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1. “Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content

   https://doi.org/10.1177/0002764219878224  

   Molina, Maria D.; Sundar, S. Shyam; Le, Thai; Lee, Dongwon (February 2021, American Behavioral Scientist)

   null (Ed.)

   As the scourge of “fake news” continues to plague our information environment, attention has turned toward devising automated solutions for detecting problematic online content. But, in order to build reliable algorithms for flagging “fake news,” we will need to go beyond broad definitions of the concept and identify distinguishing features that are specific enough for machine learning. With this objective in mind, we conducted an explication of “fake news” that, as a concept, has ballooned to include more than simply false information, with partisans weaponizing it to cast aspersions on the veracity of claims made by those who are politically opposed to them. We identify seven different types of online content under the label of “fake news” (false news, polarized content, satire, misreporting, commentary, persuasive information, and citizen journalism) and contrast them with “real news” by introducing a taxonomy of operational indicators in four domains—message, source, structure, and network—that together can help disambiguate the nature of online news content. 

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2. Disinformation Spillover: Uncovering the Ripple Effect of Bot-Assisted Fake Social Engagement on Public Attention

   https://doi.org/10.25300/MISQ/2023/17195  

   Lee, Sanghak; Shin, Donghyuk; Kwon, K Hazel; Han, Sang Pil; Lee, Seok Kee (September 2024, MIS Quarterly)

   Disinformation activities that aim to manipulate public opinion pose serious challenges to managing online platforms. One of the most widely used disinformation techniques is bot-assisted fake social engagement, which is used to falsely and quickly amplify the salience of information at scale. Based on agenda-setting theory, we hypothesize that bot-assisted fake social engagement boosts public attention in the manner intended by the manipulator. Leveraging a proven case of bot-assisted fake social engagement operation in a highly trafficked news portal, this study examines the impact of fake social engagement on the digital public’s news consumption, search activities, and political sentiment. For that purpose, we used ground-truth labels of the manipulator’s bot accounts, as well as real-time clickstream logs generated by ordinary public users. Results show that bot-assisted fake social engagement operations disproportionately increase the digital public’s attention to not only the topical domain of the manipulator’s interest (i.e., political news) but also to specific attributes of the topic (i.e., political keywords and sentiment) that align with the manipulator’s intention. We discuss managerial and policy implications for increasingly cluttered online platforms. 

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3. Correcting fake news headlines after repeated exposure: memory and belief accuracy in younger and older adults

   https://doi.org/10.1186/s41235-024-00585-3  

   Kemp, Paige L; Loaiza, Vanessa M; Kelley, Colleen M; Wahlheim, Christopher N (December 2024, Cognitive Research: Principles and Implications)

   Abstract The efficacy of fake news corrections in improving memory and belief accuracy may depend on how often adults see false information before it is corrected. Two experiments tested the competing predictions that repeating fake news before corrections will either impair or improve memory and belief accuracy. These experiments also examined whether fake news exposure effects would differ for younger and older adults due to age-related differences in the recollection of contextual details. Younger and older adults read real and fake news headlines that appeared once or thrice. Next, they identified fake news corrections among real news headlines. Later, recognition and cued recall tests assessed memory for real news, fake news, if corrections occurred, and beliefs in retrieved details. Repeating fake news increased detection and remembering of corrections, correct real news retrieval, and erroneous fake news retrieval. No age differences emerged for detection of corrections, but younger adults remembered corrections better than older adults. At test, correct fake news retrieval for earlier-detected corrections was associated with better real news retrieval. This benefit did not differ between age groups in recognition but was greater for younger than older adults in cued recall. When detected corrections were not remembered at test, repeated fake news increased memory errors. Overall, both age groups believed correctly retrieved real news more than erroneously retrieved fake news to a similar degree. These findings suggest that fake news repetition effects on subsequent memory accuracy depended on age differences in recollection-based retrieval of fake news and that it was corrected. 

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4. Going Viral: How Fear, Socio-Cognitive Polarization and Problem-Solving Influence Fake News Detection and Proliferation During COVID-19 Pandemic

   https://doi.org/10.3389/fcomm.2020.562588  

   Salvi, Carola; Iannello, Paola; Cancer, Alice; McClay, Mason; Rago, Sabrina; Dunsmoor, Joseph E.; Antonietti, Alessandro (January 2021, Frontiers in Communication)

   null (Ed.)

   In times of uncertainty, people often seek out information to help alleviate fear, possibly leaving them vulnerable to false information. During the COVID-19 pandemic, we attended to a viral spread of incorrect and misleading information that compromised collective actions and public health measures to contain the spread of the disease. We investigated the influence of fear of COVID-19 on social and cognitive factors including believing in fake news, bullshit receptivity, overclaiming, and problem-solving—within two of the populations that have been severely hit by COVID-19: Italy and the United States of America. To gain a better understanding of the role of misinformation during the early height of the COVID-19 pandemic, we also investigated whether problem-solving ability and socio-cognitive polarization were associated with believing in fake news. Results showed that fear of COVID-19 is related to seeking out information about the virus and avoiding infection in the Italian and American samples, as well as a willingness to share real news (COVID and non-COVID-related) headlines in the American sample. However, fear positively correlated with bullshit receptivity, suggesting that the pandemic might have contributed to creating a situation where people were pushed toward pseudo-profound existential beliefs. Furthermore, problem-solving ability was associated with correctly discerning real or fake news, whereas socio-cognitive polarization was the strongest predictor of believing in fake news in both samples. From these results, we concluded that a construct reflecting cognitive rigidity, neglecting alternative information, and black-and-white thinking negatively predicts the ability to discern fake from real news. Such a construct extends also to reasoning processes based on thinking outside the box and considering alternative information such as problem-solving. 

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5. Is Less Really More? Fake News Detection with Limited Information

   https://doi.org/10.1145/3748239.3748243  

   Cao, Zhaoyang; Nguyen, John; Zafarani, Reza (July 2025, ACM SIGKDD Explorations Newsletter)

   The threat that online fake news and misinformation pose to democracy, justice, public confidence, and especially to vulnerable populations has led to a sharp increase in the need for fake news detection and intervention. Whether multi-modal or pure text-based, most existing fake news detection methods depend on textual analysis of entire articles. However, these fake news detection methods come with certain limitations. For instance, fake news detection methods that rely on full text can be computationally inefficient, demand large amounts of training data to achieve competitive accuracy, and may lack robustness across different datasets. This is because fake news datasets have strong variations in terms of the level and types of information they provide; where some can include large paragraphs of text with images and metadata, and others can be a few short sentences. Perhaps if one could only use minimal information to detect fake news, fake news detection methods could become more robust and resilient to the lack of information. We aim to overcome these limitations by detecting fake news using systematically selected, limited information that is both effective and capable of delivering robust, promising performance. We propose a framework called SLIM (Systematically-selected Limited Information) for fake news detection. In SLIM, we quantify the amount of information by introducing information-theoretic measures. SLIM leverages limited information (e.g., a few named entities) to achieve performance in fake news detection comparable to that of state-of-the-art obtained using the full text, even when the dataset is sparse. Furthermore, by combining various types of limited information, SLIM can perform even better while significantly reducing the quantity of information required for training compared to state-of-the-art language model-based fake news detection techniques. 

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