Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach
- Authors
- Jia XueJunxiang ChenRan HuChen ChenChengda Zheng
In The Last Decade
doi.org/10.2196/20550 →Countries where authors are citing Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach
This map shows the geographic impact of Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. 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 Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach more than expected).
Fields of papers citing Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach
This network shows the impact of Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach.
About Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach
This paper, published in 2020, received 239 indexed citations . Written by Jia Xue, Junxiang Chen, Ran Hu, Chen Chen, Chengda Zheng, Yue Su and Tingshao Zhu covering the research area of Epidemiology, Sociology and Political Science and Health. It is primarily cited by scholars working on Sociology and Political Science (161 citations), Artificial Intelligence (112 citations) and Communication (47 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/10.2196/20550.