Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends
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doi.org/10.2196/19447 →Countries where authors are citing Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends
This map shows the geographic impact of Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends. 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 Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends more than expected).
Fields of papers citing Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends
This network shows the impact of Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends.
About Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends
This paper, published in 2020, received 305 indexed citations . Written by May O. Lwin, Jiahui Lu, Anita Sheldenkar, Peter J. Schulz, Wonsun Shin, Raj Kumar Gupta and Yinping Yang covering the research area of Communication, Clinical Psychology and Sociology and Political Science. It is primarily cited by scholars working on Sociology and Political Science (221 citations), Artificial Intelligence (90 citations) and Communication (66 citations). Published in JMIR Public Health and Surveillance.
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/10.2196/19447.