Blessing Sibanda
Impact in
- Artificial Intelligence top 5%
- Natural Language Processing Techniques
- Topic Modeling
- Sentiment Analysis and Opinion Mining
- Domain Adaptation and Few-Shot Learning
- Speech and dialogue systems
- Safety Research top 10%
- Ethics and Social Impacts of AI
Papers in
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- Natural Language Processing Techniques 6
- Topic Modeling 4
- Text and Document Classification Technologies 1
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- Ethics and Social Impacts of AI 1
- Co-authors
- Gloria Ejehiohen Iyawa (2 shared papers)David Ifeoluwa Adelani (1 shared paper)Jade Abbott (1 shared paper)Anuoluwapo Aremu (1 shared paper)
- Journals
- Patterns (1 paper)Transactions of the Association for Computational Linguistics (1 paper)Advances in intelligent systems and computing (1 paper)Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (1 paper)Conference on Empirical Methods in Natural Language Processing (EMNLP) (1 paper)
- Partner nations
- UgandaSouth AfricaGermany
In The Last Decade
Blessing Sibanda
11 papers receiving 379 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 297
- Safety Research 42
- Health Informatics 4
- Information Systems 36
- Human-Computer Interaction 6
Countries citing papers authored by Blessing Sibanda
This map shows the geographic impact of Blessing Sibanda'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 Blessing Sibanda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Blessing Sibanda more than expected).
Fields of papers citing papers by Blessing Sibanda
This network shows the impact of papers produced by Blessing Sibanda. 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 Blessing Sibanda. The network helps show where Blessing Sibanda may publish in the future.
Co-authors
The 4 scholars most cited alongside Blessing Sibanda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 161 | |
| 2 | 2020 | 109 | |
| 3 | 2022 | 38 | |
| 4 | 2021 | 37 | |
| 5 | 2022 | 26 | |
| 6 | 2023 | 14 | |
| 7 | 2023 | 7 | |
| 8 | 2023 | 5 | |
| 9 | 2021 | 4 | |
| 10 | 2021 | 4 | |
| 11 | 2025 | 3 |
About Blessing Sibanda
Blessing Sibanda is a scholar working on Artificial Intelligence, Safety Research, Plant Science, Nuclear Energy and Engineering and Waste Management and Disposal, having authored 11 papers that have together received 408 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Topic Modeling (4 papers), Smart Agriculture and AI (2 papers), Ethics and Social Impacts of AI (1 paper) and Text and Document Classification Technologies (1 paper). The work is most often cited by research in Artificial Intelligence (297 citations), Safety Research (42 citations), Health Informatics (4 citations), Information Systems (36 citations) and Human-Computer Interaction (6 citations). Blessing Sibanda has collaborated with scholars based in Uganda, South Africa and Germany. Frequent co-authors include Gloria Ejehiohen Iyawa, David Ifeoluwa Adelani, Jade Abbott and Anuoluwapo Aremu. Their work appears in journals such as Patterns, Transactions of the Association for Computational Linguistics, Advances in intelligent systems and computing, Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) and Conference on Empirical Methods in Natural Language Processing (EMNLP).
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.