Misinformation as a Window into Prejudice

Akbar, Syeda Zainab; Panda, Anmol; Kukreti, Divyanshu; Meena, Azhagu; Pal, Joyojeet

doi:10.1145/3432948

Cited by 39 publications

(16 citation statements)

References 59 publications

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“…Regarding the first research question, the narrative threads of misinformation that have circulated in Saudi Arabia since the COVID-19 pandemic include the origin of COVID-19 and health advice coping with COVID-19. The findings suggest that misinformation could be tied in with sense-making, which is consistent with previous research [1,2]. It has been concluded that people turn to rumors as a way to cope with uncertainty.…”

Section: Discussionsupporting

confidence: 89%

“…However, not all these languages have easy access to verified or reliable information, especially during this tough time. There are many studies investigating misinformation on Twitter related to COVID-19 in different languages (e.g., [1,9,[16][17][18]). For example, Jussila et al [17] explored misinformation related to COVID-19 in Finnish.…”

Section: Health Misinformation In the Arabic Languagementioning

confidence: 99%

“…False rumors, misinformation, and disinformation (e.g., "fake news") are diffused and often inadvertently endorsed through social media platforms markedly faster, deeper, and more broadly than trustworthy information. Research has shown that misinformation can foster an atmosphere of panic and discrimination in pandemics [1]. Hence, a need for the public during pandemics to have access to clear, up-to-date information and to be transparent in decision-making at the strategic and operational levels.…”

Section: Introductionmentioning

confidence: 99%

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A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

et al. 2021

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COVID-19 has had broad disruptive effects on economies, healthcare systems, governments, societies, and individuals. Uncertainty concerning the scale of this crisis has given rise to countless rumors, hoaxes, and misinformation. Much of this type of conversation and misinformation about the pandemic now occurs online and in particular on social media platforms like Twitter. This study analysis incorporated a data-driven approach to map the contours of misinformation and contextualize the COVID-19 pandemic with regards to socio-religious-political information. This work consists of a combined system bridging quantitative and qualitative methodologies to assess how information-exchanging behaviors can be used to minimize the effects of emergent misinformation. The study revealed that the social media platforms detected the most significant source of rumors in transmitting information rapidly in the community. It showed that WhatsApp users made up about 46% of the source of rumors in online platforms, while, through Twitter, it demonstrated a declining trend of rumors by 41%. Moreover, the results indicate the second-most common type of misinformation was provided by pharmaceutical companies; however, a prevalent type of misinformation spreading in the world during this pandemic has to do with the biological war. In this combined retrospective analysis of the study, social media with varying approaches in public discourse contributes to efficient public health responses.

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Section: Discussionsupporting

confidence: 89%

Section: Health Misinformation In the Arabic Languagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

et al. 2021

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“…Journalistic objectivity often points to impartiality, nonalignment, non-involvement, and non-partisanship in building narratives but most consistently involves all of these characteristics. According to (Akbar, Panda, Kukreti, Meena, & Pal, 2021), journalism from the very broad perspective is the field of human endeavour that deals with the fabrication, processing, manufacturing and circulation of occurrences of events within local or national and to the global environment. The concept of journalism relates to employment, career and profession in addition to citizenship correspondences which involves assemblage and circulation of information, (Carlson & Lewis, 2015).…”

Section: The Concept Of Iot Drone Journalismmentioning

confidence: 99%

News Reporting in Drone Internet of Things Digital Journalism

Nwanakwaugwu

Matthew

Okey

et al. 2023

International Journal of Interactive Communication Systems and Technologies

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The current study investigated several innovations for drone technology adoption in journalistic expeditions for intelligence and news gathering purposes. The necessity to leverage technologies to improve the direct involvement of eyewitnesses especially in violence-prone areas where physical and direct human involvement would be impossible or with high risk of survivability expectations is the motivating factor that directed the current research. The paper surveys the adoption of autonomous sensing drone systems in internet of things journalism and amalgamated the theoretic ingredients from the academic standpoint with realistic technological advancements from the global perspective and eventually expanded the propositions for conceivable adoption in the credible societal applications. The paper envisioned the future journalism and mass media practices and how drone innovation can revolutionize the journalism profession for the purpose of news and intelligence gathering with practical and technical realism with reduction of journalistic casualties.

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“…There is a strong need for developing technology to counter harmful online content in India. With the increasing popularity of social media platforms (e.g., Facebook, Twitter) [10] and instant messaging services (e.g., WhatsApp) in India, researchers have been studying the role of phenomena such as online misinformation [11] and hate speech [12] in Indian society and investigating ways to cope with their widespread prevalence.…”

Section: Motivationmentioning

confidence: 99%

An Evaluation of Multilingual Offensive Language Identification Methods for the Languages of India

Ranasinghe

Zampieri

2021

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The pervasiveness of offensive content in social media has become an important reason for concern for online platforms. With the aim of improving online safety, a large number of studies applying computational models to identify such content have been published in the last few years, with promising results. The majority of these studies, however, deal with high-resource languages such as English due to the availability of datasets in these languages. Recent work has addressed offensive language identification from a low-resource perspective, exploring data augmentation strategies and trying to take advantage of existing multilingual pretrained models to cope with data scarcity in low-resource scenarios. In this work, we revisit the problem of low-resource offensive language identification by evaluating the performance of multilingual transformers in offensive language identification for languages spoken in India. We investigate languages from different families such as Indo-Aryan (e.g., Bengali, Hindi, and Urdu) and Dravidian (e.g., Tamil, Malayalam, and Kannada), creating important new technology for these languages. The results show that multilingual offensive language identification models perform better than monolingual models and that cross-lingual transformers show strong zero-shot and few-shot performance across languages.

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Misinformation as a Window into Prejudice

Cited by 39 publications

References 59 publications

A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

A Retrospective Analysis of the COVID-19 Infodemic in Saudi Arabia

News Reporting in Drone Internet of Things Digital Journalism

An Evaluation of Multilingual Offensive Language Identification Methods for the Languages of India

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