1/3/2024 0 Comments The honeycomb![]() Using structural equations modeling we tested the research hypothesis and the proposed conceptual model. ![]() A convenience sample was chosen, and 922 valid responses were obtained. We conducted an empirical investigation through a questionnaire-based online survey among social media users of the most popular SNS. Relevant studies from fake news literature, online trust, and social media usage were also included to develop the hypothesis and conceptual model. ![]() Our theoretical framework enhances flow theory, which is conceptualized as a sequential process, involving social media users' intrinsic interest, concentration, perceived control, enjoyment, and time distortion. The purpose of this study is to examine the relationship between users' optimal experience while surfing SNS, the sharing behavior of fake news about companies, online trust, and increased social media usage. This is one of the first papers which focus on both explaining and predicting click-baitiness and click-bait virality. The paper helps social media managers create a mechanism to detect clickbaits and also predict which ones of them can become viral so that corrective measures can be taken. The paper also provides explainability to the econometric and machine learning predictive models, thus performing methodological contribution too. It focuses on the differential roles of the social media post, the article shared and the source in explaining click-baitiness and click-bait virality via psycho-linguistic framework. The paper contributes towards the literature of dark behavior in social media at large and click-bait prediction and explanation in particular. We find that language formality, readability, sentiment scores, and proper noun usage of social media posts and various parts of the target article play differential and important roles in click-baitiness and click-bait virality. We have used 17745 tweets from Twitter with 4370 click-baits from the top 27 publishers and applied econometric along with machine learning methods to explain and predict click-baitiness and click-bait virality. In this study, we try to explore how the contents of the social media posts and the article can be used to explain and predict social media posts and the virality of click-bait. Therefore, click-bait detection and prediction of click-bait virality have become important challenges for social media platforms to keep the platform click-bait free and give a better user experience. Not only viral websites but also mainstream publishers, such as news channels, use click-baits for generating traffic. In the age of social media, when publishers are vying for consumer attention, click-baits have become very common. ![]() These results have significant theoretical and practical implications. The study results also suggest that social media users who engage in active corrective action are unlikely to share fake news due to lack of time. However, authenticating news before sharing had no effect on sharing fake news due to lack of time and religiosity. The study results suggest that instantaneous sharing of news for creating awareness had positive effect on sharing fake news due to lack of time and religiosity. Two data sets obtained from cross-sectional surveys with 471 and 374 social media users were utilized to test the proposed model. Age and gender were the control variables. Thereafter, research model hypothesizing the association between these behaviours was proposed using the honeycomb framework and the third-person effect hypothesis. To begin with, qualitative data from 58 open-ended essays was analysed to identify six behavioural manifestations associated with sharing fake news. This study adopts a mixed-method approach to explore fake-news sharing behaviour. Sharing of fake news on social media platforms is a global concern, with research offering little insight into the motives behind such sharing.
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