| Data mining and content analysis of the Chinese social media platform Weibo during the early COVID-19 outbreak: retrospective observational infoveillance study J Li, Q Xu, R Cuomo, V Purushothaman, T Mackey JMIR Public Health and Surveillance 6 (2), e18700, 2020 | 208 | 2020 |
| Machine learning to detect self-reporting of symptoms, testing access, and recovery associated with COVID-19 on Twitter: retrospective big data infoveillance study T Mackey, V Purushothaman, J Li, N Shah, M Nali, C Bardier, B Liang, ... JMIR public health and surveillance 6 (2), e19509, 2020 | 162 | 2020 |
| Big data, natural language processing, and deep learning to detect and characterize illicit COVID-19 product sales: Infoveillance study on Twitter and Instagram TK Mackey, J Li, V Purushothaman, M Nali, N Shah, C Bardier, M Cai, ... JMIR public health and surveillance 6 (3), e20794, 2020 | 110 | 2020 |
| A machine learning approach for the detection and characterization of illicit drug dealers on instagram: model evaluation study J Li, Q Xu, N Shah, TK Mackey Journal of medical Internet research 21 (6), e13803, 2019 | 93 | 2019 |
| Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during COVID-19 using unsupervised machine learning and social network analysis MR Haupt, A Jinich-Diamant, J Li, M Nali, TK Mackey Online Social Networks and Media 21, 100114, 2021 | 70 | 2021 |
| Application of unsupervised machine learning to identify and characterise hydroxychloroquine misinformation on Twitter TK Mackey, V Purushothaman, M Haupt, MC Nali, J Li The Lancet Digital Health 3 (2), e72-e75, 2021 | 51 | 2021 |
| Use of machine learning to detect wildlife product promotion and sales on Twitter Q Xu, J Li, M Cai, TK Mackey Frontiers in big Data 2, 28, 2019 | 46 | 2019 |
| Identification and characterization of tweets related to the 2015 Indiana HIV outbreak: A retrospective infoveillance study M Cai, N Shah, J Li, WH Chen, RE Cuomo, N Obradovich, TK Mackey PloS one 15 (8), e0235150, 2020 | 22 | 2020 |
| An unsupervised machine learning approach for the detection and characterization of illicit drug-dealing comments and interactions on Instagram N Shah, J Li, TK Mackey Substance Abuse 43 (1), 273-277, 2022 | 21 | 2022 |
| Sub-national longitudinal and geospatial analysis of COVID-19 tweets RE Cuomo, V Purushothaman, J Li, M Cai, TK Mackey Plos one 15 (10), e0241330, 2020 | 19 | 2020 |
| Detection of self-reported experiences with corruption on twitter using unsupervised machine learning J Li, WH Chen, Q Xu, N Shah, JC Kohler, TK Mackey Social Sciences & Humanities Open 2 (1), 100060, 2020 | 19 | 2020 |
| Applying topic modelling and qualitative content analysis to identify and characterise ENDS product promotion and sales on Instagram N Shah, M Nali, C Bardier, J Li, J Maroulis, R Cuomo, TK Mackey Tobacco Control, 2021 | 15 | 2021 |
| Leveraging big data to identify corruption as an SDG goal 16 humanitarian technology J Li, WH Chen, Q Xu, N Shah, T Mackey 2019 IEEE Global Humanitarian Technology Conference (GHTC), 1-4, 2019 | 12 | 2019 |
| Detecting suicide and self-harm discussions among opioid substance users on Instagram using machine learning V Purushothaman, J Li, TK Mackey Frontiers in psychiatry 12, 551296, 2021 | 8 | 2021 |
| Evaluation of Hybrid Unsupervised and Supervised Machine Learning Approach to Detect Self-Reporting of COVID-19 Symptoms on Twitter M Cai, J Li, M Nali, TK Mackey 2021 IEEE International Conference on Communications Workshops (ICC …, 2021 | 7 | 2021 |
| Characterizing alternative and emerging tobacco product transition of use behavior on Twitter C Bardier, JS Yang, J Li, TK Mackey BMC Research Notes 14 (1), 1-10, 2021 | 2 | 2021 |
| Detection of health sector corruption-related self-reports on twitter using unsupervised machine learning J Li, T Mackey, WH Chen, Q Xu, N Shah, J Kohler APHA's 2020 VIRTUAL Annual Meeting and Expo (Oct. 24-28), 2020 | | 2020 |
