Challenge: Recent research has shown that predicting sources’ reliability is an important first-prior step in addressing additional challenges such as fake news detection and fact-checking.
Approach: They propose a method that leverages reinforcement learning strategies to estimate the reliability degree of news sources based on how all the news media sources interact with each other on the Web.
Outcome: The proposed method can predict reliability labels on a large news media reliability dataset.

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Hidden Biases in Unreliable News Detection Datasets (2021.eacl-main)

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Challenge: Recent studies show that automatic unreliable news detection models only use the article itself without resorting to fact-checking mechanisms.
Approach: They propose to use a simple model as a difficulty/bias probe instead of a complex one . they observe a significant drop in accuracy for all models tested in a clean split .
Outcome: The proposed model can achieve good performance by memorizing site-label mapping instead of modeling the real task.
Predicting Factuality of Reporting and Bias of News Media Sources (D18-1)

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Challenge: a new study examines the factuality of news media and its biases . social media has democratized content creation and spread information online .
Approach: They propose to characterize entire news media to predict factuality and bias . they experiment with news websites and a set of features derived from their content .
Outcome: The proposed model shows that the features of news websites perform better than baseline . the results show that the feature types are important for fact-checking systems .
A Survey on Predicting the Factuality and the Bias of News Media (2024.findings-acl)

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Challenge: a growing number of scholars are profiling entire news outlets to profile fake content . political bias detection is also an important topic, but the two problems have been addressed separately .
Approach: They argue that media profiling should be based on factuality and bias together . they argue that it is difficult to fact-check every single suspicious claim or article manually .
Outcome: The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically.
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 .
Identifying Informational Sources in News Articles (2023.emnlp-main)

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Challenge: Identifying sources of information in news articles is relevant to many tasks in NLP, including misinformation detection and argumentation.
Approach: They propose a task to study compositionality of sources in news articles to understand how they are chosen to complement each other.
Outcome: The proposed dataset can be used to train high-performing models for information detection and source attribution.
No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media (2022.findings-emnlp)

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Challenge: Recent work has relied on word embedding bias measures, such as WEAT, but these methods can be inaccurate due to several representation issues, such low-resource settings and token frequency differences.
Approach: They propose to use WEAT to quantify social bias in US online news articles and embed embedding algorithms to account for the aforementioned issues.
Outcome: The proposed algorithms do not match the literature, but they reduce the gap.
Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources (P18-2)

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Challenge: a new study examines how users react to news sources with different levels of credibility . a recent study found that 59% of bitly-URLs on Twitter are shared without ever being read .
Approach: They develop a model to classify user reactions into one of nine types . they also measure the speed and type of reaction for trusted and deceptive news sources .
Outcome: The proposed model classifies user reactions into one of nine types, such as answer, elaboration, and question, etc.
Predicting Clickbait Strength in Online Social Media (2020.coling-main)

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Challenge: Clickbaits are sensational, provocative or controversial posts that entice readers to click on them.
Approach: They propose to model clickbait strength prediction using transformers to predict clickbaiting intensity.
Outcome: The proposed model outperforms existing methods on a benchmark dataset with 39K posts on 3K posts.
Evidence-based Trustworthiness (P19-1)

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Challenge: Existing methods to estimate the trustworthiness of information sources are local in that they apply to a given claim.
Approach: They propose a framework for estimating the trustworthiness of information sources in an environment where multiple sources provide claims and supporting evidence.
Outcome: The proposed models show that they improve on baselines and show that the proposed models are more accurate than baselines.
Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts (2025.findings-acl)

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Challenge: Important efforts to characterize news media outlets in terms of their political bias and factuality are labor-intensive and prone to human biases.
Approach: They propose a method that emulates criteria used by professional fact-checkers to assess the factuality and political bias of an entire outlet.
Outcome: The proposed method improves on baselines and with multiple LLMs.

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