Ahmed, W., Bath, P. A., Sbaffi, L., & Demartini, G. (2019). Novel insights into views towards H1N1 during the 2009 Pandemic: a thematic analysis of Twitter data. Health Information & Libraries Journal, 36(1), 60–72.
Article
Google Scholar
Alshaabi, T., Minot, J. R., Arnold, M. V., Adams, J. L., Dewhurst, D. R., Reagan, A. J., ... & Dodds, P. S. (2020). How the world's collective attention is being paid to a pandemic: COVID-19 related 1-gram time series for 24 languages on Twitter. arXiv preprint arXiv:2003.12614.
Ashby, M. P. (2020). Initial evidence on the relationship between the coronavirus pandemic and crime in the United States. Crime Science, 9, 1–16.
Article
Google Scholar
Ashktorab, Z., Brown, C., Nandi, M., & Culotta, A. (2014). Tweedr: Mining Twitter to inform disaster response. In ISCRAM (pp. 269–272).
Barati, M., Bashirian, S., Jenabi, E., Khazaei, S., Karimi-Shahanjarini, A., Zareian, S., et al. (2020). Factors associated with preventive behaviours of COVID-19 among hospital staff in Iran in 2020: an application of the protection motivation theory. J Hospital Infect, 105, 430.
Article
Google Scholar
Blythe, J. M., & Johnson, S. D. (2019). A systematic review of crime facilitated by the consumer Internet of Things. Security Journal. https://doi.org/10.1057/s41284-019-00211-8.
Article
Google Scholar
Boserup, B., McKenney, M., & Elkbuli, A. (2020). Alarming trends in US domestic violence during the COVID-19 pandemic. The American Journal of Emergency Medicine.
Boyd, D., Golder, S., & Lotan, G. (2010, January). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. In 2010 43rd Hawaii international conference on system sciences (pp. 1–10). IEEE, New York.
Broniatowski, D. A., Paul, M. J., & Dredze, M. (2013). National and local influenza surveillance through Twitter: an analysis of the 2012–2013 influenza epidemic. PLoS ONE, 8(12), e83672.
Article
Google Scholar
Brumfield, C. (2020) Beware malware-laden emails offering COVID-19 information, US secret service warns. CSO. Retrieved 24 April, 2020 from. https://www.csoonline.com/article/3536696/us-secret-service-warns-of-malicious-emails-offering-covid-19-information.html
Buil-Gil, D., Miró-Llinares, F., Moneva, A., Kemp, S., & Díaz-Castaño, N. (2020). Cybercrime and shifts in opportunities during COVID-19: a preliminary analysis in the UK. European Societies, 12, 1–13.
Article
Google Scholar
Caldwell, M., Andrews, J. T. A., Tanay, T., & Griffin, L. D. (2020). AI-enabled future crime. Crime Science, 9(1), 1–13.
Article
Google Scholar
Campbell, A. M. (2020). An increasing risk of family violence during the Covid-19 pandemic: Strengthening community collaborations to save lives. Forensic Science International: Reports, 100089.
Chandan, J. S., Taylor, J., Bradbury-Jones, C., Nirantharakumar, K., Kane, E., & Bandyopadhyay, S. (2020). COVID-19: a public health approach to manage domestic violence is needed. The Lancet Public Health, 5(6), e309.
Article
Google Scholar
Charles-Smith, L. E., Reynolds, T. L., Cameron, M. A., Conway, M., Lau, E. H., Olsen, J. M., et al. (2015). Using social media for actionable disease surveillance and outbreak management: a systematic literature review. PLoS ONE, 10(10), e0139701.
Article
Google Scholar
Chen, E., Lerman, K., & Ferrara, E. (2020). Tracking social media discourse about the covid-19 pandemic: development of a public coronavirus twitter data set. JMIR Public Health and Surveillance, 6(2), e19273.
Article
Google Scholar
Cheong, M., & Lee, V. C. (2011). A microblogging-based approach to terrorism informatics: exploration and chronicling civilian sentiment and response to terrorism events via Twitter. Information Systems Frontiers, 13(1), 45–59.
Article
Google Scholar
Chew, C., & Eysenbach, G. (2010). Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS ONE, 5(11), e14118.
Article
Google Scholar
Cimpanu, C. (2020) FBI says cybercrime reports quadrupled during COVID-19 pandemic. Retrieved 20 April, 2020. https://www.zdnet.com/article/fbi-says-cybercrime-reports-quadrupled-during-covid-19-pandemic/.
Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., & Scala, A. (2020). The covid-19 social media infodemic. arXiv preprint . arXiv:2003.05004.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37–46.
Article
Google Scholar
Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: a routine activity approach. American Sociological Review, 44, 588–608.
Article
Google Scholar
Crump, J. (2011). What are the police doing on Twitter? Social media, the police and the public. Policy & Internet, 3(4), 1–27.
Article
Google Scholar
Dekker, R., van den Brink, P., & Meijer, A. (2020). Social media adoption in the police: Barriers and strategies. Government Information Quarterly, 101441.
Denef, S., Bayerl, P. S., & Kaptein, N. A. (2013). Social media and the police: Tweeting practices of British police forces during the August 2011 riots. In proceedings of the SIGCHI conference on human factors in computing systems (pp. 3471–3480).
Diaz-Aviles, E., & Stewart, A. (2012). Tracking twitter for epidemic intelligence: case study: Ehec/hus outbreak in Germany, 2011. In Proceedings of the 4th annual ACM web science conference (pp. 82–85).
Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases, 20(5), 533–534.
Article
Google Scholar
Ekblom, P. (2011). Crime prevention, security and community safety using the 5Is framework. Berlin: Springer.
Book
Google Scholar
Elgabry, M., Nesbeth, D., & Johnson, S. D. (2020). A systematic review protocol for crime trends facilitated by synthetic biology. Systematic Reviews, 9(1), 22.
Article
Google Scholar
Felson, M., Jiang, S., & Xu, Y. (2020). Routine activity effects of the Covid-19 pandemic on burglary in Detroit, March 2020. Crime Science, 9(1), 1–7.
Article
Google Scholar
Fernandez, M., Dickinson, T., & Alani, H. (2017, September). An analysis of UK policing engagement via social media. In International Conference on Social Informatics (pp. 289–304). Springer, Cham.
Fielding, N., and Caddick, N. (n.d). Police communications and social media. OSCAR Working Paper #02. https://crimeandsecurity.org/feed/2017/5/8/. police-communications-and- social-media. Accessed 23/09/2020
Floyd, D. L., Prentice-Dunn, S., & Rogers, R. W. (2000). A meta-analysis of research on protection motivation theory. Journal of applied social psychology, 30(2), 407–429.
Article
Google Scholar
Fong, E., & Chang, L. Y. (2011). Community under stress: Trust, reciprocity, and community collective efficacy during SARS outbreak. Journal of community health, 36(5), 797–810.
Article
Google Scholar
Greening, L. (1997). Adolescents' cognitive appraisals of cigarette smoking: an application of the protection motivation theory 1. Journal of Applied Social Psychology, 27(22), 1972–1985.
Article
Google Scholar
Grimmelikhuijsen, S. G., & Meijer, A. J. (2015). Does Twitter increase perceived police legitimacy? Public Administration Review, 75(4), 598–607.
Article
Google Scholar
Grover, S., & Aujla, G. S. (2015). Twitter data based prediction model for influenza epidemic. In 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 873–879). IEEE, New York.
Hakak, S., Khan, W. Z., Imran, M., Choo, K. K. R., & Shoaib, M. (2020). Have you been a victim of COVID-19-related cyber incidents? Survey, taxonomy, and mitigation strategies. IEEE Access, 8, 124134–124144.
Article
Google Scholar
Halford, E., Dixon, A., Farrell, G., Malleson, N., & Tilley, N. (2020). Crime and coronavirus: social distancing, lockdown, and the mobility elasticity of crime. Crime Science, 9(1), 1–12.
Article
Google Scholar
Halpern, D. (2015). Inside the Nudge Unit: How small changes can make a big difference. London: WH Allen.
Google Scholar
Hawdon, J., Parti, K., & Dearden, T. E. (2020). Cybercrime in America amid COVID-19: the Initial Results from a Natural Experiment. American Journal of Criminal Justice, 1–17.
Heverin, T., & Zach, L. (2010). Twitter for city police department information sharing. Proceedings of the American Society for Information Science and Technology, 47(1), 1–7.
Article
Google Scholar
Hong, L., Dan, O., & Davison, B. D. (2011, March). Predicting popular messages in twitter. In Proceedings of the 20th international conference companion on World wide web (pp. 57–58).
Houston, J. B., Hawthorne, J., Perreault, M. F., Park, E. H., Goldstein Hode, M., Halliwell, M. R., et al. (2015). Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters, 39(1), 1–22.
Article
Google Scholar
Imran, M., Elbassuoni, S., Castillo, C., Diaz, F., & Meier, P. (2013). Extracting information nuggets from disaster-Related messages in social media. In Iscram.
Ji, X., Chun, S. A., & Geller, J. (2012, April). Epidemic outbreak and spread detection system based on twitter data. In International Conference on Health Information Science (pp. 152–163). Springer, Berlin, Heidelberg.
Johnson, S.D., Ekblom, P., Laycock, G., Frith, M.J., Sombatraung, N., Valdez, E.R. (2018). Future Crime. In R. Wortley, Sidebottom, A., Tilley, N., and Laycock, G (Eds.) Routledge Handbook of Crime Science.
Kearney, M. W., Heiss, A., & Briatte, F. (2019). Packagrtweet: Collecting Twitter Data. R Package Version 0.6. 9e ‘Rtweet’Title Collecting Twitter Data.
Kemp, S. (2020). COVID-19, Protection Motivation Theory and social distancing: The inefficiency of coronavirus warnings in the UK and Spain (Spanish Network of Early Career Researchers in Criminology, Blog post available: https://rejicblog.wordpress.com/2020/03/22/covid-19-protection-motivation-theory-and-social-distancing-the-inefficiency-of-corona-virus-warnings-in-the-uk-and-spain/)
Kostkova, P., Szomszor, M., & St. Louis, C., (2014). # swineflu: The use of twitter as an early warning and risk communication tool in the 2009 swine flu pandemic. ACM Transactions on Management Information Systems (TMIS), 5(2), 1–25.
Article
Google Scholar
Kouloumpis, E., Wilson, T., & Moore, J. (2011, July). Twitter sentiment analysis: The good the bad and the omg!. In Fifth International AAAI conference on weblogs and social media.
Kumar, S., Barbier, G., Abbasi, M. A., & Liu, H. (2011). Tweettracker: an analysis tool for humanitarian and disaster relief. In Fifth international AAAI conference on weblogs and social media.
Kumaran, N., & Lugani, S. (2020) Identity and security. Protecting businesses against cyber threats during COVID-19 and beyond. Retrieved 20 April, 2020 from https://cloud.google.com/blog/products/identity-security/protecting-against-cyber-threats-during-covid-19-and-beyond
Lee, M., & McGovern, A. (2013). Policing and media: Public relations, simulations and communications. Routledge.
Lieberman, J. D., Koetzle, D., & Sakiyama, M. (2013). Police departments’ use of Facebook: patterns and policy issues. Police quarterly, 16(4), 438–462.
Article
Google Scholar
Lima, A. C. E., de Castro, L. N., & Corchado, J. M. (2015). A polarity analysis framework for Twitter messages. Applied Mathematics and Computation, 270, 756–767.
Article
Google Scholar
Loeys, T., Moerkerke, B., De Smet, O., & Buysse, A. (2012). The analysis of zero-inflated count data: beyond zero-inflated Poisson regression. British Journal of Mathematical and Statistical Psychology, 65(1), 163–180.
Article
Google Scholar
Mandel, B., Culotta, A., Boulahanis, J., Stark, D., Lewis, B., & Rodrigue, J. (2012). A demographic analysis of online sentiment during hurricane irene. In Proceedings of the second workshop on language in social media (pp. 27–36).
Mazerolle, L., & Ransley, J. (2005). Third Party Policing. Cambridge: Cambridge University Press.
Google Scholar
McDowell, A. (2003). From the help desk: hurdle models. The Stata Journal, 3(2), 178–184.
Article
Google Scholar
McNeill, A., Harris, P. R., & Briggs, P. (2016). Twitter influence on UK vaccination and antiviral uptake during the 2009 H1N1 pandemic. Frontiers in Public Health, 4, 26.
Article
Google Scholar
Meijer, A., & Thaens, M. (2013). Social media strategies: Understanding the differences between North American police departments. Government Information Quarterly, 30(4), 343–350.
Article
Google Scholar
Michie, M., van Strlen, M., & West, R. (2011). The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implementation Science, 6, 42–53.
Article
Google Scholar
Miró-Llinares, F., Moneva, A., & Esteve, M. (2018). Hate is in the air! But where? Introducing an algorithm to detect hate speech in digital microenvironments. Crime Science, 7(1), 15.
Article
Google Scholar
Muncaster, P. (2020) Cyber-attacks up 37% over past month as #COVID19 bites. Infosecurity Magazine. Retrieved 25 April, 2020 from https://www.infosecurity-magazine.com/news/cyberattacks-up-37-over-past-month
Naidoo, R. (2020). A multi-level influence model of COVID-19 themed cybercrime. European Journal of Information Systems, 29, 1–16.
Article
Google Scholar
Pak, A., & Paroubek, P. (2010, May). Twitter as a corpus for sentiment analysis and opinion mining. In LREc (Vol. 10, No. 2010, pp. 1320–1326).
Pfitzner, N., Fitz-Gibbon, K., True, J. (2020). Responding to the ‘shadow pandemic’: practitioner views on the nature of and responses to violence against women in Victoria, Australia during the COVID-19 restrictions. Monash University. Report. https://doi.org/10.26180/5ed9d5198497c.
Piquero, A. R., Riddell, J. R., Bishopp, S. A., Narvey, C., Reid, J. A., & Piquero, N. L. (2020). Staying home, staying safe? a short-term analysis of COVID-19 on dallas domestic violence. American Journal of Criminal Justice, 1–35.
Ritterman, J., Osborne, M., & Klein, E. (2009, November). Using prediction markets and Twitter to predict a swine flu pandemic. In 1st international workshop on mining social media (Vol. 9, pp. 9–17).
Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change1. The Journal of Psychology, 91(1), 93–114.
Article
Google Scholar
Rogers, R. W., & Prentice-Dunn, S. (1997). Protection motivation theory. In D. S. Gochman (Ed.), Handbook of health behaviour research 1: Personal and social determinants (p. 113–132). Plenum Press.
Sampson, R., Eck, J. E., & Dunham, J. (2010). Super controllers and crime prevention: a routine activity explanation of crime prevention success and failure. Security Journal, 23(1), 37–51.
Article
Google Scholar
Signorini, A., Segre, A. M., & Polgreen, P. M. (2011). The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PLoS ONE, 6(5), e19467.
Article
Google Scholar
Smith, M., Broniatowski, D. A., Paul, M. J., & Dredze, M. (2016). Towards real-time measurement of public epidemic awareness: monitoring influenza awareness through twitter. In AAAI spring symposium on observational studies through social media and other human-generated content.
Stewart, A., & Diaz, E. (2012). Epidemic intelligence: for the crowd, by the crowd. In International Conference on Web Engineering (pp. 504–505). Springer.
Strauss, A., & Corbin, J. (1998). Basics of qualitative research techniques. Thousand Oaks, CA: Sage publications.
Google Scholar
Suh, B., Hong, L., Pirolli, P., & Chi, E. H. (2010, August). Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In 2010 IEEE Second International Conference on Social Computing (pp. 177–184). IEEE, New York.
Topalli, V., & Nikolovska, M. (2020). The future of crime: how crime exponentiation will change our field. The Criminologist, 45(3), 1–8.
Google Scholar
Usher, K., Bhullar, N., Durkin, J., Gyamfi, N., & Jackson, D. (2020). Family violence and COVID-19: Increased vulnerability and reduced options for support. International Journal of Mental Health Nursing, 29(4), 549–552. https://doi.org/https://doi.org/10.1111/inm.12735.
Article
Google Scholar
Van De Velde, B., Meijer, A., & Homburg, V. (2015). Police message diffusion on Twitter: analysing the reach of social media communications. Behaviour & Information Technology, 34(1), 4–16.
Article
Google Scholar
Vance, A., Siponen, M., & Pahnila, S. (2012). Motivating IS security compliance: insights from habit and protection motivation theory. Information & Management, 49(3–4), 190–198.
Article
Google Scholar
Walsh, J. P. (2019). Social media and border security: Twitter use by migration policing agencies. Policing and Society. https://doi.org/10.1080/10439463.2019.1666846.
Article
Google Scholar
Weisburd, D., Farrington, D. P., & Gill, C. (Eds.). (2016). What works in crime prevention and rehabilitation: Lessons from systematic reviews. Cham: Springer.
Google Scholar
Zaman, T. R., Herbrich, R., Van Gael, J., & Stern, D. (2010, December). Predicting information spreading in twitter. In Workshop on computational social science and the wisdom of crowds, nips (Vol. 104, No. 45, pp. 17599–601). Citeseer.
Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of statistical software, 27(8), 1–25.
Article
Google Scholar