University of Kwazulu-Natal

The Spread Mechanism Of Misinformation On Social Media

Bonisiwe Shabane
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the spread mechanism of misinformation on social media

Corresponding author. Institute for Physical Acivity and Nutrition, School of Health and Social Development, Deakin University, 75 Pigdons Road, Geelong, Victoria 3216, Australia. E-mail: e.denniss@deakin.edu.au This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Misinformation has been identified as a major threat to society and public health. Social media significantly contributes to the spread of misinformation and has a global reach.

Health misinformation has a range of adverse outcomes, including influencing individuals’ decisions (e.g. choosing not to vaccinate), and the erosion of trust in authoritative institutions. There are many interrelated causes of the misinformation problem, including the ability of non-experts to rapidly post information, the influence of bots and social media algorithms. Equally, the global nature of social media, limited commitment for action from social media giants, and rapid technological advancements hamper progress for improving information quality and accuracy in this setting. In short, it is a problem that requires a constellation of synergistic actions aimed at social media users, content creators, companies, and governments. A public health approach to social media-based misinformation that includes tertiary, secondary, and primary prevention may help address immediate impacts, long-term consequences, and root causes of misinformation.

Tertiary prevention to ‘treat’ this problem involves increased monitoring, misinformation debunking, and warning labels on social media posts that are at a high risk of containing misinformation. Secondary prevention strategies include nudging interventions (e.g. prompts about preventing misinformation that appear when sharing content) and education to build media and information literacy. Finally, there is an urgent need for primary prevention, including systems-level changes to address key mechanisms of misinformation and international law to regulate the social media industry. Anything less means misinformation—and its societal consequences—will continue to spread. Keywords: misinformation, social media, disinformation, health information, digital policy

Social media-based misinformation threatens public health through the provision of misleading information and undermines trust in credible experts and organizations. Furthermore, social media organizations need to provide corrections to misinformation and point out that information may be wrong or misleading. Second, the findings highlight the importance of media literacy education (Chen et al., 2022; Fendt et al., 2023). These media literacy programs should promote critical thinking skills and provide concrete strategies and techniques individuals can deploy for fact-checking and verifying information. Social media platforms, designed to facilitate global communication and information dissemination, have paradoxically become fertile ground for the propagation of misinformation. The scale and speed at which spurious or misleading content can traverse these networks presents a significant challenge to informed decision-making and societal trust.

This article dissects the technological and behavioral mechanisms that contribute to the spread of misinformation on social media, examining the underlying algorithms, user psychology, and platform architectures that exacerbate the problem. At the heart of the issue lies the architecture of social media algorithms. These complex systems are designed to maximize user engagement, often prioritizing content that elicits strong emotional responses, regardless of its factual accuracy. This creates a positive feedback loop where sensationalized or emotionally charged content, including misinformation, receives disproportionate visibility. The core mechanisms driving this algorithmic bias include: The following table illustrates the impact of algorithmic amplification:

Beyond algorithmic bias, malicious actors exploit social media platforms through the use of bots and automated accounts. These accounts, often controlled programmatically, can artificially inflate the popularity of misinformation, manipulate trending topics, and spread disinformation campaigns. The researchers sought to understand how the reward structure of social media sites drives users to develop habits of posting misinformation on social media. (Photo/AdobeStock) The USC-led study of more than 2,400 Facebook users suggests that platforms — more than individual users — have a larger role to play in stopping the spread of misinformation online. USC researchers may have found the biggest influencer in the spread of fake news: social platforms’ structure of rewarding users for habitually sharing information.

The team’s findings, published Monday by Proceedings of the National Academy of Sciences, upend popular misconceptions that misinformation spreads because users lack the critical thinking skills necessary for discerning truth from falsehood or because... Just 15% of the most habitual news sharers in the research were responsible for spreading about 30% to 40% of the fake news. Misinformation can spread rapidly and on multiple platforms. Bots, trolls, social media and message boards - even word of mouth - can spread misinformation, disinformation and propaganda. And with the exponential growth of AI, misleading content is increasingly common in everything from product reviews to social media posts. Below are information and tools to help you learn to recognize and fight the bots and trolls that help spread "fake news" !

Per a 2017 study in the University of Michigan Journal of Law Reform, the purveyors of bots and trolls typically do not seek a specific outcome; rather, they deploy them to sow chaos, confusion... They typically can be found in online message boards and social media outlets, and can be deployed in a variety of situations. According to information scientist Mike Caulfield, "The latest AI language tools are powering a new generation of spammy, low-quality content that threatens to overwhelm the internet unless online platforms and regulators find ways to... Stay tuned. A Twitter bot is a type of automated software that controls a Twitter account. Automation of such accounts is governed by a set of rules governing use.

Improper usage includes circumventing automation rate limits, a key indicator of nefarious bot behavior. Experts use multiple criteria to judge whether a particular X (formerly Twitter) account is a bot. Learn to recognize some key telltale signs!

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