Says Who?: How News Presentation Format Influences Perceived Believability and the Engagement Level of Social Media Users

Kim, Antino; Dennis, Alan R.

doi:10.24251/hicss.2018.497

Cited by 27 publications

(13 citation statements)

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“…In their study to understand a computer-mediated counter argument system, Huang et al [27] measured confidence level in individuals with confirmation bias, before and after an interruption by the system. In the fake news domain, Kim and Dennis [18] measure perceived believability of individuals with confirmation bias in a news article. They found confirmation bias towards a headline in an online Page 5894 poster had a significant positive effect on belief in the poster's topic.…”

Section: Confirmation Bias and Beliefmentioning

confidence: 99%

“…Belief and confirmation bias are strongly tied together [26]. Several studies have shown the significant effect of confirmation bias on believability [18]. We control for confirmation bias in the proposed hypotheses from 1A to 1F.…”

Section: Theory Development and Model Conceptualization 31 What Makementioning

confidence: 99%

“…Reputation theory has shown most readers of fake news are affected by confirmation bias [18]. In their experiment, Kim and Dennis [18] evaluated the effects of story format, source ratings, and source reliability on believability by using headlines. They found that headlines in story format were less believable than headlines in news format.…”

Section: Introductionmentioning

confidence: 99%

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How does Information Spread? An Exploratory Study of True and Fake News

Suntwal¹,

Brown²,

Patton³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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The intentional and non-intentional use of social media platforms resulting in digital wildfires of misinformation has increased significantly over the last few years. However, the factors that influence this rapid spread in the online space remain largely unknown. We study how believability and intention to share information are influenced by multiple factors in addition to confirmation bias. We conducted an experiment where a mix of true and false articles were evaluated by study participants. Using hierarchical linear modelling to analyze our data, we found that in addition to confirmation bias, believability is influenced by source endorser credibility and argument quality, both of which are moderated by the type of informationtrue or false. Source likeability also had a positive main effect on believability. After controlling for belief and confirmation bias, intention to share information was affected by source endorser credibility and information source likeability. Prior Theory and ResearchAs fake news has broad definitions and purposes, understanding it is emerging as a significant research challenge. Technical and behavioral scientists are looking at this problem from multiple perspectives. Behavioral scientists have made significant progress in understanding how readability, placement of titles, etc. affect belief. However, a key aspect of fake news is its ability to persuade readers that it is true. The Elaboration Likelihood Model (ELM) is a key theory for understanding persuasive communication better. The two key routes of ELM, the central and the peripheral routes, explain how persuasive communication affects both individual belief and intention to share.

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Section: Confirmation Bias and Beliefmentioning

confidence: 99%

Section: Theory Development and Model Conceptualization 31 What Makementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How does Information Spread? An Exploratory Study of True and Fake News

Suntwal¹,

Brown²,

Patton³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…It's worth mentioning that we focus on those user-news interactions in which users agree with the news. For example, we only consider those users who share news pieces without comments, and these users share the same alignment of viewpoints with the news items [12]. We will introduce more details in Section 3.3.…”

Section: Problem Statementmentioning

confidence: 99%

“…Early fake news detection aims to give early alert of fake news, by only considering limited social context within a specific range of time delay of original news posted. Specifically, we change the delay time in [12,24,36,48,60,72,84,96] hours. From Figure 4, we can see that: 1) generally, the detection performance is getting better when the delay time increase for those methods using social context information, which indicates that more social engagements of users on social media provide more additional information for fake news detection; 2) The proposed TriFN consistently achieves best performances on both datasets for accuracy and F1, which demonstrate the importance of embedding user-news interactions to capture effective feature representations; and 3) Even in the very early stage after fake news has been published, TriFN can already achieve good performance.…”

Section: Early Fake News Detectionmentioning

confidence: 99%

Beyond News Contents

Shu

Wang

Liu

2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

374

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Social media is becoming popular for news consumption due to its fast dissemination, easy access, and low cost. However, it also enables the wide propagation of fake news, i.e., news with intentionally false information. Detecting fake news is an important task, which not only ensures users receive authentic information but also helps maintain a trustworthy news ecosystem. The majority of existing detection algorithms focus on finding clues from news contents, which are generally not effective because fake news is often intentionally written to mislead users by mimicking true news. Therefore, we need to explore auxiliary information to improve detection. The social context during news dissemination process on social media forms the inherent tri-relationship, the relationship among publishers, news pieces, and users, which has potential to improve fake news detection. For example, partisan-biased publishers are more likely to publish fake news, and low-credible users are more likely to share fake news. In this paper, we study the novel problem of exploiting social context for fake news detection. We propose a tri-relationship embedding framework TriFN, which models publisher-news relations and user-news interactions simultaneously for fake news classification. We conduct experiments on two real-world datasets, which demonstrate that the proposed approach significantly outperforms other baseline methods for fake news detection.

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