Vigneshwaran Muralidaran
- Artificial Intelligence top 5%
- Information Systems
- Communication
- Computer Vision and Pattern Recognition
- Social Psychology
- Co-authors
- Bharathi Raja ChakravarthiRuba PriyadharshiniJohn P. McCraeNavya JoseShardul SuryawanshiElizabeth SherlySalud María Jiménez-ZafraIrena Spasić
- Topics
- Natural Language Processing Techniques (4 papers)Hate Speech and Cyberbullying Detection (3 papers)Topic Modeling (2 papers)
- Partner nations
- United KingdomIndiaIreland
In The Last Decade
Vigneshwaran Muralidaran
5 papers receiving 150 citations
Peers
Comparison fields: 5 of 26
- Artificial Intelligence 207
- Information Systems 26
- Communication 24
- Computer Vision and Pattern Recognition 16
- Social Psychology 14
Countries citing papers authored by Vigneshwaran Muralidaran
This map shows the geographic impact of Vigneshwaran Muralidaran's research. 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 Vigneshwaran Muralidaran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vigneshwaran Muralidaran more than expected).
Fields of papers citing papers by Vigneshwaran Muralidaran
This network shows the impact of papers produced by Vigneshwaran Muralidaran. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Vigneshwaran Muralidaran. The network helps show where Vigneshwaran Muralidaran may publish in the future.
Co-authorship network of co-authors of Vigneshwaran Muralidaran
This figure shows the co-authorship network connecting the top 25 collaborators of Vigneshwaran Muralidaran. A scholar is included among the top collaborators of Vigneshwaran Muralidaran based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vigneshwaran Muralidaran. Vigneshwaran Muralidaran is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 50 | |
| 3 | 16 | |
| 4 | Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion | 76 |
| 5 | 4 | |
| 6 | 69 | |
| 7 | 1 |
About Vigneshwaran Muralidaran
Vigneshwaran Muralidaran is a scholar working on Visual Arts and Performing Arts, Radiological and Ultrasound Technology and Artificial Intelligence, having authored 7 papers that have together received 216 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (4 papers), Hate Speech and Cyberbullying Detection (3 papers) and Topic Modeling (2 papers). The work is most often cited by research in Artificial Intelligence (207 citations), Communication (24 citations) and Information Systems (26 citations). Vigneshwaran Muralidaran has collaborated with scholars based in United Kingdom, India and Ireland. Frequent co-authors include Bharathi Raja Chakravarthi, Ruba Priyadharshini, John P. McCrae, Navya Jose, Shardul Suryawanshi, Elizabeth Sherly, Salud María Jiménez-Zafra, Irena Spasić, Prasanna Kumar Kumaresan and José Antonio García-Díaz. Their work appears in journals such as Language Resources and Evaluation, Natural Language Engineering and JMIR Formative 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.