References
- Zhang L, Hall M, Bastola D. Utilizing Twitter data for analysis of chemotherapy. Int J Med Inform. 2018;120:92–100. doi:https://doi.org/10.1016/j.ijmedinf.2018.10.002.
- de Rosa AS, Fino E, Bocci E. Addressing healthcare on-line demand and supply relating to mental illness: knowledge sharing about psychiatry and psychoanalysis through SN in Italy and France. In: Kapoor A, Kulshrestha C, editors. Dynamics of competitive advantage and consumer perception in social marketing. Hershey, PA: IGI Global; 2013. p. 16–55.
- De Martino I, D'Apolito R, McLawhorn AS, Fehring KA, Sculco PK, Gasparini G. Social media for patients: benefits and drawbacks. Curr Rev Musculoskelet Med. 2017;10(1):141–5. doi:https://doi.org/10.1007/s12178-017-9394-7.
- Sansone A, Cignarelli A, Ciocca G, Pozza C, Giorgino F, Romanelli F, Jannini EA. The sentiment analysis of tweets as a new tool to measure public perception of male erectile and ejaculatory dysfunctions. Sex Med. 2019;7(4):464–71. doi:https://doi.org/10.1016/j.esxm.2019.07.001.
- Stellefson M, Chaney B, Ochipa K, Chaney D, Haider Z, Hanik B, Chavarria E, Bernhardt JM. YouTube as a source of chronic obstructive pulmonary disease patient education: a social media content analysis. Chron Respir Dis. 2014;11(2):61–71. doi:https://doi.org/10.1177/1479972314525058.
- Hinjoy S, Tsukayama R, Chuxnum T, Masunglong W, Sidet C, Kleeblumjeak P, Onsai N, Iamsirithaworn S. Self-assessment of the Thai Department of Disease Control's communication for international response to COVID-19 in the early phase. Int J Infect Dis. 2020;96:205–10. doi:https://doi.org/10.1016/j.ijid.2020.04.042.
- Limaye RJ, Sauer M, Ali J, Bernstein J, Wahl B, Barnhill A, Labrique A. Building trust while influencing online COVID-19 content in the social media world. Lancet Digit Health. 2020;2(6):e277–8. doi:https://doi.org/10.1016/S2589-7500(20)30084-4.
- Hu Y, Sun J, Dai Z, Deng H, Li X, Huang Q, Wu Y, Sun L, Xu Y. Prevalence and severity of corona virus disease 2019 (COVID-19): a systematic review and meta-analysis. J Clin Virol. 2020;127:104371. doi:https://doi.org/10.1016/j.jcv.2020.104371.
- Ibarra-Vega D. Lockdown, one, two, none, or smart. Modeling containing Covid-19 infection. A conceptual model. Sci Total Environ. 2020;730:138917. doi:https://doi.org/10.1016/j.scitotenv.2020.138917.
- Kar SK, Arafat SMY, Sharma P, Dixit A, Marthoenis M, Kabir R. COVID-19 pandemic and addiction: current problems and future concerns. Asian J Psychiatr. 2020;51:102064. doi:https://doi.org/10.1016/j.ajp.2020.102064.
- Rossi R, Socci V, Talevi D, Mensi S, Niolu C, Pacitti F, Marco AD, Rossi A, Siracusano A, Lorenzo GD. COVID-19 pandemic and lockdown measures impact on mental health among the general population in Italy. An N = 18147 web-based survey. MedRxiv. 2020; 2020.04.09.20057802. doi:https://doi.org/10.1101/2020.04.09.20057802.
- Mackolil J, Mackolil J. Addressing psychosocial problems associated with the COVID-19 lockdown. Asian J Psychiatr. 2020;51:102156. doi:https://doi.org/10.1016/j.ajp.2020.102156.
- Király O, Potenza MN, Stein DJ, King DL, Hodgins DC, Saunders JB, Griffiths MD, Gjoneska B, Billieux J, Brand M, et al. Preventing problematic internet use during the COVID-19 pandemic: consensus guidance. Compr Psychiatry. 2020;100:152180. doi:https://doi.org/10.1016/j.comppsych.2020.152180.
- Dubey MJ, Ghosh R, Chatterjee S, Biswas P, Chatterjee S, Souvik D. COVID-19 and addiction. Diabetes Metab Syndr. 2020;14(5):817–23. doi:https://doi.org/10.1016/j.dsx.2020.06.008.
- Mallet J, Dubertret C, Le Strat Y. Addictions in the COVID-19 era: current evidence, future perspectives a comprehensive review. Prog Neuropsychopharmacol Biol Psychiatry. 2021;106:110070. doi:https://doi.org/10.1016/j.pnpbp.2020.110070.
- Satre DD, Hirschtritt ME, Silverberg MJ, Sterling SA. Addressing problems with alcohol and other substances among older adults during the COVID-19 pandemic. Am J Geriatr Psychiatry. 2020;28(7):780–3. doi:https://doi.org/10.1016/j.jagp.2020.04.012.
- American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 5th Edition: DSM-5. 5th ed. Arlington, VA: American Psychiatric Publishing; 2013.
- Griffiths MD. Problem gambling and gambling addiction are not the same. J Addict Depend. 2016;2(1):1–3. doi:https://doi.org/10.15436/2471-061X.16.014.
- Griffiths MD. A “components” model of addiction within a biopsychosocial framework. J Subst Use. 2005;10(4):191–7. doi:https://doi.org/10.1080/14659890500114359.
- Iliceto P, Fino E, Schiavella M, Candilera G. Individual differences in interpersonal security predict suicidal ideation and problem gambling. Pers Individ Diff. 2020;162:110031. doi:https://doi.org/10.1016/j.paid.2020.110031.
- Gainsbury SM, Hing N, Suhonen N. Professional help-seeking for gambling problems: awareness, barriers and motivators for treatment. J Gambl Stud. 2014;30(2):503–19. doi:https://doi.org/10.1007/s10899-013-9373-x.
- Abbott MW. The changing epidemiology of gambling disorder and gambling-related harm: public health implications. Public Health. 2020;184:41–5. doi:https://doi.org/10.1016/j.puhe.2020.04.003.
- Binde P, Romild U, Volberg RA. Forms of gambling, gambling involvement and problem gambling: evidence from a Swedish population survey. Int Gambl Stud. 2017;17(3):490–507. doi:https://doi.org/10.1080/14459795.2017.1360928.
- Gainsbury SM. Online gambling addiction: the relationship between internet gambling and disordered gambling. Curr Addict Rep. 2015;2(2):185–93. doi:https://doi.org/10.1007/s40429-015-0057-8.
- Griffiths M. Internet gambling: preliminary results of the first U.K. prevalence study. JGI. 2001;5(5):5. doi:https://doi.org/10.4309/jgi.2001.5.8.
- Price A. Online gambling in the midst of COVID-19: a nexus of mental health concerns, substance use and financial stress. Int J Mental Health Addict. 2020;1–18. doi:https://doi.org/10.1007/s11469-020-00366-1.
- Wardle H, Moody A, Griffiths MD, Orford J, Volberg R. Defining the online gambler and patterns of behavior integration: evidence from the British Gambling Prevalence Survey 2010. Int Gambl Stud. 2011;11(3):339–56. doi:https://doi.org/10.1080/14459795.2011.628684.
- Sharman S, Roberts A, Bowden-Jones H, Strang J. Gambling in COVID-19 Lockdown in the UK: Depression, Stress, and Anxiety. Front Psychiatry [Internet]. 2021 [cited 2021 Feb 10];12. Available from: https://doi.org/https://www.frontiersin.org/articles/10/3389/fpsyt.2021.621497/full.
- Gainsbury SM, Delfabbro P, King DL, Hing N. An exploratory study of gambling operators' use of social media and the latent messages conveyed. J Gambl Stud. 2016;32(1):125–41. doi:https://doi.org/10.1007/s10899-015-9525-2.
- Håkansson A. Changes in gambling behavior during the COVID-19 pandemic—a web survey study in Sweden. IJERPH. 2020;17(11):4013. doi:https://doi.org/10.3390/ijerph17114013.
- Auer M, Malischnig D, Griffiths MD. Gambling before and during the COVID-19 pandemic among European regular sports bettors: An empirical study using behavioral tracking data. Int J Mental Health Addict. 2020;1–8. Advance online publication. doi:https://doi.org/10.1007/s11469-020-00327-8.
- Auer M, Griffiths MD. Gambling before and during the COVID-19 pandemic among online casino gamblers: an empirical study using behavioral tracking data. Int J Mental Health Addict. 2021;1–11. Advance online publication. doi:https://doi.org/10.1007/s11469-020-00462-2.
- Vasudevan M, Mehrolia S, Alagarsamy S, Balachandran AK. Work from home in the pandemic era: loss of mental equilibrium? Asian J Psychiatr. 2021;55:102490. doi:https://doi.org/10.1016/j.ajp.2020.102490.
- Charles-Smith LE, Reynolds TL, Cameron MA, Conway M, Lau EHY, Olsen JM, Pavlin JA, Shigematsu M, Streichert LC, Suda KJ, et al. Using social media for actionable disease surveillance and outbreak management: a systematic literature review. PLoS One. 2015;10(10):e0139701. doi:https://doi.org/10.1371/journal.pone.0139701.
- Himelboim I, Han JY. Cancer talk on Twitter: community structure and information sources in breast and prostate cancer social networks. J Health Commun. 2014;19(2):210–25. doi:https://doi.org/10.1080/10810730.2013.811321.
- Sugawara Y, Narimatsu H, Hozawa A, Shao L, Otani K, Fukao A. Cancer patients on Twitter: a novel patient community on social media. BMC Res Notes. 2012;5(1):699. doi:https://doi.org/10.1186/1756-0500-5-699.
- Clement J. Twitter: number of monthly active users 2010-2019. Statista; 2019. https://www.statista.com/statistics/282087/number-of-monthly-active-twitter-users.
- Houghton S, McNeil A, Hogg M, Moss M. Comparing the Twitter posting of British gambling operators and gambling affiliates: a summative content analysis. Int Gambl Stud. 2020;36:1–23. doi:https://doi.org/10.1080/14459795.2018.1561923.
- Killick EA, Griffiths MD. A content analysis of gambling operators’ Twitter accounts at the start of the English Premier League football season. J Gambl Stud. 2020;36(1):319–41. doi:https://doi.org/10.1007/s10899-019-09879-4.
- Wood & Griffiths (2008) to the reference list: Wood RTA, Griffiths MD. Why Swedish people play online poker and factors that can increase or decrease trust in poker Web sites: A qualitative investigation. Journal of Gambling Issues. 2008 Jun 1;0(21):80–97. doi:https://doi.org/http://dx.doi.org/10/4309/jgi.2008.21.8
- Blair RA, Morse BS, Tsai LL. Public health and public trust: survey evidence from the Ebola virus disease epidemic in Liberia. Soc Sci Med. 2017;172:89–97. doi:https://doi.org/10.1016/j.socscimed.2016.11.016.
- Vallurupalli V, Bose I. Exploring thematic composition of online reviews: a topic modeling approach. Electron Markets. 2020;30(4):791–804. doi:https://doi.org/10.1007/s12525-020-00397-5.
- World Health Organization. Novel coronavirus. 2020 [accessed 2020 Dec 14] https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
- Kearney M. rtweet: collecting and analyzing Twitter data. JOSS. 2019;4(42):1829. doi:https://doi.org/10.21105/joss.01829.
- Twitter. Twitter developer—API reference index. 2021 [accessed 2021 Feb 15] https://developer.twitter.com/en/docs/api-reference-index.
- Aguilar-Gallegos N, Romero-García LE, Martínez-González EG, García-Sánchez EI, Aguilar-Ávila J. Dataset on dynamics of coronavirus on Twitter. Data Brief. 2020;30:105684. doi:https://doi.org/10.1016/j.dib.2020.105684.
- Mariano ER, Kou A, Stiegler MA, Matava C. The rise and fall of the COVID-19 aerosol box through the lens of Twitter. J Clin Anesth. 2020;69:110145. doi:https://doi.org/10.1016/j.jclinane.2020.110145.
- Ogundepo E, Folorunso S, Adekanmbi O, Akinsande O, Banjo O, Ogbuju E, Oladipo F, Abimbola O, Oseghale E, Babajide O. An exploratory assessment of a multidimensional healthcare and economic data on COVID-19 in Nigeria. Data Brief. 2020;33:106424. doi:https://doi.org/10.1016/j.dib.2020.106424.
- Trovato CM, Montuori M, Oliva S, Cucchiara S, Cignarelli A, Sansone A. Assessment of public perceptions and concerns of celiac disease: a Twitter-based sentiment analysis study. Dig Liver Dis. 2020;52(4):464–6. doi:https://doi.org/10.1016/j.dld.2020.02.004.
- Dunford D, Dale B, Stylianou N, Lowther E, Ahmed M, de la Torre Arenas I. Coronavirus: the world in lockdown in maps and charts. BBC News; 2020 [accessed 2020 Dec 14]. https://www.bbc.co.uk/news/world-52103747.
- Lopez-Gonzalez H, Guerrero-Solé F, Estévez A, Griffiths MD. Betting is loving and bettors are predators: a conceptual metaphor approach to online sports betting advertising. J Gambl Stud. 2018;34(3):709–26. doi:https://doi.org/10.1007/s10899-017-9727-x.
- Bickel MW. Reflecting trends in the academic landscape of sustainable energy using probabilistic topic modeling. Energ Sustain Soc. 2019;9(1):49. doi:https://doi.org/10.1186/s13705-019-0226-z.
- Symeonidis S, Effrosynidis D, Arampatzis A. A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis. Expert Syst Appl. 2018;110:298–310. doi:https://doi.org/10.1016/j.eswa.2018.06.022.
- Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, Grolemund G, Hayes A, Henry L, Hester J, et al. Welcome to the Tidyverse. JOSS. 2019;4(43):1686. doi:https://doi.org/10.21105/joss.01686.
- Silge J, Robinson D, Hester J. Tidytext: text mining using Dplyr, Ggplot2, and other Tidy tools; 2016 [accessed 2020 Dec 14]. doi:https://doi.org/10.5281/zenodo.56714.
- Silge J, Robinson D. Text mining with R: a tidy approach. 1st ed. Sebastopol, CA: O’Reilly Media; 2017.
- Yan X, Guo J, Lan Y, Cheng X. A biterm topic model for short texts. Proceedings of the 22nd International Conference on World Wide Web (WWW ‘13); 2013. p. 1445–56. doi:https://doi.org/10.1145/2488388.2488514.
- Blei DM. Probabilistic topic models. Commun ACM. 2012;55(4):77–84. doi:https://doi.org/10.1145/2133806.2133826.
- Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.
- Qiu Z, Wu B, Wang B, Shi C, Yu L. Collapsed Gibbs sampling for latent Dirichlet allocation on spark. Proceedings of the 3rd International Conference on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, Vol. 36, 2014. p. 17–28.
- Wijffels J. BTM: Biterm Topic Models for Short Text. R package version 0.3; 2020 [accessed 2020 Dec 14] https://CRAN.R-project.org/package=BTM.
- Roberts ME, Stewart BM, Tingley D. stm: an R package for structural topic models. J Stat Soft. 2019;91(2):1–40. doi:https://doi.org/10.18637/jss.v091.i02.
- Bischof J, Airoldi EM. Capturing topical content with frequency and exclusivity. Proceedings of the 29th International Conference on Machine Learning; 2012. p. 9–16. https://icml.cc/2012/papers/113.pdf
- Griffiths TL, Steyvers M. Finding scientific topics. Proce Natl Acad Sci. 2004;101(Supplement 1):5228–35. doi:https://doi.org/10.1073/pnas.0307752101.
- Sun L, Yin Y. Discovering themes and trends in transportation research using topic modeling. Transp Res Part C Emerg Technol. 2017;77:49–66. doi:https://doi.org/10.1016/j.trc.2017.01.013.
- Jockers ML. Syuzhet: extract sentiment and plot arcs from text; 2015 https://github.com/mjockers/syuzhet. Accessed 10 May 2020.
- Yada A, Jha CK, Sharan A, Vaish V. Sentiment analysis of financial news using unsupervised approach. Procedia Comput Sci. 2020;167:589–98. doi:https://doi.org/10.1016/j.procs.2020.03.325.
- Mohammad S, Turney P. Crowdsourcing a word-emotion association lexicon. Comput Intell. 2013;29(3):436–65. doi:https://doi.org/10.1111/j.1467-8640.2012.00460.x.
- Reinert A. Une méthode de classification descendante hiérarchique: application à l’analyse lexicale par contexte [A hierarchical descendent classification method: application to lexical analysis by context]. Cahiers L’Analyse Données. 1983;8(2):187–98.
- Sirola A, Kaakinen M, Savolainen I, Oksanen A. Loneliness and online gambling-community participation of young social media users. Comput Hum Behav. 2019;95:136–45. doi:https://doi.org/10.1016/j.chb.2019.01.023.
- Bücker L, Bierbrodt J, Hand I, Wittekind C, Moritz S. Effects of a depression-focused internet intervention in slot machine gamblers: a randomized controlled trial. PLoS One. 2018;13(6):e0198859. doi:https://doi.org/10.1371/journal.pone.0198859.
- Casey LM, Oei TPS, Raylu N, Horrigan K, Day J, Ireland J, Bonnie A. Internet-based delivery of cognitive behaviour therapy compared to monitoring, feedback and support for problem gambling: a randomised controlled trial. J Gambl Stud. 2017;33(3):993–1010. doi:https://doi.org/10.1007/s10899-016-9666-y.
- Ransing R, Adiukwu F, Pereira-Sanchez V, Ramalho R, Orsolini L, Teixeira ALS, Gonzalez-Diaz JM, Pinto da Costa M, Soler-Vidal J, Bytyçi DG, et al. Mental health interventions during the COVID-19 pandemic: a conceptual framework by early career psychiatrists. Asian J Psychiatr. 2020;51:102085. doi:https://doi.org/10.1016/j.ajp.2020.102085.
- Gambling Commission. Customer interaction—additional formal guidance for remote operators during COVID-19 outbreak. Gambling Commission; 2020 [accessed 2020 Dec 14] http://www.gamblingcommission.gov.uk/news-action-and-statistics/Statistics-and-research/Covid-19-research/Customer-interaction-%E2%80%93-Additional-formal-guidance-for-remote-operators-during-COVID-19-outbreak.aspx.