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The spread of misinformation and disinformation in news platforms and social media outlets
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Problem Statement- Disinformation and misinformation has become a global crisis on social media networks and news platforms due to their critical growth in this new age of internet technology. The widespread disinformation and misinformation in news platforms and social media outlets can affect individuals’ decision-making. Despite the current rate of identifying disinformation, misinformation within news platforms is nontrivial owing to its difficulty, variety, multi-modality, and fact-checking or commentary prices. This research aims to cover the efforts to address this gap by exploring individuals’ behavior towards authentication and sharing of fake news.
Annotated Bibliography
Aldwairi, M., & Alwahedi, A. (2018). Detecting fake news in social media networks. Procedia Computer Science, 141, 215-222. https://doi.org/10.1016/j.procs.2018.10.171
Detecting Fake News
The authors allege that news platforms and social media networks use fake news to enhance readership. The study aimed to find an intervention that users can employ to detect and remove fake news from social media outlets and news platforms. Moreover, the authors emphasize an intervention to detect and filter fake news. This article is therefore important to the research topic as it provides a solution to help social media users identify and remove sites containing fake news. The article also provides an ease-to-use tool that allows social media users to identify and filter fake news. In addition, the article is important in terms of demonstrating that fake news interferes with users’ capacity to understand relevant internet information, particularly if the news is used in decision-making.
Sharing of Fake News
Iosifidis, P., & Nicoli, N. (2020). The battle to end fake news: A qualitative content analysis of Facebook announcements on how it combats disinformation. International Communication Gazette, 82(1), 60-81. https://doi.org/10.1177/1748048519880729
Qualitative research was used to examine how Facebook addresses the issue of fake news and disinformation. The article is important to the research topic because it offers a platform for holding social media platforms accountable for resolving disinformation. Moreover, the article provides evidence as the basis for investigating the research topic. Specifically, the article emphasizes the dissemination of intentional falsehood on social media for social influence or profit purposes. Again, the article is useful information for detecting and categorizing false information. Consequently, the approach is suitable for the research topic since it demonstrates that disinformation on social media outlets requires a multidisciplinary approach to resolve, including governments, mass communication professionals, technology providers, and regulators, to shape internet usage effectively.
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2020). Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data, 8(3), 171-188. https://doi.org/10.1089/big.2020.0062
Social Media Sharing of Fake News
The authors indicate that although social media is a ubiquitous communication channel, it facilitates the dissemination of fake news. Using exploratory analysis, they provide an understanding of various perspectives that reflect the quality of data and how to detect, evaluate and mitigate fake news. The article applies to the research topic because it provides multidimensional data that allow social media users to detect fake news. Furthermore, the article is appropriate since it presents insights into disinformation strategies.
While detecting fake news on social media is challenging, the article provides relevant information for understanding user engagement and social behavior on new platforms and social media platforms. The article is also useful to the topic since it offers evidence on how fake news is disseminated by detecting persuaders, provenances, and interventions.
Social Media Sharing of Fake News
The Negative Aspect of Fake News
Baccarella, C. V., Wagner, T. F., Kietzmann, J. H., & McCarthy, I. P. (2018). Social media? It’s serious! Understanding the dark side of social media. European Management Journal, 36(4), 431-438. https://doi.org/10.1016/j.emj.2018.07.002
The authors examined the multidimensionality of the dark side of social media platforms and the associated undesirable outcomes. The authors used the social media honeycomb framework to describe the implications of social media concerning seven building blocks, including relationships, identity, groups, sharing, conversations, and presence. They found that while social media platforms have negative impacts, users do not question the short and long-term impacts of sharing fake news. As a result, this article is pertinent to the research topic since it shows how even social media building blocks facilitate the misinformation and disinformation of inaccurate news. The article is also relevant because it provides evidence that enhances understanding and examines various elements of the dark side of social media. The article also provides current evidence to understand online behavior that can help researchers to examine social interaction in the context of the digital age.
Cheng, Y., & Chen, Z. F. (2020). The influence of presumed fake news influence: Examining public support for corporate corrective response, media literacy interventions, and governmental regulation. Mass Communication and Society, 23(5), 705-729. https://doi.org/10.1080/15205436.2020.1750656
Social Media Sharing of Fake News
The authors used a theoretical model to explore the antecedents and impacts of fake news on presumed effects of fake news on others (PFNE3). Moreover, a total of 661 participants were recruited to share their views about fake news on Facebook. The authors found that PFNE3 was an important predictor of public support for corporate corrective measures, government regulation, and interventions for media literacy. The article is relevant to the research topic since it presents evidence for corporates, public, and communication experts to prevent the spread of fake news using social media outlets. Furthermore, the article is useful as it highlights the significance of behavioral impacts associated with the perception of disinformation and misinformation about corporate entities. Additionally, the article is significant to the research topic in terms of demonstrating how corporates should take responsibility to combat the adverse effects of fake news.
Jang, S. M., & Kim, J. K. (2018). Third person effects of fake news: Fake news regulation and media literacy interventions. Computers in Human Behavior, 80, 295-302. https://doi.org/10.1016/j.chb.2017.11.034
The authors show that social media outlets and the internet have facilitated information dissemination. Nonetheless, the spread of this information comes with inaccurate information due to high public demand and uncertainty for information. In this article, the authors used the third-person effect (TPE) to investigate the antecedents and consequences of third-person perception. The authors analyzed the views of 1,299 respondents in the US and found that the social undesirability of content was positively linked to third-person perception. As such, the peer-reviewed journal is important to the research topic because a greater level of third-person perception is likely to promote media regulation to combat the spread of fake news. Besides, the article is essential since it provides factors that encourage misinformation and disinformation on social media platforms.
The Motivations behind Fake News
Jang, S. M., Geng, T., Li, J. Y. Q., Xia, R., Huang, C. T., Kim, H., & Tang, J. (2018). Evolution tree analysis is a computational approach for examining the roots and spreading patterns of fake news. Computers in Human Behavior, 84, 103-113. https://doi.org/10.1016/j.chb.2018.02.032
This study evaluated the root content, evolution pattern, and original sources from 307,738 tweets about 30 fake and 30 real news stories. The authors found that actual news spreads faster than fake news. The article is important to the research topic because it brings into light ways to combat disinformation and misinformation on social media. It also demonstrates how to detect the original sources and evolution trends of social fake news. Furthermore, the article is pertinent as it differentiates fake and real news based on evolutionary trends. The article is suitable for the research topic because it highlights the importance of creating awareness, such as media literacy intervention, to prevent misinformation.
Why People Share Fake News
Talwar, S., Dhir, A., Kaur, P., Zafar, N., & Alrasheedy, M. (2019). Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. Journal of Retailing and Consumer Services, 51, 72-82. https://doi.org/10.1016/j.jretconser.2019.05.026
Social Media Sharing of Fake News
The study evaluated the relationship between the data side of social media and sharing of fake news among social media users. In addition, to test the research model, the authors selected 1,022 WhatsApp users. The study found that sharing fake news is positively associated with online trust, self-disclosure, social media fatigue, and fear of missing out (FoMO). Consequently, online trust negatively correlates with sharing fake news before authentication. Even though sharing fake news manifests underlying emotions, the article is relevant to the research topic. This is because it provides up-to-date information on how disinformation and misinformation on social media platforms negatively affect brands and products. Moreover, the article shows how marketers can use news platforms and social media outlets to run contests for social media users to identify fake news.
Talwar, S., Dhir, A., Singh, D., Virk, G. S., & Salo, J. (2020). Sharing of fake news on social media: Application of the honeycomb framework and the third-person effect hypothesis. Journal of Retailing and Consumer Services, 57, 102197. https://doi.org/10.1016/j.jretconser.2020.102197
In this peer-reviewed article, the authors used mixed-method research to investigate fake news sharing behavior. In study A, the authors used qualitative data from 58 WhatsApp users to identify six behavioral manifestations of sharing fake news. In study B, researchers used non-probability judgmental sampling to select 471 and 374 social media users in northern and western India, respectively, to test the third-person effect (TPE). The article found that prompt sharing positively impacts fake news because of the lack of time. Nonetheless, authenticating news before sharing does not affect sharing fake news. This article is relevant to the research topic because it highlights how social media users adhere to best practices of sharing news, hence less likely to fake news due to lack of time. In addition, the article is relevant since it brings into perspective how lack of time and religiosity leads to disinformation and misinformation. Again, the article is appropriate for the research topic because it bridges the gap of motives for sharing disinformation on social media platforms.
Social Media Sharing of Fake News
Tandoc Jr, E. C., Lim, D., & Ling, R. (2020). Diffusion of disinformation: How social media users respond to fake news and why. Journalism, 21(3), 381-398 https://doi.org/10.1177/1464884919868325.
The study aimed to evaluate how social media users respond to false news. The authors used 69 fake news websites and 9,540 fake news stories on social media. The study is relevant to the research topic since it shows fake news disinformation on social media. Additionally, the article is crucial as it reflects how people interact with fake news on various media outlets. For example, while interaction with false news has increased on Facebook and Twitter, the engagement ratio has declined considerably. The article also provides factors that facilitate disinformation on social media platforms, making it relevant to the research topic.
Social Media Sharing of Fake News
References
Aldwairi, M., & Alwahedi, A. (2018). Detecting fake news in social media networks. Procedia Computer Science, 141, 215-222. https://doi.org/10.1016/j.procs.2018.10.171
Baccarella, C. V., Wagner, T. F., Kietzmann, J. H., & McCarthy, I. P. (2018). Social media? It’s serious! Understanding the dark side of social media. European Management Journal, 36(4), 431-438. https://doi.org/10.1016/j.emj.2018.07.002
Cheng, Y., & Chen, Z. F. (2020). The influence of presumed fake news influence: Examining public support for corporate corrective response, media literacy interventions, and governmental regulation. Mass Communication and Society, 23(5), 705-729. https://doi.org/10.1080/15205436.2020.1750656
Jang, S. M., & Kim, J. K. (2018). Third person effects of fake news: Fake news regulation and media literacy interventions. Computers in Human Behavior, 80, 295-302. https://doi.org/10.1016/j.chb.2017.11.034
Jang, S. M., Geng, T., Li, J. Y. Q., Xia, R., Huang, C. T., Kim, H., & Tang, J. (2018). A computational approach for examining the roots and spreading patterns of fake news: Evolution tree analysis. Computers in Human Behavior, 84, 103-113. https://doi.org/10.1016/j.chb.2018.02.032
Iosifidis, P., & Nicoli, N. (2020). The battle to end fake news: A qualitative content analysis of Facebook announcements on how it combats disinformation. International Communication Gazette, 82(1), 60-81. https://doi.org/10.1177/1748048519880729
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2020). Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data, 8(3), 171-188. https://doi.org/10.1089/big.2020.0062
Talwar, S., Dhir, A., Kaur, P., Zafar, N., & Alrasheedy, M. (2019). Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. Journal of Retailing and Consumer Services, 51, 72-82. https://doi.org/10.1016/j.jretconser.2019.05.026
Talwar, S., Dhir, A., Singh, D., Virk, G. S., & Salo, J. (2020). Sharing of fake news on social media: Application of the honeycomb framework and the third-person effect hypothesis. Journal of Retailing and Consumer Services, 57, 102197. https://doi.org/10.1016/j.jretconser.2020.102197
Tandoc Jr, E. C., Lim, D., & Ling, R. (2020). Diffusion of disinformation: How social media users respond to fake news and why. Journalism, 21(3), 381-398 https://doi.org/10.1177/1464884919868325.
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