coordinated behaviour to identify communities of users who exhibit similar patterns.
CritiSense: Critical Digital Literacy and Resilience Against Misinformation (2026.acl-demo)

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Challenge: a recent study found that social media misinformation is reactive and claim-specific, and can degrade under temporal and cross-lingual/domain shift.
Approach: They present a mobile media-literacy app that builds digital literacy skills through short, interactive challenges with instant feedback.
Outcome: The app is the first multilingual and modular platform to improve digital literacy skills.
Entity Framing and Role Portrayal in the News (2025.findings-acl)

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Challenge: a dataset of news articles containing 22 fine-grained characters is annotated for entity framing and role portrayal . the dataset includes 1,378 recent news articles in five languages focusing on the Ukraine-Russia War and climate change .
Approach: They propose a multilingual and hierarchical corpus annotated for entity framing and role portrayal in news articles.
Outcome: The proposed dataset includes 1,378 recent news articles in five languages focusing on the Ukraine-Russia War and climate change . the authors report evaluation results on state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, paragraph, and sentence .
MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection (2026.eacl-long)

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Challenge: Existing studies on intentionality behind disinformation do not address intent behind disinformative agents.
Approach: They propose an intent-augmented reasoning system that integrates intent analysis to mitigate the persuasive impact of disinformation.
Outcome: The proposed corpus is the first human-annotated English corpus to capture disinformation and its malicious intent.
PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles (2025.acl-long)

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Challenge: a new dataset of news articles annotated for narratives provides a framework for narrative detection . recurring narratives can propagate with very high velocity across audiences, languages and countries .
Approach: They propose a multilingual dataset annotated for narratives using two-level taxonomies . they define narrative as a recurring, repetitive, overt or implicit claim that promotes a specific interpretation or viewpoint on an ongoing topic .
Outcome: The proposed dataset will foster research in narrative detection and enable new research directions . the authors identify multiple narratives in the same article, and the results are published online .
NarratEX Dataset: Explaining the Dominant Narratives in News Texts (2025.findings-emnlp)

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Challenge: a dataset is created to explain the choice of the dominant narrative in a news article . the dataset is intended to address discourse polarization and propaganda detection .
Approach: They propose a dataset for explaining the choice of the dominant narrative in a news article . the dataset is annotated manually with a dominant narrative and sub-narrative labels .
Outcome: The proposed dataset is designed to explain the choice of the dominant narrative in a news article.
PropXplain: Can LLMs Enable Explainable Propaganda Detection? (2025.findings-emnlp)

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Challenge: Currently, propagandistic content detection studies focus on detection, with little attention given to explanations justifying the predicted label.
Approach: They propose a multilingual explanation-enhanced dataset and an explanation-based LLM to address this issue.
Outcome: The proposed model performs comparably while also generating explanations.

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