Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors

We propose to use Twitter data as social-spatial sensors. This study deals with the question whether research papers on certain diseases are perceived by people in regions (worldwide) that are especially concerned by these diseases. Since (some) Twitter data contain location information, it is possible to spatially map the activity of Twitter users referring to certain papers (e.g., dealing with tuberculosis). The resulting maps reveal whether heavy activity on Twitter is correlated with large numbers of people having certain diseases. In this study, we focus on tuberculosis, human immunodeficiency virus (HIV), and malaria, since the World Health Organization ranks these diseases as the top three causes of death worldwide by a single infectious agent. The results of the social-spatial Twitter maps (and additionally performed regression models) reveal the usefulness of the proposed sensor approach. One receives an impression of how research papers on the diseases have been perceived by people in regions that are especially concerned by these diseases. Our study demonstrates a promising approach for using Twitter data for research evaluation purposes beyond simple counting of tweets.

[1]  F. Triguero,et al.  Scientific knowledge percolation process and social impact: A case study on the biotechnology and microbiology perceptions on Twitter , 2018 .

[2]  J. Hilbe Modeling Count Data , 2014, International Encyclopedia of Statistical Science.

[3]  S. Nuti,et al.  The Use of Google Trends in Health Care Research: A Systematic Review , 2014, PloS one.

[4]  Rodrigo Costas,et al.  The unbearable emptiness of tweeting—About journal articles , 2017, PloS one.

[5]  Sam Work Social Media in Scholarly Communication. A Review of the Literature and Empirical Analysis of Twitter Use by SSHRC Doctoral Award Recipients , 2015 .

[6]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[7]  Yin Leng Theng,et al.  Altmetrics: an analysis of the state-of-the-art in measuring research impact on social media , 2016, Scientometrics.

[8]  Hadley Wickham,et al.  'SQLite' Interface for R [R package RSQLite version 2.2.1] , 2020 .

[9]  Nicolás Robinson-García,et al.  Mapping social media attention in Microbiology: Identifying main topics and actors , 2019, FEMS microbiology letters.

[10]  Rémi Toupin A climate of sharing : Who are the users engaging with climate research on Twitter? , 2018 .

[11]  Rodrigo Costas,et al.  How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications , 2014, Scientometrics.

[12]  Stefanie Haustein,et al.  Influence of Study Type on Twitter Activity for Medical Research Papers , 2015, ISSI.

[13]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[14]  Loet Leydesdorff,et al.  Automated Analysis of Topic-Actor Networks on Twitter: New approach to the analysis of socio-semantic networks , 2017, ArXiv.

[15]  Keeheon Lee,et al.  Examining Characteristics of Traditional and Twitter Citation , 2016, Front. Res. Metr. Anal..

[16]  Lutz Bornmann,et al.  Scientific Revolution in Scientometrics: The Broadening of Impact from Citation to Societal , 2016 .

[17]  Kim Holmberg,et al.  Highly tweeted science articles: who tweets them? An analysis of Twitter user profile descriptions , 2017, Scientometrics.

[18]  Ricardo Arencibia Jorge,et al.  A review of altmetrics as an emerging discipline for research evaluation , 2016, Learn. Publ..

[19]  J. Brownstein,et al.  Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. , 2012, The American journal of tropical medicine and hygiene.

[20]  Mohamed K. Haneefa,et al.  Scholarly use of social media , 2016 .

[21]  Ludo Waltman,et al.  F1000 Recommendations as a Potential New Data Source for Research Evaluation: A Comparison With Citations , 2014, J. Assoc. Inf. Sci. Technol..

[22]  A. Darzi,et al.  Twitter and the health reforms in the English National Health Service. , 2013, Health policy.

[23]  Stacy Konkiel,et al.  Altmetrics for librarians: 100+ tips, tricks, and examples , 2016 .

[24]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[25]  Mike Thelwall,et al.  Can alternative indicators overcome language biases in citation counts? A comparison of Spanish and UK research , 2016, Scientometrics.

[26]  Rodrigo Costas,et al.  Social media metrics for new research evaluation , 2018, Springer Handbook of Science and Technology Indicators.

[27]  Jason Priem,et al.  How and why scholars cite on Twitter , 2010, ASIST.

[28]  Vincent Larivière,et al.  Tweets vs. Mendeley readers: How do these two social media metrics differ? , 2014, it Inf. Technol..

[29]  Arkaitz Zubiaga,et al.  Real‐time classification of Twitter trends , 2014, J. Assoc. Inf. Sci. Technol..

[30]  Thed N. van Leeuwen,et al.  SSH & the City. A Network Approach for Tracing the Societal Contribution of the Social Sciences and Humanities for Local Development , 2016, ArXiv.

[31]  #Globalhealth Twitter Conversations on #Malaria, #HIV, #TB, #NCDS, and #NTDS: a Cross-Sectional Analysis. , 2017, Annals of global health.

[32]  G. Sivertsen,et al.  Do national funding organizations properly address the diseases with the highest burden?: Observations from China and the UK , 2020, Scientometrics.

[33]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[34]  Duncan Temple Lang,et al.  General Network (HTTP/FTP/...) Client Interface for R [R package RCurl version 1.98-1.2] , 2020 .

[35]  Vincent Larivière,et al.  Scholarly communication or public communication of science? Assessing who engage with climate research on Twitter , 2019, ISSI.

[36]  Stefanie Haustein,et al.  Grand challenges in altmetrics: heterogeneity, data quality and dependencies , 2016, Scientometrics.

[37]  Tx Station Stata Statistical Software: Release 7. , 2001 .

[38]  Russell V. Lenth,et al.  Regression Models for Categorical Dependent Variables Using Stata (rev.) , 2005 .

[39]  Ian Rowlands,et al.  What are we measuring? Refocusing on some fundamentals in the age of desktop bibliometrics. , 2018, FEMS microbiology letters.

[40]  Hadley Wickham,et al.  ggmap: Spatial Visualization with ggplot2 , 2013, R J..

[41]  Cassidy R. Sugimoto,et al.  Theories of Informetrics and Scholarly Communication , 2016 .

[42]  Kevin Crow,et al.  SHP2DTA: Stata module to converts shape boundary files to Stata datasets , 2015 .

[43]  N. Baumann How to use the medical subject headings (MeSH) , 2016, International journal of clinical practice.

[44]  Stacy Konkiel,et al.  Dimensions: Bringing down barriers between scientometricians and data , 2020, Quantitative Science Studies.

[45]  Tim C. E. Engels,et al.  Limited Dependent Variable Models and Probabilistic Prediction in Informetrics , 2014 .

[46]  J. S. Long,et al.  Regression models for categorical dependent variables using Stata, 2nd Edition , 2005 .

[47]  Alok N. Choudhary,et al.  Real-time disease surveillance using Twitter data: demonstration on flu and cancer , 2013, KDD.

[48]  Henk F. Moed,et al.  Applied Evaluative Informetrics , 2017, Qualitative and Quantitative Analysis of Scientific and Scholarly Communication.

[49]  Joost C. F. de Winter,et al.  The relationship between tweets, citations, and article views for PLOS ONE articles , 2014, Scientometrics.

[50]  R. Costas,et al.  Do altmetric indicators capture societal engagement? A comparison between survey and social media data. , 2017 .

[51]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[52]  Chao Liu,et al.  A probabilistic approach to spatiotemporal theme pattern mining on weblogs , 2006, WWW '06.

[53]  Maurizio Pisati,et al.  SPMAP: Stata module to visualize spatial data , 2007 .

[54]  Bridget M. Kuehn,et al.  Twitter Streams Fuel Big Data Approaches to Health Forecasting. , 2015, JAMA.

[55]  Ying Ding,et al.  Measuring Scholarly Impact , 2014, Springer International Publishing.

[56]  Loet Leydesdorff,et al.  Automated analysis of actor–topic networks on twitter: New approaches to the analysis of socio‐semantic networks , 2019, J. Assoc. Inf. Sci. Technol..

[57]  Lutz Bornmann,et al.  Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK research excellence framework (REF) , 2018, J. Informetrics.

[58]  Mike Thelwall,et al.  Web indicators for research evaluation. Part 2: Social media metrics , 2015 .

[59]  Vincent Larivière,et al.  Tweets as impact indicators: Examining the implications of automated “bot” accounts on Twitter , 2014, J. Assoc. Inf. Sci. Technol..

[60]  Vincent Larivière,et al.  Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature , 2013, J. Assoc. Inf. Sci. Technol..

[61]  Björn Hammarfelt,et al.  Using altmetrics for assessing research impact in the humanities , 2014, Scientometrics.

[62]  Diane H. Sonnenwald,et al.  Association for Information Science and Technology , 2017 .

[63]  Loet Leydesdorff,et al.  Does the public discuss other topics on climate change than researchers? A comparison of networks based on author keywords and hashtags , 2018, J. Informetrics.

[64]  L. Bornmann,et al.  Altmetrics and societal impact measurements: Match or mismatch? A literature review , 2020, El Profesional de la Información.

[65]  Rahmi Oklu,et al.  Social Medicine: Twitter in Healthcare , 2018, Journal of clinical medicine.

[66]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[67]  Lutz Bornmann,et al.  Does evaluative scientometrics lose its main focus on scientific quality by the new orientation towards societal impact? , 2016, Scientometrics.

[68]  Vincent Larivière,et al.  Scholarly use of social media and altmetrics: A review of the literature , 2016, J. Assoc. Inf. Sci. Technol..

[69]  Andreas Meier,et al.  Altmetrics: State of the Art and a Look into the Future , 2018, Scientometrics.

[70]  M. Thelwall Web Indicators for Research Evaluation , 2017 .

[71]  Stefanie Haustein,et al.  Scholarly Twitter metrics , 2018, Springer Handbook of Science and Technology Indicators.

[72]  Alberto M. Segre,et al.  Using Twitter to Estimate H1N1 Influenza Activity , 2010 .

[73]  Houqiang Yu,et al.  Context of altmetrics data matters: an investigation of count type and user category , 2017, Scientometrics.

[74]  Kevin A Padrez,et al.  Twitter as a Tool for Health Research: A Systematic Review , 2017, American journal of public health.

[75]  Michael Thelwall,et al.  Web Indicators for Research Evaluation: A Practical Guide , 2016, Synthesis Lectures on Information Concepts, Retrieval, and Services.

[76]  Lutz Bornmann,et al.  Alternative metrics in scientometrics: a meta-analysis of research into three altmetrics , 2014, Scientometrics.

[77]  Alicia Karspeck,et al.  Real-Time Influenza Forecasts during the 2012–2013 Season , 2013, Nature Communications.

[78]  Holly M. Bik,et al.  An Introduction to Social Media for Scientists , 2013, PLoS biology.

[79]  Mike Thelwall,et al.  Springer Handbook of Science and Technology Indicators , 2019, Springer Handbook of Science and Technology Indicators.