Effects of fact‐checking warning labels and social endorsement cues on climate change fake news credibility and engagement on social media

Koch, T.; Frischlich, Lena; Lermer, Eva

doi:10.1111/jasp.12959

Cited by 27 publications

(12 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the source is credible, meaning that it has a proven track record of providing accurate and unbiased information, the news story is more likely to be credible. On the other hand, if the source is unreliable or has a history of publishing inaccurate or biased information, the news story may be less credible or even misleading [52]. In the digital age, where anyone can publish news or information online, it is important to be critical of sources and to verify information before accepting it as true.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Towards Fake News Detection: A Multivocal Literature Review of Credibility Factors in Online News Stories and Analysis Using Analytical Hierarchical Process

Abrar,

Khan,

Khan

et al. 2023

Electronics

View full text Add to dashboard Cite

Information and communication technologies have grown globally in the past two decades, expanding the reach of news networks. However, the credibility of the information is now in question. Credibility refers to a person’s belief in the truth of a subject, and online readers consider various factors to determine whether a source is trustworthy. Credibility significantly impacts public behaviour, and less credible news spreads faster due to people’s interest in emotions like fear and disgust. This can have negative consequences for individuals and economies. To determine the credibility factors in digital news stories, a Multivocal Literature Review (MLR) was conducted to identify relevant studies in both white and grey literature. A total of 161 primary studies were identified from published (white) literature and 61 were identified from unpublished (grey) literature. As a result, 14 credibility factors were identified, including “number of views”, “reporter reputations”, “source information”, and “impartiality”. These factors were then analysed using statistical tests and the Analytic Hierarchy Process (AHP) for decision-making to determine their criticality and importance in different domains.

show abstract

Section: Resultsmentioning

confidence: 99%

“…This is especially important in the context of social media, where news stories can spread quickly and without any fact-checking or editorial oversight. By paying attention to the source of a digital news story, readers can better assess its credibility and make informed decisions about whether or not to believe it [50][51][52].…”

Section: Resultsmentioning

confidence: 99%

Towards Fake News Detection: A Multivocal Literature Review of Credibility Factors in Online News Stories and Analysis Using Analytical Hierarchical Process

Abrar,

Khan,

Khan

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Although these studies provide some evidence that endorsement information may influence belief in false information, other studies have found no effect of perceived endorsement on (mis)information belief (Koch et al, 2023;Mena et al, 2020). Further, almost all studies have exclusively focused on positive engagement (i.e., likes and shares), neglecting how negative engagement (which may be perceived as disendorsement; e.g., dislikes) might impact belief.…”

Section: Introductionmentioning

confidence: 82%

Others (dis‐)endorse this so it must (not) be true: High relative endorsement increases perceived misinformation veracity but not correction effectiveness

Butler,

Fay,

Ecker

2023

Applied Cognitive Psychology

View full text Add to dashboard Cite

People increasingly rely on social‐media platforms to access information; thus, understanding how platform characteristics influence belief in misinformation is important. Recent findings indicate perceived social endorsement of information (e.g., number of likes) can influence misinformation belief and correction acceptance. However, how the influence of endorsement may be modulated by concurrent disendorsement information (e.g., dislikes) is unclear. Across two experiments, we assessed the influence of relative endorsement on misinformation belief and correction acceptance. Experiment 1 exposed participants to claims and fact‐checks with a high or low likes‐to‐dislikes ratio. Experiment 2 simplified the relative‐endorsement information into a single value (i.e., a percentage). Results suggest high relative social endorsement of misinformation significantly increases misinformation belief, particularly when the endorsement information is presented as a single value. Conversely, relative endorsement had a negligible impact on correction effectiveness. This suggests perceived relative endorsement may influence belief primarily when other cues of information veracity are unavailable.

show abstract

“…The sentences are extracted from the web server, and a deep learning system keeps track of the assertions made. Therefore, using manual and automatic fact-checking, our strategy used fact-checking to identify and match propaganda news claims [63]. As input documents, the architectural pipeline transforms the news from the source file.…”

Section: Protext Labeling and Matching Claimsmentioning

confidence: 99%

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

et al. 2023

View full text Add to dashboard Cite

Social media, fake news, and different propaganda strategies have all contributed to an increase in misinformation online during the past ten years. As a result of the scarcity of high-quality data, the present datasets cannot be used to train a deep-learning model, making it impossible to establish an identification. We used a natural language processing approach to the issue in order to create a system that uses deep learning to automatically identify propaganda in news items. To assist the scholarly community in identifying propaganda in text news, this study suggested the propaganda texts (ProText) library. Truthfulness labels are assigned to ProText repositories after being manually and automatically verified with fact-checking methods. Additionally, this study proposed using a fine-tuned Robustly Optimized BERT Pre-training Approach (RoBERTa) and word embedding using multi-label multi-class text classification. Through experimentation and comparative research analysis, we address critical issues and collaborate to discover answers. We achieved an evaluation performance accuracy of 90%, 75%, 68%, and 65% on ProText, PTC, TSHP-17, and Qprop, respectively. The big-data method, particularly with deep-learning models, can assist us in filling out unsatisfactory big data in a novel text classification strategy. We urge collaboration to inspire researchers to acquire, exchange datasets, and develop a standard aimed at organizing, labeling, and fact-checking.

show abstract

Effects of fact‐checking warning labels and social endorsement cues on climate change fake news credibility and engagement on social media

Cited by 27 publications

References 90 publications

Towards Fake News Detection: A Multivocal Literature Review of Credibility Factors in Online News Stories and Analysis Using Analytical Hierarchical Process

Towards Fake News Detection: A Multivocal Literature Review of Credibility Factors in Online News Stories and Analysis Using Analytical Hierarchical Process

Others (dis‐)endorse this so it must (not) be true: High relative endorsement increases perceived misinformation veracity but not correction effectiveness

Robust Benchmark for Propagandist Text Detection and Mining High-Quality Data

Contact Info

Product

Resources

About