Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter

358 indexed citations
published 2011
Authors
Eiji AramakiMizuki Morita
Journal
Empirical Methods in Natural Language Processing

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doi.org/w8422104 →

Countries where authors are citing Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter

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Citations

This map shows the geographic impact of Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter. 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 Catches The Flu: Detecting Influenza Epidemics using Twitter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter more than expected).

Fields of papers citing Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter.

About Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter

This paper, published in 2011, received 358 indexed citations . Written by Eiji Aramaki and Mizuki Morita covering the research area of Epidemiology and Sociology and Political Science. It is primarily cited by scholars working on Epidemiology (153 citations), Artificial Intelligence (152 citations) and Sociology and Political Science (128 citations). Published in Empirical Methods in Natural Language Processing.

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/w8422104.

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