Health Misinformation in the Covid-19 Era - Detecting Misinformation on Bi-lingual Corpora using Lexical Features

Ramakrishnan, Kalaimagal; Balakrishnan, Vimala

doi:10.1109/iceccme55909.2022.9988481

Cited by 1 publication

(2 citation statements)

References 15 publications

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“… 3 , 10 Another important aspect to consider is the more extensive research on algorithms to detect misinformation in English compared to Malay language. 14 – 17 Social media sites such as Twitter attempted to combat misinformation by having specific features such as labelling content that may contain problematic information and giving prompts when users engage with a potentially misleading post. 21 , 22 However, these features will be obsolete if the algorithm to detect misinformation in the Malay language is deficient.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the large number of studies on misinformation in social media, many of these focused only on one language, especially English. 3 , 14 – 17 The problem of health misinformation is not limited to English-language content, and it can be even more challenging in languages other than English. Malaysia is a multiracial country that uses Malay as the national language.…”

Section: Introductionmentioning

confidence: 99%

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Breast cancer prevention and treatment misinformation on Twitter: An analysis of two languages

Yussof,

Ab Muin,

Mohd

et al. 2023

DIGITAL HEALTH

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Objective To determine the prevalence and types of misinformation on Twitter related to breast cancer prevention and treatment; and compare the differences between the misinformation in English and Malay tweets. Methods A total of 6221 tweets related to breast cancer posted between 2018 and 2022 were collected. An oncologist and two pharmacists coded the tweets to differentiate between true information and misinformation, and to analyse the misinformation content. Binary logistic regression was conducted to identify determinants of misinformation. Results There were 780 tweets related to breast cancer prevention and treatment, and 456 (58.5%) contain misinformation, with significantly more misinformation in Malay compared to English tweets (OR = 6.18, 95% CI: 3.45–11.07, p < 0.001). Other determinants of misinformation were tweets posted by product sellers and posted before the COVID-19 pandemic. Less misinformation was associated with tweets utilising official/peer-reviewed sources of information compared to tweets without external sources and those that utilised less reliable information sources. The top three most common content of misinformation were food and lifestyle, alternative medicine and supplements, comprising exaggerated claims of anti-cancer properties of traditional and natural-based products. Conclusion Misinformation on breast cancer prevention and treatment is prevalent on social media, with significantly more misinformation in Malay compared to English tweets. Our results highlighted that patients need to be educated on digital health literacy, with emphasis on utilising reliable sources of information and being cautious of any promotional materials that may contain misleading information. More studies need to be conducted in other languages to address the disparity in misinformation.

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