| A large-scale COVID-19 Twitter chatter dataset for open scientific research—an international collaboration JM Banda, R Tekumalla, G Wang, J Yu, T Liu, Y Ding, E Artemova, ... Epidemiologia 2 (3), 315-324, 2021 | 363 | 2021 |
| Social media mining toolkit (SMMT) R Tekumalla, JM Banda Genomics & informatics 18 (2), 2020 | 40 | 2020 |
| A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research–An International Collaboration. Epidemiologia 2, 315–324 JM Banda, R Tekumalla, G Wang, J Yu, T Liu, Y Ding, E Artemova, ... Aug, 2021 | 20 | 2021 |
| Mining Archive. org’s twitter stream grab for pharmacovigilance research gold R Tekumalla, JR Asl, JM Banda Proceedings of the International AAAI Conference on Web and Social Media 14 …, 2020 | 20 | 2020 |
| Characterizing drug mentions in COVID-19 Twitter Chatter R Tekumalla, JM Banda Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, 2020 | 17* | 2020 |
| Using weak supervision to generate training datasets from social media data: a proof of concept to identify drug mentions R Tekumalla, JM Banda Neural Computing and Applications 35 (25), 18161-18169, 2023 | 15 | 2023 |
| A large-scale Twitter dataset for drug safety applications mined from publicly existing resources R Tekumalla, JM Banda arXiv preprint arXiv:2003.13900, 2020 | 8 | 2020 |
| Characterizing Anti-Asian Rhetoric During The COVID-19 Pandemic: A Sentiment Analysis Case Study on Twitter R Tekumalla, Z Baig, M Pan, LAR Hernandez, M Wang, JM Banda Workshop Proceedings of the 16th International AAAI Conference on Web and …, 2022 | 7 | 2022 |
| TweetDIS: A large twitter dataset for natural disasters built using weak supervision R Tekumalla, JM Banda 2022 IEEE International Conference on Big Data (Big Data), 4816-4823, 2022 | 4 | 2022 |
| An enhanced approach to identify and extract medication mentions in tweets via weak supervision R Tekumalla, JM Banda Proceedings of the BioCreative VII Challenge Evaluation Workshop, 2021 | 4 | 2021 |
| Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets R Tekumalla, JM Banda International Conference on Human-Computer Interaction, 356-366, 2023 | 2 | 2023 |
| Automatic extraction of medication mentions from tweets—overview of the biocreative VII shared task 3 competition D Weissenbacher, K O’Connor, S Rawal, Y Zhang, RTH Tsai, T Miller, ... Database 2023, baac108, 2023 | 2 | 2023 |
| Identifying epidemic related Tweets using noisy learning R Tekumalla, JM Banda arXiv preprint arXiv:2209.12614, 2022 | 2 | 2022 |
| An empirical study on characterizing natural disasters in class imbalanced social media data using weak supervision R Tekumalla, JM Banda 2022 IEEE International Conference on Big Data (Big Data), 4824-4832, 2022 | 1 | 2022 |
| When Silver Is As Good As Gold: Using Weak Supervision to Train Machine Learning Models on Social Media Data R Tekumalla Georgia State University, 2022 | 1 | 2022 |
| Testing an informatics consulting service for systematic bias using negative control reference sets M Jackson, S Gombar, R Manickam, R Brown, R Tekumalla, P Ballentine, ... | | 2024 |
| Towards automatic identification of self-reported COVID-19 tweets: Introducing a multilingual manually annotated dataset, baseline systems and exploratory evaluations R Tekumalla, LAR Hernandez, JM Banda 2023 IEEE International Conference on Big Data (BigData), 3736-3742, 2023 | | 2023 |
| Towards Lexical, Semantic and Similarity Search in Phenotype Libraries R Tekumalla, R Manickam, Y Low Observational Health Data Sciences and Informatics, 2022 | | 2022 |
| Leveraging the OHDSI vocabulary to characterize the COVID-19 epidemic using Twitter data and NLP R Tekumalla, JM Banda Observational Health Data Sciences and Informatics, 0 | | |
Juan M. BandaStanford Health CareVerified email at stanfordhealthcare.org
