{"doi":"10.1101/2020.05.27.20114983","title":"A call for governments to pause Twitter censorship: a cross-sectional study using Twitter data as social-spatial sensors of COVID-19/SARS-CoV-2 research diffusion","abstract":"<h4>ABSTRACT</h4> <h4>Objectives</h4> To determine whether Twitter data can be used as social-spatial sensors to show how research on COVID-19/SARS-CoV-2 diffuses through the population to reach the people that are especially affected by the disease. <h4>Design</h4> Cross-sectional bibliometric analysis conducted between 23 rd March and 14 th April 2020. <h4>Setting</h4> Three sources of data were used in the analysis: (1) deaths per number of population for COVID-19/SARS-CoV-2 retrieved from Coronavirus Resource Center at John Hopkins University and Worldometer, (2) publications related to COVID-19/SARS-CoV-2 retrieved from WHO COVID-19 database of global publications, and (3) tweets of these publications retrieved from Altmetric.com and Twitter. <h4>Main Outcome(s) and Measure(s)</h4> To map Twitter activity against number of publications and deaths per number of population worldwide and in the USA states. To determine the relationship between number of tweets as dependent variable and deaths per number of population and number of publications as independent variables. <h4>Results</h4> Deaths per one hundred thousand population for countries ranged from 0 to 104, and deaths per one million population for USA states ranged from 2 to 513. Total number of publications used in the analysis was 1761, and total number of tweets used in the analysis was 751,068. Mapping of worldwide data illustrated that high Twitter activity was related to high numbers of COVID-19/SARS-CoV-2 deaths, with tweets inversely weighted with number of publications. Poisson regression models of worldwide data showed a positive correlation between the national deaths per number of population and tweets when holding the country’s number of publications constant (coefficient 0.0285, S.E. 0.0003, p<0.001). Conversely, this relationship was negatively correlated in USA states (coefficient –0.0013, S.E. 0.0001, p<0.001). <h4>Conclusions</h4> This study shows that Twitter can play a crucial role in the rapid research response during the COVID-19/SARS-CoV-2 global pandemic, especially to spread research with prompt public scrutiny. Governments are urged to pause censorship of social media platforms during these unprecedented times to support the scientific community’s fight against COVID-19/SARS-CoV-2. <h4>SUMMARY BOX</h4> <h4>What is already known on this topic</h4> Twitter is progressively being used by researchers to share information and knowledge transfer. Tweets can be used as ‘social sensors’, which is the concept of transforming a physical sensor in the real world through social media analysis. Previous studies have shown that social sensors can provide insight into major social and physical events. <h4>What this study adds</h4> Using Twitter data used as social-spatial sensors, we demonstrated that Twitter activity was significantly positively correlated to the numbers of COVID-19/SARS-CoV-2 deaths, when holding the country’s number of publications constant. Twitter can play a crucial role in the rapid research response during the COVID-19/SARS-CoV-2 global pandemic.","journal":null,"year":2020,"id":119,"datarank":0.3068123308828442,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.09886817671486058,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.09886817671486058,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":2,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0336,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-05-29","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":839,"name":"Robin Haunschild","orcid":"0000-0001-7025-7256","position":1,"is_corresponding":false},{"id":661,"name":"Lutz Bornmann","orcid":"0000-0003-0810-7091","position":2,"is_corresponding":false},{"id":840,"name":"George Garas","orcid":"0000-0001-7468-3287","position":3,"is_corresponding":false},{"id":841,"name":"Vanash Patel","orcid":"0000-0001-9579-8227","position":4,"is_corresponding":false},{"id":838,"name":"Vanash M. Patel","orcid":null,"position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","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":[]}