Characterizing the Gendered Twitter Discussion of COVID-19 Hoax

ABSTRACT

We collected data from Twitter and used content analysis to better understand the gendered discussion around COVID-19 as a hoax. We identified three main categories in the inductive stage of the research: (1) sympathetic to human rights & perceived injustice, (2) invincibility and superiority of COVID hoaxers, (3) conspiracies and/or hidden agendas. The findings of the study show that among all gender groups, the first category is the most dominant (44.4%), the third category is the second most frequent (35.6%), and the last category (19.9%) is the least frequent. However, when the discussion is centered on men (40.2%) and gender and sexual minorities (GSM; 69.6%) groups, the last category is the most dominant with regard to stigmatizing GSM groups by falsely associating them with progressive secret agendas. As for women’s group, being sympathetic to human rights and the perceived injustice against them during the pandemic constitute the most dominant category (51.5%). We discuss the implications of the study in the conclusion.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. TCAT is a Twitter collection platform that is not capable of fetching historical data because it used the old API access. We started our search on September 2, 2020 and continued until February 2, 2021 using 12 keywords that we collected from reading previous literature on the pandemic hoaxers: Covid_Hoax, Covid-Hoax, CovidHoax, scamdemic, HydroxychloroquineIsTheCure, Covid1984, FakePandemic, fakevirus, Covidisover, shamdemic, Covidhoax2020, plandemic2020. From this first search, we managed to collect 563,269 tweets in this stage of the study. However, Twitter announced in early 2021 the release of its new academic API which allowed us to collect all the necessary data that we needed. To be specific about our search due to the challenges of dealing with big data extraction, we used the above TCAT dataset that we collected to identify the main hashtags that this community uses. In order to find these most frequent hashtags, we used a Python script (Al-Rawi, Siddiqi et al., 2021), and 17 relevant hashtags were identified: #Covid-Hoax, #Covid1984,#CovidHoax, #Covid_Hoax, #Covidhoax2020, #Covidhoax2021, #FakePandemic, #HydroxychloroquineIsTheCure, #fakevirus, #scamdemic,#shamdemic, #plandemichoax, #casedemic, #pandemicfraud, #covidscam, #coronahoax, #scamdemicisover.

Additional information

Funding

This project is funded by the Canadian Institutes for Health Research (CIHR) grant entitled “Gender and COVID-19” https://www.sfu.ca/fhs/gendercovid.html [Grant number: OV7-170639].