A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19

Abstract

At the time of this study, the SARS-CoV-2 virus that caused the COVID-19 pandemic has spread significantly across the world. Considering the uncertainty about policies, health risks, financial difficulties, etc. the online media, especially the Twitter platform, is experiencing a high volume of activity related to this pandemic. Among the hot topics, the polarized debates about unconfirmed medicines for the treatment and prevention of the disease have attracted significant attention from online media users. In this work, we present a stance data set, COVID-CQ, of user-generated content on Twitter in the context of COVID-19. We investigated more than 14 thousand tweets and manually annotated the tweet initiators’ opinions regarding the use of “chloroquine” and “hydroxychloroquine” for the treatment or prevention of COVID-19. To the best of our knowledge, COVID-CQ is the first data set of Twitter users’ stances in the context of the COVID-19 pandemic, and the largest Twitter data set on users’ stances towards a claim, in any domain. We have made this data set available to the research community via the Mendeley Data repository. We expect this data set to be useful for many research purposes, including stance detection, evolution and dynamics of opinions regarding this outbreak, and changes in opinions in response to the exogenous shocks such as policy decisions and events.

Unmasking People’s Opinions behind Mask-Wearing during COVID-19 Pandemic—A Twitter Stance Analysis

“…The use of drugs for fighting COVID-19 based on Twitter extracted data has been analyzed in the scientific literature. Mutlu et al [47] have provided a set containing 14,374 tweets extracted from 11,552 unique users on Twitter in connection with the efficacy of hydroxychloroquine as a treatment for COVID-19. In the provided dataset, 47.59% of tweets were in favor, 32.59% of tweets were against, while 19.81 tweets were neutral [47].…”

Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection

“…We use a split of 80% and 20% of the documents in a dataset for training and testing, respectively. This split is the most common configuration for the tested datasets [36,19] and is comparable to other studies [7]. We use 10% of the train dataset as a hold-out validation set.…”

“…Data. We compile an evaluation set from six popular datasets for detecting COVID-19 misinformation in social media, news articles, and scientific publications, i.e., CORD-19 [33], CoAID [7], COVID-CQ [19], ReCOVery [36], CMU-MisCov19 [17], and COVID19FN. 13 Table 1 gives an overview of the datasets and Appendix A.1 presents more details.…”

“…COVID-BERT 7 and COVID-SciBERT 8 are pre-trained on the CORD-19 dataset and only available via the Huggingface API. Others, such as COVID-CQ [19] and CMU-MisCov19 [17] are used to either investigate intrinsic details (e.g., how dense misinformed communities are) or to explore the applicability of statistical techniques.…”

“…2 Researchers worldwide collaborate on automating the detection of false information on COVID-19. 5 The initiatives build collections of scientific papers, social media posts, and news articles to analyze their content, spread, source, and propagators [33,7,19].…”