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Developing a Stressor Strain Outcome Model That Predicts Fake News Sharing Behaviour on Social Media: The Mediating Role of Social Media Exhaustion

压力源 社会化媒体 心理学 拉伤 社会心理学 结果(博弈论) 计算机科学 临床心理学 医学 万维网 内科学 经济 微观经济学
作者
Luhui Hua,Yuan Jing,Xiaocui Sun,Lujia Tian,Oberiri Destiny Apuke
出处
期刊:International Journal of Human-computer Interaction [Taylor & Francis]
卷期号:: 1-14
标识
DOI:10.1080/10447318.2024.2352927
摘要

Despite the growing corpus of knowledge on fake news sharing, nothing much is known about the relationship between the dissemination of fake news and social media exhaustion from an empirical standpoint. There is limited knowledge that exists to show if social media exhaustion could contribute to fake news-sharing behaviour. To address the gaps in the literature, this study created and evaluated a stressor strain outcome model that forecasts social media spread of fake news. It looked at the contributing elements of social media exhaustion before connecting it to disseminating fake news online. It also looked at how social media exhaustion functions as a mediator between its antecedents and fake news-sharing activity. Data was obtained from 1650 social media users using an online survey. Smart PLS structural equation modelling was used to examine the data. Our results, which drew on the stressor strain outcome model, demonstrated that, in the context of fake news-sharing behaviour, social media usage intensity is the most important predictor of social media exhaustion. Social media exhaustion was also predicted by information irrelevance, social media overload, the fear of missing out, and information overload. Also, social media tiredness predicts how people would share misleading news. Further research indicates that the link between information relevance, social overload, fear of missing out, information overload, and the dissemination of fake news is fully mediated by social media exhaustion. Conversely, it partially mediated the link between the frequency of using social media and the dissemination of false information. Our research provides recommendations to social media businesses and developers who wish to reduce concerns about social media tiredness and the spread of fake news.
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