Incorporating message format into user evaluation of microblog information credibility: A nonlinear perspective

Yin, Chunxiao; Zhang, Xiaofei

doi:10.1016/j.ipm.2020.102345

Cited by 32 publications

(29 citation statements)

References 45 publications

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“…Surprisingly, information richness strengthens the effect of information prospectiveness on PEB. This finding indicates that multimedia content can increase information credibility [ 89 ] and encourage public engagement on social media during health emergencies.…”

Section: Discussionmentioning

confidence: 99%

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Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Zhou

Luo

et al. 2022

Decision Support Systems

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This paper investigates how information timeliness and richness affect public engagement using text data from China's largest social media platform during times of the COVID-19 pandemic. We utilize a similarity calculation method based on natural language processing (NLP) and text mining to evaluate three dimensions of information timeliness: retrospectiveness, immediateness, and prospectiveness. Public engagement is divided into breadth and depth. The empirical results show that information retrospectiveness is negatively associated with public engagement breadth but positively with depth. Both information immediateness and prospectiveness improved the breadth and depth of public engagement. Interestingly, information richness has a positive moderating effect on the relationships between information retrospectiveness, prospectiveness, and public engagement breadth but no significant effects on immediateness; meanwhile, it has a negative moderating effect on the relationship between retrospectiveness and depth but a positive effect on immediateness, prospectiveness. In the extension analysis, we constructed a supervised NLP model to identify and classify health emergency-related information (epidemic prevention and help-seeking) automatically. We find that public engagement differs in the two emergency-related information categories. The findings can promote a more responsive public health strategy that magnifies the transfer speed for critical information and mitigates the negative impacts of information uncertainty or false information.

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Section: Discussionmentioning

confidence: 99%

“…Media richness theory (MRT) indicates that richness can affect information credibility and argument quality [ 89 ]. Nowadays, technologies enable people to create and use multimedia content easily.…”

Section: Introductionmentioning

confidence: 99%

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Zhou

Luo

et al. 2022

Decision Support Systems

View full text Add to dashboard Cite

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“…However, despite the impressive role of SNSs during a crisis, users are still worried about the credibility of information and its anonymity contributors ( Liao and Mak, 2019 ). The source’s credibility of a message is essential for the public as it reflects the trustworthiness of an individual and group who creates a message ( Yin and Zhang, 2020 ). Prior studies show that the qualities of a credible source such as attractiveness, trustworthiness, and expertise have a significant persuasion effect on people’s beliefs, attitudes, and behaviors ( Ryu et al, 2018 ; Sokolova and Kefi, 2020 ).…”

Section: Theoretical Background and Hypothesis Buildingmentioning

confidence: 99%

Source Credibility and the Information Quality Matter in Public Engagement on Social Networking Sites During the COVID-19 Crisis

Shah

Lü

2022

Front. Psychol.

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During the coronavirus disease 2019 (COVID-19) pandemic, people use social networking sites (SNSs) to seek social support, ease the move toward the social distance, and communicate and engage with one another. However, there is growing evidence that trustworthiness and quality of information can affect individuals’ online engagement behaviors. This study proposes a theoretical model to test people’s online engagement during the COVID-19 pandemic by applying the elaboration likelihood model (ELM). Through a questionnaire survey of 630 SNS users, the study examines whether and how source credibility and information quality affect people’s online engagement during the COVID-19 pandemic. The model was tested using structural equation modeling. The findings show that source credibility and information quality have a significantly positive relationship with perceived benefit, while negative and significantly associated with perceived risk. Furthermore, perceived benefit is a stronger predictor of online public engagement than the perceived risk. To improve online public engagement as a crisis response strategy, careful source selection and careful generation of online crisis information should not be overlooked.

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“…Because we outlined how appearance in a profile picture and having a regular provider affect credibility, these 2 operate as indirect predictors of engagement. Credible sources are more likely to persuade others by changing their attitudes and behavior [37], with prior research showing the credibility of a source on social media to be associated with the strength of the resulting attitudes and behavioral intentions [38][39][40][41].…”

Section: Hypothesis 3: As Ratings Of Physician Credibility (Competenc...mentioning

confidence: 99%

Factors Affecting Physicians’ Credibility on Twitter When Sharing Health Information: Online Experimental Study

Ferrell¹,

Campos-Castillo²

2022

JMIR Infodemiology

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Background Largely absent from research on how users appraise the credibility of professionals as sources for the information they find on social media is work investigating factors shaping credibility within a specific profession, such as physicians. Objective We address debates about how physicians can show their credibility on social media depending on whether they employ a formal or casual appearance in their profile picture. Using prominence-interpretation theory, we posit that formal appearance will affect perceived credibility based on users' social context—specifically, whether they have a regular health care provider. Methods For this experiment, we recruited 205 social media users using Amazon Mechanical Turk. We asked participants if they had a regular health care provider and then randomly assigned them to read 1 of 3 Twitter posts that varied only in the profile picture of the physician offering health advice. Next, we tasked participants with assessing the credibility of the physician and their likelihood of engaging with the tweet and the physician on Twitter. We used path analysis to assess whether participants having a regular health care provider impacted how the profile picture affected their ratings of the physician’s credibility and their likelihood to engage with the tweet and physician on Twitter. Results We found that the profile picture of a physician posting health advice in either formal or casual attire did not elicit significant differences in credibility, with ratings comparable to those having no profile image. Among participants assigned the formal appearance condition, those with a regular provider rated the physician higher on a credibility than those without, which led to stronger intentions to engage with the tweet and physician. Conclusions The findings add to existing research by showing how the social context of information seeking on social media shapes the credibility of a given professional. Practical implications for professionals engaging with the public on social media and combating false information include moving past debates about casual versus formal appearances and toward identifying ways to segment audiences based on factors like their backgrounds (eg, experiences with health care providers).

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Incorporating message format into user evaluation of microblog information credibility: A nonlinear perspective

Cited by 32 publications

References 45 publications

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

Source Credibility and the Information Quality Matter in Public Engagement on Social Networking Sites During the COVID-19 Crisis

Factors Affecting Physicians’ Credibility on Twitter When Sharing Health Information: Online Experimental Study

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