Spread of misinformation online: Simulation impact of social media newsgroups

Safieddine, Fadi; Dordevic, Milan; Pourghomi, Pardis

doi:10.1109/sai.2017.8252201

Cited by 15 publications

(15 citation statements)

References 20 publications

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“…However, possibly the prime influencers in the spread of fake news are newsgroups, cyber-bots, and user influencers. In reviewing the influence of social media newsgroup in the dissemination of misinformation, the work of [3] showed the impact of what was found earlier as the protagonist of influencers. The tests demonstrated how authentication algorithms could significantly reduce the dissemination of misinformation on social media.…”

Section: Social Media Networkmentioning

confidence: 96%

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Identifying Fake News from the Variables that Governs the Spread of Fake News

Dordevic

Pourghomi

Safieddine

2020

2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA

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Several researchers have attempted to investigate the processes that govern and support the spread of fake news. This paper collates and identifies these variables. This paper then categorises these variables based on three key players that are involved in the process: Users, Content, and Social Networks. The authors conducted an extensive review of the literature and a reflection on the key variables that are involved in the process. The paper has identified a total of twenty-seven variables. Then the paper presents a series of tasks to mitigate or eliminate these variables in a holistic process that could be automated to reduce or eliminate fake news propagation. Finally, the paper suggests further research into testing the method in lab conditions.

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Section: Social Media Networkmentioning

confidence: 96%

“…The spread of fake news has been likened to the spread of disease [1], [2], [3], & [7]. And medical researchers attempt to understand the range of a given disease by learning how it starts, transmit, behave, disappears, those it infects and those it does not affect.…”

Section: Introductionmentioning

confidence: 99%

Identifying Fake News from the Variables that Governs the Spread of Fake News

Dordevic

Pourghomi

Safieddine

2020

2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA

Self Cite

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“…The tracked data often lack specificity [130], can be misinterpreted and/or distorted, and subsequently promoted by influential personas without appropriate training or content expertise [131,132]. Reversing damage caused by distortion of the facts and misinterpretation can be challenging [133][134][135][136][137]. For example, the controversy regarding the alleged association between childhood vaccination and autism exemplifies how concerns of global nature can be distorted in a highly publicized fashion [138][139][140].…”

Section: International Health Security: Potentially Harmful Aspects Omentioning

confidence: 99%

The Evolving Interplay between Social Media and International Health Security: A Point of View

Conti¹,

Desai²,

Stawicki³

et al. 2021

Contemporary Developments and Perspectives in International Health Security - Volume 1

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Human communication and interaction had been rapidly evolving with the advent and continuing influence of social media (SM) thereby accelerating information exchange and increasing global connectivity. Despite clear advantages, this new technology can present unintended consequences including medical misinformation and "fake news." Although International Health Security (IHS) stands to benefit tremendously from various SM platforms, high-level decision-makers and other stakeholders must also be aware of the dangers related to its intentional and unintentional misuse (and abuse). An overview of SM utility in fighting disease, disseminating life-saving information, and organizing people and teams in a constructive fashion is discussed herein. The potential negatives associated with SM misuse, including intentional and unintentional misinformation, as well as the ability to organize people in a disruptive fashion, will also be presented. Our treatise will additionally outline how deliberate misinformation may lead to harmful behaviors, public health panics, and orchestrated patterns of distrust. In terms of both its affirmative and destructive considerations, SM can be viewed as an asymmetric influencing force, with observed effects (whether beneficial or harmful) being disproportionately greater than the cost of the intervention.

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“…New coronavirus disease 2019 (COVID- 19) is an infectious disease that recently appeared in December 2019 as lung syndrome. The symptoms mostly include fever, cough, dyspnea and viral pneumonia [1].…”

Section: Introductionmentioning

confidence: 99%

“…The second line of research was to apply text mining methods to social network data. These include sentiment analysis, e.g., [8]- [14], and misinformation modeling, forecasting and detection, e.g., [15]- [19]. The third line of research was to diagnose COVID-19 patients using machine and deep learning methods, e.g., [20]- [24].…”

Section: Introductionmentioning

confidence: 99%

Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic

Al-Shargabi

Selmi

2021

IEEE Access

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Twitter data have been used widely in times of pandemics and crises. Currently, the world is suffering from the outbreak of new coronavirus disease 2019 , in which the COVID-19 virus has infected more than one hundred and twenty million people worldwide with more than two million deaths. Consequently, people tend to share COVID-19-related content on social media extensively. As a new pandemic, only a small number of studies have been conducted to analyze COVID-19-related tweets, and even fewer were meant for Arabic tweets. This research explores the influence of the COVID-19 pandemic on Saudi users' tweeting behavior. In particular, the research adopts a social network analysis (SNA) for COVID-19 Arabic tweets. This approach is interesting, as it is based on analysis of social structures, such as Twitter users and the relationships among them, through the use of networks and graph theory without the contents of the tweets themselves. Based on 8905 collected Arabic tweets, this research resulted in three main contributions: 1) a visualization of the social network for COVID-19 tweets of Saudi users, 2) an identification of information sources that Twitter users employ during the COVID-19 pandemic, and 3) an identification of the most popular influencers among users of COVID-19 tweets. The results of this study may help identify the most popular Twitter influencers and those who deliver the information to Twitter users, utilizing them to increase awareness and deliver information and instructions to overcome the COVID-19 pandemic.

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Spread of misinformation online: Simulation impact of social media newsgroups

Cited by 15 publications

References 20 publications

Identifying Fake News from the Variables that Governs the Spread of Fake News

Identifying Fake News from the Variables that Governs the Spread of Fake News

The Evolving Interplay between Social Media and International Health Security: A Point of View

Social Network Analysis and Visualization of Arabic Tweets During the COVID-19 Pandemic

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