Challenge: a growing number of people consume news online, but there are different types of "fake news" many online news outlets use the same journalistic principles that have been in use for newspapers for decades, especially factchecking.
Approach: They propose a metadata scheme to enable users to handle "fake news" they also propose 'filter bubble' effect and abuse language .
Outcome: The proposed metadata scheme enables standardisation of these phenomena in online media.

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Automatic Detection of Fake News (C18-1)

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Challenge: a growing number of fake news detection tools are needed to identify trustworthy news sources.
Approach: They propose to use two novel datasets to automate the identification of fake news . they propose learning experiments to build accurate fake news detectors .
Outcome: The proposed algorithms achieve accuracies of up to 76% and compare them with other tools . the proposed algorithms are based on satirical news sources and fact-checking websites .
An Interactive Framework for Profiling News Media Sources (2024.naacl-long)

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Challenge: Existing tools for detecting fake news are difficult for automated systems . e.g., we focus on the source level, and ask: Is this source factual or politically biased?
Approach: They propose an interactive framework for news media profiling that uses graphs and pre-trained large language models to characterize social context on social media.
Outcome: The proposed framework can detect fake and biased news media with as little as 5 human interactions . it can scale better, as often sources publish have same factuality/political bias as source .
BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)

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Challenge: a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles .
Approach: They introduce manually verified corpus of compelling fake and questionable news articles on the USA politics . they plan to extend the corpus in the future and use it for automated fake news detection.
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The Battlefront of Combating Misinformation and Coping with Media Bias (2022.aacl-tutorials)

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Challenge: a growing number of misinformation and misinformation is affecting our daily lives . a tutorial aims to address the challenges of detecting fake news and media bias .
Approach: They provide an overview of the frontier in fighting misinformation . they propose to develop a robust fake news detection system to combat misinformation.
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Annotation-Scheme Reconstruction for “Fake News” and Japanese Fake News Dataset (2022.lrec-1)

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Challenge: Contemporary research focuses on the factuality aspect of the news, but this aspect alone is insufficient to explain “fake news.”
Approach: They propose to use Japanese fake news datasets to classify whether news content is false . they propose to do this by using existing fake news data to investigate fake news .
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Words are the Window to the Soul: Language-based User Representations for Fake News Detection (2020.coling-main)

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Challenge: Existing studies on fake news classification focus on textual content, but also social context in which news are consumed.
Approach: They propose a model that creates representations of individuals on social media based only on the language they produce and uses them to detect fake news.
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Discovering Biased News Articles Leveraging Multiple Human Annotations (2020.lrec-1)

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Challenge: Political propaganda and one-sided views can be found in the news and can cause distrust in media.
Approach: They propose to annotate politically biased news articles by an algorithm annotated by domain experts and crowd workers and to compare them to crowd workers.
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Treebanking User-Generated Content: A Proposal for a Unified Representation in Universal Dependencies (2020.lrec-1)

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Challenge: Despite the increasing number of contributions on Part-of-Speech tagging and parsing, automatic processing of user-generated content (UGC) still represents a challenging task.
Approach: They propose a set of guidelines for the annotation of user-generated texts within the Universal Dependencies framework.
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Veritas Annotator: Discovering the Origin of a Rumour (D19-66)

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Challenge: a growing number of fake news sites are used for spreading fake news . a lack of a reliable data set is limiting the use of machine learning in fact-checking .
Approach: They propose a web application that can detect the origin of a rumour by identifying its source .
Outcome: The proposed application can detect fake news claims with better accuracy than humans .
FakeFlow: Fake News Detection by Modeling the Flow of Affective Information (2021.eacl-main)

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Challenge: In short news articles, authors add exaggerations or fabricate events to manipulate readers' emotions.
Approach: They propose to model the flow of affective information in fake news articles using a neural architecture and combine topic and affective data extracted from text.
Outcome: The proposed model outperforms state-of-the-art methods on four real-world datasets and shows that it can capture the flow of affective information in fake news articles.

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