{"doi":"10.1101/2022.04.27.22274390","title":"Can accurate demographic information about people who use prescription medications non-medically be derived from Twitter?","abstract":"Abstract Traditional surveillance mechanisms for nonmedical prescription medication use (NPMU) involve substantial lags. Social media-based approaches have been proposed for conducting close-to-real-time surveillance, but such methods typically cannot provide fine-grained statistics about subpopulations. We address this gap by developing methods for automatically characterizing a large Twitter NPMU cohort (n=288,562) in terms of age-group, race, and gender. Our methods achieved 0.88 precision (95%-CI: 0.84-0.92) for age-group, 0.90 (95%-CI: 0.85-0.95) for race, and 0.94 accuracy (95%-CI: 0.92-0.97) for gender. We compared the automatically-derived statistics for the NPMU of tranquilizers, stimulants, and opioids from Twitter to statistics reported in traditional sources ( eg ., the National Survey on Drug Use and Health). Our estimates were mostly consistent with the traditional sources, except for age-group-related statistics, likely caused by differences in reporting tendencies and representations in the population. Our study demonstrates that subpopulation-specific estimates about NPMU may be automatically derived from Twitter to obtain early insights.","journal":"medRxiv","year":2022,"id":303156,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9419,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":371225,"name":"Mohammed Ali Al-Garadi","orcid":"0000-0002-6991-2687","position":1,"is_corresponding":false},{"id":830030,"name":"Jennifer S. Love","orcid":"0000-0002-5882-4390","position":2,"is_corresponding":false},{"id":273387,"name":"Hannah L. F. Cooper","orcid":"0000-0002-0521-2438","position":3,"is_corresponding":false},{"id":371226,"name":"Jeanmarie Perrone","orcid":"0000-0001-7073-9060","position":4,"is_corresponding":false},{"id":96345,"name":"Abeed Sarker","orcid":"0000-0001-7358-544X","position":5,"is_corresponding":false},{"id":556280,"name":"Yuan‐Chi Yang","orcid":"0000-0001-5441-8371","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-19T00:32:20.348385Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}