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    The Destructive Impact of Misinformation on Social Media

    The ease of generating and spreading misinformation on social media has led to its destructive impact on various aspects of our lives, including public health, politics, climate change, and the economy. The COVID-19 pandemic is an example of how misinformation has led to inappropriate protective measures and psychological issues among the public. In politics, the spread of misinformation can increase political fragmentation and lower trust in the government. Misinformation about climate change has confused the public and hindered responses to mitigation policies, while economy-related misinformation can affect corporations’ reputations and consumers’ purchase intentions. Therefore, there is a need for a careful and systematic review of relevant literature to understand factors that contribute to the spread of misinformation and develop strategies to confront it.

    Concepts related to misinformation

    The different terms and concepts related to misinformation can be classified into three types of information disorder based on intention and facticity: misinformation (false information spread without intent to mislead), disinformation (false information deliberately produced to cause harm), and malinformation (true information propagated to cause harm).

    Key factors driving the spread of misinformation on social media

    Based on the SMCR model, the factors that impact the dissemination of misinformation on social media can be categorized into four groups: source-related, message-related, context-related, and receiver-related factors. When an authoritative source delivers a persuasive message that resonates with a receptive audience in a favorable environment, misinformation can rapidly spread.

    The focus of research on combating misinformation on social media has been on source, message, and context-related factors, but the role of receiver-related factors, particularly the receiver’s persuasion knowledge, has been largely unexplored. The persuasion knowledge model suggests that individuals’ topic knowledge, agent knowledge, and persuasion knowledge affect how they respond to persuasion attempts, including misinformation. Most research has focused on investigating the influence of topic and agent knowledge on misinformation-spreading behaviors, but the role of persuasion knowledge in the process remains largely unknown. Future research should explore how individuals’ persuasion knowledge affects their awareness of the persuasion attempt behind misinformation and their tendency to spread it on social media, and how to effectively increase social media users’ persuasion knowledge.

    The authors note that there are few studies investigating the interaction effects between receiver-related factors and other factors in the spread of misinformation. They argue that such studies can provide important insights into the moderating and/or mediating effects of receiver-related factors. For example, an individual’s worry about COVID-19 mediated the relationship between peer communication and misinformation sharing. The authors suggest that examining the interaction effects among receiver-related factors may also help disentangle inconsistencies in previous findings, such as the contradictory results on the relationship between neuroticism and vulnerability to misinformation in different population groups. The article discusses five categories of solutions that have been explored to combat misinformation on social media: message, source, network, policy, and education-based approaches. However, the article also highlights several areas that have been largely overlooked, including the explainability of message, source, and network-based technical solutions, the need to consider individuals’ motivation and emotional state in the design of educational interventions, and the importance of verifying the effectiveness of proposed strategies in real cases.

    The research article debates the importance of explainable artificial intelligence (XAI) in fact-checking and detecting misinformation. While high-performance AI-based fact-checking systems can identify potential misinformation, it is crucial to explain why a piece of message is detected as misinformation to build trust and credibility. XAI has gained attention in this field, but studies still focus on evaluating the performance of the system in predicting misinformation, except for a few that evaluate the explainability of the system and the impact of generated explanations on social media users. Moreover, highlights XAI-based fact-checking and misinformation detection as a future trend in combating the spread of misinformation. In addition, examines the importance of motivation and emotional state in interventions aimed at improving individuals’ ability to identify misinformation on social media. While education-based interventions like information literacy education and educational games can improve knowledge and cognitive skills, they may not address individuals’ motivation to pursue the truth and emotional state when encountering information. The authors suggest interventions that motivate individuals to pursue truth and address their emotional state, such as nurturing scientific identity and teaching people to reflect on their emotions when browsing social media content.

    More information can be found in the scientific study titled “Spread of misinformation on social media: What contributes to it and how to combat it” published by Sijing Chen, Lu Xiao, Akit Kumar.

    Daniel Mican
    Daniel Micanhttps://scholar.google.com/citations?user=OJVwSwwAAAAJ
    Daniel Mican is an associate professor and PhD supervisor in the field of Business Information Systems. His research interests are in the area of recommendation systems, web usage mining, collective intelligence, user behavior, and social media. In his free time, he writes articles for Gherf.com.
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