As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

Twitter has been one of the most popular social network sites for academic research; the main objective of this study was to update the current knowledge boundary surrounding Twitter-related investigations and, further, identify the major research topics and analyze their evolution across time. A bibliometric analysis has been applied in this article: we retrieved 19,205 Twitter-related academic articles from Web of Science after several steps of data cleaning and preparation. The R package “Bibliometrix” was mainly used in analyzing this content. Our study has two sections, and performance analysis contains 5 categories (Annual Scientific Production, Most Relevant Sources, Most Productive Authors, Most Cited Publications, Most Relevant Keywords.). The science mapping included country collaboration analysis and thematic analysis. We highlight our thematic analysis by splitting the whole bibliographic dataset into three temporal periods, thus a thematic evolution across time has been presented. This study is one of the most comprehensive bibliometric overview in analyzing Twitter-related studies by far. We proceed to explain how the results will benefit the understanding of current academic research interests on the social media giant.

While COVID-19 is becoming one of the most severe public health crises in the twenty-first century, media coverage about this pandemic is getting more important than ever to make people informed. Drawing on data scraped from Twitter, this study aims to analyze and compare the news updates of two main Spanish newspapers El País and El Mundo during the pandemic. Throughout an automatic process of topic modeling and network analysis methods, this study identifies eight news frames for each newspaper’s Twitter account. Furthermore, the whole pandemic development process is split into three periods—the pre-crisis period, the lockdown period and the recovery period. The networks of the computed frames are visualized by these three segments. This paper contributes to the understanding of how Spanish news media cover public health crises on social media platforms.

The novel coronavirus disease (SARS-CoV-2/COVID-19) resulted in a severe global pandemic in 2020. Amid the unprecedented health crisis, visuals play important roles in media framing and health crisis communication. This article provides one of the first analyses of textual-visual news concerning COVID-19 posted by two leading German newspapers on Twitter. Our objective is to integrate texts and visuals into framing analysis to better understand the multimodal news presentation of health crises. A quantitative content analysis of 2479 tweets examined nine news frames, among which politics and instructions, update information, economic impact and social impact presented more thematic salience. The semiotic signs of indexical, symbolic and iconic appeared in news visuals and served evidential, performative and illustrative functions. The results also detected recurrent news visuals in the color-clustered patterns. Notably, an alluvial diagram revealed the connections between textual frames and visual signs across three crisis phases. In recognizing the diverse roles of visuals in constructing mediated reality, this article demonstrates the value in the multimodal framing of health crises and suggests expanding our focus to risk cultures for future international comparative research.

Twitter is an important object of research in social science studies, and the emergence of numerous Twitter software tools has provided researchers with the possibility of gaining insights into Twitter data; however, at the same time, early stage researchers may experience difficulties in selecting the most suitable tool for their own projects. Due to personal or institutional financial constraints, the purchase of commercial software is not a wise investment for all researchers. Hence, this article aims to provide a review of nine different free-of-charge and low-cost software tools for studying Twitter and highlights their advantages and disadvantages, in order to serve as a guide for social science scholars. This review is divided into two parts: background information and data retrieval features of the software tools, and their data analysis features. At the end of the review, several recommendations and suggestions are made for the use of these tools.