Discriminating Gender on Twitter
- Journal
- Empirical Methods in Natural Language Processing
In The Last Decade
doi.org/w14469998 →Countries where authors are citing Discriminating Gender on Twitter
This map shows the geographic impact of Discriminating Gender on 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 Discriminating Gender on Twitter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Discriminating Gender on Twitter more than expected).
Fields of papers citing Discriminating Gender on Twitter
This network shows the impact of Discriminating Gender on Twitter. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Discriminating Gender on Twitter.
About Discriminating Gender on Twitter
This paper, published in 2011, received 348 indexed citations . Written by John D. Burger, John C. Henderson, George Kim and Guido Zarrella covering the research area of Artificial Intelligence and Information Systems. It is primarily cited by scholars working on Artificial Intelligence (273 citations), Information Systems (128 citations), Sociology and Political Science (87 citations), Statistical and Nonlinear Physics (51 citations) and Human-Computer Interaction (34 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/w14469998.