COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data
In The Last Decade
doi.org/10.2196/19458 →Countries where authors are citing COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data
This map shows the geographic impact of COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data more than expected).
Fields of papers citing COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data
This network shows the impact of COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data.
About COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data
This paper, published in 2020, received 514 indexed citations . Written by Wasim Ahmed, Josep Vidal‐Alaball, Joseph Downing and Francesc López Seguí covering the research area of Communication, Sociology and Political Science and Information Systems. It is primarily cited by scholars working on Sociology and Political Science (401 citations), Health (144 citations) and Communication (128 citations). Published in Journal of Medical Internet Research.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.2196/19458.