Spread of misinformation on social media: What contributes to it and how to combat it

Chen, Sijing; Xiao, Lu; Kumar, Akit

doi:10.1016/j.chb.2022.107643

Cited by 69 publications

(36 citation statements)

References 239 publications

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“…People may share misinformation for multiple reasons (S. Chen, Xiao, et al, 2023). For example, factors that relate to the story itself-including perceiving the information to be new and eye-catching (X.…”

Section: Reasons For Sharing Misinformationmentioning

confidence: 99%

“…Researchers have proposed and studied a wide range of factors that potentially underlie decisions to share misinformation online (S. Chen, Xiao, et al, 2023;Metzger et al, 2021;Van Bavel et al, 2021). However, research in this area is complicated by the fact that when an individual shares a piece of false information online, it is often done in the mistaken belief that it is true.…”

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confidence: 99%

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confidence: 99%

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Why Do People Share Political Information and Misinformation Online? Developing a Bottom-Up Descriptive Framework

Perach,

Joyner,

Husbands

et al. 2023

Social Media + Society

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Social media users are key actors in the spreading of misleading or incorrect information. To develop an integrative parsimonious summary of social media users’ own accounts of motives for sharing political information, we conducted: (1) a literature review of motives for personally sharing false information as reported by social media users and (2) qualitative research concerning these motives using an innovative, ecologically valid method. Based on our findings, we developed a pool of items evaluating social media users’ motives for sharing false political information, which we then tested and analyzed the dimensionality of in (3) a pre-registered questionnaire-based study to identify key clusters of users’ own accounts of motives for sharing both true and false political information. The current findings show that there are distinct sets of motives people report for their misinformation sharing behavior: prosocial activism, attack or manipulation of others, entertainment, awareness, political self-expression, and fighting false information. Also, these sets of motives are associated with variables known to predict sharing misinformation, and some of these sets predict social media users’ self-reports of having shared misinformation in the past. Our findings highlight and elaborate on users’ motives that reflect a concern with “making things better” and acting in a manner that is beneficial to society as a whole, and suggest that different interventions may be required to combat misinformation sharing driven by different motives. A potential set of 18 items that could be used in questionnaires measuring motivations for sharing political news online is described.

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Section: Reasons For Sharing Misinformationmentioning

confidence: 99%

mentioning

confidence: 99%

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Why Do People Share Political Information and Misinformation Online? Developing a Bottom-Up Descriptive Framework

Perach,

Joyner,

Husbands

et al. 2023

Social Media + Society

View full text Add to dashboard Cite

show abstract

“…The situation surrounding the spread of misinformation online is not much different. Even though measuring the extent of misinformation on social media can be challenging due to its various forms and often ephemeral nature, researchers have extensively documented its impact, spread, and prevalence across social media platforms (e.g., Chen, Xiao & Kumar, 2023;van der Linden, 2022). This phenomenon is particularly evident in discussions of politically polarized topics, such as gun control (Williams, 2022), climate change (Falkenberg et al, 2022), abortion (Pagoto et al, 2023), vaccination (Gruzd et al, 2023), refugees (Zhen et al, 2023), and more recently, the COVID-19 pandemic (Gruzd et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Trust and Safety on Social Media: Understanding the Impact of Anti-Social Behavior and Misinformation on Content Moderation and Platform Governance

Gruzd,

Soares,

Mai

2023

Social Media + Society

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The Special Issue on Trust and Safety on Social Media delves into two pressing and interlinked concerns: the growing prevalence of anti-social behavior and the widespread presence of misinformation within and across various social media platforms. The collection of articles featured in the issue collectively examines factors that contribute to these concerns and proposes potential strategies to mitigate their negative impact on social media users and society. The articles included in the issue are extended versions of research first presented at the 2022 International Conference on Social Media & Society (#SMSociety), organized by the Social Media Lab at Toronto Metropolitan University.

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“…Generative technologies like Generative Adversarial Networks (GANs) or Generative Pre-trained Transformer (GPT) along with appropriate training data, a user can generate any media using simple prompts resulting in the widespread dissemination of misinformation. 3 AI-generated images have been a recent but rapidly growing phenomenon. 4 From advertisement to art created in social media, AI-generated images have found applications in an increasing number of information platforms.…”

Section: Introductionmentioning

confidence: 99%

AUSOME: authenticating social media images using frequency analysis

Poredi,

Nagothu,

Chen

2023

Disruptive Technologies in Information Sciences VII

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Ever since human society entered the age of social media, every user has had a considerable amount of visual content stored online and shared in variant virtual communities. As an efficient information circulation measure, disastrous consequences are possible if the contents of images are tampered with by malicious actors. Specifically, we are witnessing the rapid development of machine learning (ML) based tools like DeepFake apps. They are capable of exploiting images on social media platforms to mimic a potential victim without their knowledge or consent. These content manipulation attacks can lead to the rapid spread of misinformation that may not only mislead friends or family members but also has the potential to cause chaos in public domains. Therefore, robust image authentication is critical to detect and filter off manipulated images. In this paper, we introduce a system that accurately AUthenticates SOcial MEdia images (AUSOME) uploaded to online platforms leveraging spectral analysis and ML. Images from DALL-E 2 are compared with genuine images from the Stanford image dataset. Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are used to perform a spectral comparison. Additionally, based on the differences in their frequency response, an ML model is proposed to classify social media images as genuine or AI-generated. Using real-world scenarios, the AUSOME system is evaluated on its detection accuracy. The experimental results are encouraging and they verified the potential of the AUSOME scheme in social media image authentications.

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Spread of misinformation on social media: What contributes to it and how to combat it

Cited by 69 publications

References 239 publications

Why Do People Share Political Information and Misinformation Online? Developing a Bottom-Up Descriptive Framework

Why Do People Share Political Information and Misinformation Online? Developing a Bottom-Up Descriptive Framework

Trust and Safety on Social Media: Understanding the Impact of Anti-Social Behavior and Misinformation on Content Moderation and Platform Governance

AUSOME: authenticating social media images using frequency analysis

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