Papers by Shaden Shaar

8 papers
A Survey on Multimodal Disinformation Detection (2022.coling-1)

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Challenge: Recent years have witnessed the proliferation of offensive content online such as fake news, propaganda, misinformation, and disinformation.
Approach: They propose to tackle online multimodal offensive content using different modalities and combinations thereof.
Outcome: The proposed approach combines factuality and harmfulness in a framework that can be used for multiple modalities and combinations of modality.
Cross-lingual Emotion Detection (2022.lrec-1)

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Challenge: Emotion detection is a useful tool for understanding human behavior, but constructing annotated datasets to train models can be expensive.
Approach: They propose to use English as the source language with Arabic and Spanish as target languages to train models for emotion detection in a target language.
Outcome: The proposed approaches surpass state-of-the-art models in Arabic and Spanish by 4% and 5% respectively.
Detecting Propaganda Techniques in Memes (2021.acl-long)

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Challenge: Propaganda can be defined as a form of communication that aims to influence opinions or the actions of people towards a specific goal.
Approach: They propose to detect the type of propaganda techniques used in memes by annotating them with 22 techniques.
Outcome: The proposed model identifies 22 propaganda techniques in memes, which can appear in text, image or both .
Prta: A System to Support the Analysis of Propaganda Techniques in the News (2020.acl-demos)

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Challenge: recent events have brought the public attention to the dangers of online disinformation.
Approach: a new tool helps users analyze propaganda using specific rhetorical and psychological techniques. a prta system identifies the spans in which propaganda techniques occur and compares them.
Outcome: a new tool can analyze articles crawled on a regular basis and compare them on the basis of their use of propaganda techniques.
The Role of Context in Detecting Previously Fact-Checked Claims (2022.findings-naacl)

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Challenge: Recent years have seen the proliferation of disinformation and fake news online.
Approach: They propose to model the context of a political debate and the contexts of the document describing the fact-checked claim.
Outcome: The proposed model improves on the state-of-the-art model by modeling the context of the claim . the experimental results show that the model can provide 10+ points of improvement over the state of the art model .
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document (2022.findings-emnlp)

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Challenge: Recent years have brought us a proliferation of false claims online, which spread fast . fact-checkers have been using automated fact-finding to verify claims .
Approach: They propose a system that can detect claims that can be fact-checked by a given database . they create a manually annotated document dataset and propose evaluation measures .
Outcome: The proposed system achieves sizable performance gains over strong baselines.
That is a Known Lie: Detecting Previously Fact-Checked Claims (2020.acl-main)

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Challenge: a large number of fact-checked claims have been accumulated over the years . despite the importance of fact checking, it has been largely ignored by the research community .
Approach: They propose to automate fact-checking by focusing on claims that have already been fact-tested . they propose to use specialized datasets to compare different methods .
Outcome: The proposed task shows that it improves over state-of-the-art methods.
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.
Approach: They use a dataset of manually annotated tweets to analyze COVID-19 disinformation . they show that tweets contain fake cures, rumors, conspiracy theories and xenophobia .
Outcome: The proposed dataset shows that it is useful in monolingual vs. multilingual settings.

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