萝莉社官网

INTEREST CATEGORY: INNOVATION AND TECH
POSTING TYPE: Calls: Journals

Author: Giandomenico Di Domenico

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Call for papers

The social, ethical, economic, and political implications of misinformation

Closes:聽31 Jul 2022

闯辞耻谤苍补濒:听

Guest editors

• Giandomenico Di Domenico
• Maria Teresa Borges-Tiago
• Giampaolo Viglia
• Yang Alice Cheng

Paper Submission due: July 31, 2022

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Lies are as old as humankind. However, digital technologies have given new impetus to the spreading of misinformation (Allcott & Gentzkow, 2017), broadening its reach (Vosoughi, Roy & Aral, 2018), and giving mainstream popularity to conspiratorial groups once were fringe. False information can assume many different formats, reflecting the deliberately or inadvertently deceiving creation process: misinformation or disinformation (Cummings & Kong, 2019). Misinformation consists of all misleading information that is created and shared, not aiming to manipulate people. On the other hand, disinformation consists of all misleading information designed to confuse or exploit people. Disinformation can assume different forms, ranging from fake news, fake stories, clickbait, cyber propaganda, hoaxes, rumors, spam, satire/parody, to sloppy journalism (Borges-Tiago et al., 2020; Islam et al., 2020). It can also be divided into three broad categories according to its origin: (i) individuals; (ii) firms; (ii) or content farms, also known as troll factories or content mills (Novaes & Ridder, 2021). However, the line between misinformation and disinformation is often blurred in digital environments as disinformation can become misinformation when users believe in the content they read and start a pass-on movement.

To date, most of the scholarship has investigated the spreading patterns, detection methods, and characteristics of misinformation (e.g. Wang & Song, 2021; 聽Zhang et al., 2020; Del Vicario et al., 2019; Pennycook & Rand, 2019; Borges-Tiago et al., 2020). However, more research is needed to understand how misinformation and disinformation evolve and their social, ethical, economic, and political implications.聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽

Topics of interest

1. A different medium for a different type of misinformation

Most academic research on misinformation has focused on the effects of fake news headlines and articles on individuals (Pennycook & Rand, 2019; Kim & Dennis, 2019). However, misinformation might come in various formats, such as memes, images, and videos, leaving interesting gaps on their differential impact compared to textual articles.

New AI-enabled technologies allow the creation of more sophisticated forms of misinformation. Among them, Deep fakes and Cheap fakes have a more severe impact on individuals (Di Domenico & Visentin, 2020; Agarwal et al., 2019). Interestingly, misinformation spreads through social media and other channels such as messaging apps (de Freitas Melo et al., 2019). The massive spreading of misinformation through messaging apps has forced some platforms to restrict the dissemination of misleading content (Arun, 2019).

The availability of different types of content and channels opens unprecedented possibilities for fake contents creators to spread a specific type of content through a specific channel. For example, manipulated images and videos appear to be more popular on social media platforms like Facebook (Shen et al., 2019), while messaging apps are more suitable mediums for fake news article spreading. Hence, understanding how the content is customized and how a medium is chosen is fundamentally essential to prevent the spread of misinformation.

Topics of interest include (but are not limited to):

• The differential impact of various types of misinformation and disinformation on individuals
• Predictors of fake images and videos credibility assessments
• The impact of new forms of misinformation and disinformation
• The effectiveness of existing restrictions enabled by messaging apps
• How users assess the credibility of information on messaging apps
• How the type of fake content is adapted to the differential medium

2. The implications of misinformation

Misinformation impacts all aspects of our lives; thus, research is needed to discuss misinformation’s social, ethical, economic, and political implications. From a social point of view, misinformation creates tensions among individuals, such as cyberbullying (de Fonseca & Borges-Tiago, 2021) or even violence and assaults, as in the case of mob violence in India (Banaji et al., 2019). Moreover, the algorithmic logic favoring the creation of so-called “echo chambers” – where misinformation thrives – opens up lingering ethics questions.

The format and source of misleading information are diverse, ranging from potentially deceptive online reviews to fabricated brand-related content on social media (Chen & Cheng, 2019).

Finally, misinformation can affect important political outcomes such as the US Presidential Elections or the Brexit referendum (Allcott & Gentzkow, 2017).

Technology enhances users’ possibilities of creating and sharing misleading information since anyone can publish content online and potentially reach large audiences. But, technology can also be used to diminish the effects of this type of information, using reporting and flagging tools, supporting fact-checking sites, or creating AI tools that can detect deepfakes.

The implications of misinformation might be amplified or reduced depending on cultural differences (Borges-Tiago et al., 2020). A less explored culture-related aspect is language. Although deep bidirectional transformer language models are growing, most research efforts have target misinformation in English, neglecting the occurrence of this phenomenon in other languages (Song et al., 2021). Thus, understanding the influence of culture might provide valuable novel insights for designing more fine-grained public-policy interventions.

Topics of interest include (but are not limited to):

• The implications of misinformation and disinformation from a social, ethical, economic, and political point of view
• Long vs. short term effect of misinformation and disinformation
• The role of technology in mitigating the consequences of misinformation and disinformation
• Cross-cultural differences in responses to misinformation and disinformation

This call for papers aims to gather interdisciplinary research to discuss the above thematical areas. It invites submissions from a variety of methodological, theoretical, and multidisciplinary perspectives. In bringing technical, behavioral, and managerial perspectives together, this special issue explores where and why misinformation originates and propagates in digital environments and provides an understanding of its adverse implications for individuals, organizations, and societies.

Submission and Review Schedule

• Submission system open:聽April 1, 2022
• Paper submission due:聽July 31, 2022
• First review result:聽August 31, 2022
• Revision due:聽November 30, 2022
• Second review result:聽December 31, 2022
• Final decision:聽February 2023

References

Agarwal, S., Farid, H., Gu, Y., He, M., Nagano, K., & Li, H. (2019, June). Protecting World Leaders Against Deep Fakes. In聽CVPR workshops聽(Vol. 1).

Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election.聽Journal of economic perspectives,听31(2), 211-36.

Arun, C. (2019). On WhatsApp, rumours, lynchings, and the Indian Government.聽Economic & Political Weekly,听54(6).

Banaji, S., Bhat, R., Agarwal, A., Passanha, N., & Sadhana Pravin, M. (2019). WhatsApp vigilantes: An exploration of citizen reception and circulation of WhatsApp misinformation linked to mob violence in India.聽London School of Economics Research Online, monograph.

Borges鈥怲iago, T., Tiago, F., Silva, O., Guaita Martinez, J. M., & Botella鈥怌arrubi, D. (2020). Online users’ attitudes toward fake news: Implications for brand management.聽Psychology & Marketing,听37(9), 1171-1184.

Chen, Z. F., & Cheng, Y. (2019). Consumer response to fake news about brands on social media: the effects of self-efficacy, media trust, and persuasion knowledge on brand trust.聽Journal of Product & Brand Management.

Cummings, C. L., & Kong, W. Y. (2019). Breaking Down “Fake News”: Differences Between Misinformation, Disinformation, Rumors, and Propaganda. In Resilience and Hybrid Threats (pp. 188-204). IOS Press.

da Fonseca, J. M. R., & Borges-Tiago, M. T. (2021). Cyberbullying From a Research Viewpoint: A Bibliometric Approach. In聽Handbook of Research on Cyber Crime and Information Privacy聽(pp. 182-200). IGI Global.

de Freitas Melo, P., Vieira, C. C., Garimella, K., de Melo, P. O. V., & Benevenuto, F. (2019, December). Can WhatsApp counter misinformation by limiting message forwarding?. In聽International conference on complex networks and their applications聽(pp. 372-384). Springer, Cham.

Del Vicario, M. D., Quattrociocchi, W., Scala, A., & Zollo, F. (2019). Polarization and fake news: Early warning of potential misinformation targets.聽ACM Transactions on the Web (TWEB),听13(2), 1-22.

Di Domenico, G., & Visentin, M. (2020). Fake news or true lies? Reflections about problematic contents in marketing.聽International Journal of Market Research,听62(4), 409-417.

Islam, M. R., Liu, S., Wang, X., & Xu, G. (2020). Deep learning for misinformation detection on online social networks: a survey and new perspectives.聽Social Network Analysis and Mining, 10(1), 1-20.

Kim, A., & Dennis, A. R. (2019). Says who? The effects of presentation format and source rating on fake news in social media.聽Mis quarterly, 43(3).

Novaes, C. D., & de Ridder, J. (2021). Is Fake News Old News?. In The Epistemology of Fake News (pp. 156-179). Oxford University Press.

Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning.聽Cognition,听188, 39-50.

Shen, C., Kasra, M., Pan, W., Bassett, G. A., Malloch, Y., & O’Brien, J. F. (2019). Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online.聽New media & society,听21(2), 438-463.

Song, C., Ning, N., Zhang, Y., & Wu, B. (2021). A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks.聽Information Processing & Management, 58(1), 102437.

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online.聽Science,听359(6380), 1146-1151.

Wang, X., & Song, Y. (2020). Viral misinformation and echo chambers: The diffusion of rumors about genetically modified organisms on social media.聽Internet Research.

Zhang, W., Du, W., Bian, Y., Peng, C. H., & Jiang, Q. (2020). Seeing is not always believing: an exploratory study of clickbait in WeChat.聽Internet Research.