characterizing and comparing covid 19 misinformation across languages countries and CORD-Papers-2022-06-02 (Version 1)

Title: Characterizing and Comparing COVID-19 Misinformation across Languages Countries and Platforms
Abstract: Misinformation/disinformation about COVID-19 has been rampant on social media around the world. In this study we investigate COVID-19 misinformation/ disinformation on social media in multiple languages/countries: Chinese (Mandarin)/China English/USA and Farsi (Persian)/Iran;and on multiple platforms such as Twitter Facebook Instagram WhatsApp Weibo WeChat and TikTok. Misinformation especially about a global pandemic is a global problem yet it is common for studies of COVID-19 misinformation on social media to focus on a single language like English a single country like the USA or a single platform like Twitter. We utilized opportunistic sampling to compile 200 specific items of viral and yet debunked misinformation across these languages countries and platforms emerged between January 1 and August 31. We then categorized this collection based both on the topics of the misinformation and the underlying roots of that misinformation. Our multi-cultural and multi-linguistic team observed that the nature of COVID-19 misinformation on social media varied in substantial ways across different languages/countries depending on the cultures beliefs/religions popularity of social media types of platforms freedom of speech and the power of people versus governments. We observe that politics is at the root of most of the collected misinformation across all three languages in this dataset. We further observe the different impact of government restrictions on platforms and platform restrictions on content in China Iran and the USA and their impact on a key question of our age: how do we control misinformation without silencing the voices we need to hold governments accountable?. 2021 ACM.
Published: 2021
Journal: 30th World Wide Web Conference WWW 2021
Author Name: Madraki G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/madraki_g
Author Name: Grasso I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/grasso_i
Author Name: Otala J M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/otala_j_m
Author Name: Liu Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Matthews J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/matthews_j
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #covidwho-1276994
has_full_text: FALSE
G_ID: characterizing_and_comparing_covid_19_misinformation_across_languages_countries_and