Understanding Interactions Between Municipal Police Departments and the Public on Twitter

  • Yun HuangEmail author
  • Qunfang Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10766)


Law enforcement agencies have started using social media for building community policing, i.e., establishing collaborations between the people in a community and local police departments. Both researchers and practitioners need to understand how the two parties interact on social media on a daily basis, such that effective strategies or tools can be developed for the agencies to better leverage the platforms to fulfill their missions. In this paper, we collected 9,837 tweets from 16 municipal police department official Twitter accounts within 6 months in 2015 and annotated them into different strategies and topics. We further examined the association between tweet features (e.g., hashtags, mentions, content) and user interactions (favorites and retweets) by using regression models. The models reveal surprising findings, e.g., that the number of mentions has a negative correlation with favorites. Our findings provide insights into how to improve interactions between the two parties.



This material is based upon work supported by the National Science Foundation under Grant No. 1464312. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


  1. 1.
    Survey on law enforcement’s use of social media (2014). Accessed 22 Sept 2015
  2. 2.
    Abramson, J.: 10 cities making real progress since the launch of the 21st century policing task force (2015)Google Scholar
  3. 3.
    Asur, S., Huberman, B.A.: Predicting the future with social media. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 492–499. IEEE (2010)Google Scholar
  4. 4.
    Brainard, L., Edlins, M.: Top 10 US municipal police departments and their social media usage. Am. Rev. Public Adm. 45(6), 728–745 (2015)CrossRefGoogle Scholar
  5. 5.
    Community Policing Consortium, Publicity Manager, United States of America: Understanding community policing: a framework for action. BJA Monographs, 79 (1994)Google Scholar
  6. 6.
    Criado, J.I., Sandoval-Almazan, R., Gil-Garcia, J.R.: Government innovation through social media. Gov. Inf. Q. 30(4), 319–326 (2013)CrossRefGoogle Scholar
  7. 7.
    Crump, J.: What are the police doing on Twitter? Social media, the police and the public. Policy Internet 3(4), 1–27 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Denef, S., Bayerl, P.S., Kaptein, N.A.: 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. ACM (2013)Google Scholar
  9. 9.
    Fern, X.Z., Brodley, C.E., Friedl, M.A.: Correlation clustering for learning mixtures of canonical correlation models. In: SDM, pp. 439–448. SIAM (2005)Google Scholar
  10. 10.
    Fox, J.: Applied Regression Analysis, Linear Models, and Related Methods. Sage Publications, Inc., Thousand Oaks (1997)Google Scholar
  11. 11.
    Frohlich, K., Hess, E.M.: The most dangerous cities in America (2014). Accessed 19 Nov 2015
  12. 12.
    Gardner, W., Mulvey, E.P., Shaw, E.C.: Regression analyses of counts and rates: poisson, overdispersed poisson, and negative binomial models. Psychol. Bull. 118(3), 392 (1995)CrossRefGoogle Scholar
  13. 13.
    Grubbs, F.E.: Sample criteria for testing outlying observations. Ann. Math. Stat. 21, 27–58 (1950)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Hoffman, T.: NYPD turns to social media to strengthen community relations (2015). Accessed 22 June 2015
  15. 15.
    Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in Twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 57–58. ACM (2011)Google Scholar
  16. 16.
    Hu, Y., Farnham, S., Talamadupula, K.: Predicting user engagement on Twitter with real-world events. In: Ninth International AAAI Conference on Web and Social Media (2015)Google Scholar
  17. 17.
    Huang, Y.L., Starbird, K., Orand, M., Stanek, S.A., Pedersen, H.T.: Connected through crisis: emotional proximity and the spread of misinformation online. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 969–980. ACM (2015)Google Scholar
  18. 18.
    Huang, Y., Wu, Q., Hou, Y.: Examining Twitter mentions between police agencies and public users through the Lens of Stakeholder theory. In: Proceedings of the 18th Annual International Conference on Digital Government Research, dg.o 2017, pp. 30–38. ACM, New York (2017).
  19. 19.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)CrossRefGoogle Scholar
  20. 20.
    Keyling, T., Jünger, J.: Facepager (version, fe 3.3). An application for generic data retrieval through APIs (2013)Google Scholar
  21. 21.
    Leichtle, A.: The Games-Howell Test in R (2012). Assessed 1 Oct 2012
  22. 22.
    Meijer, A., Thaens, M.: Social media strategies: understanding the differences between North American police departments. Gov. Inf. Q. 30(4), 343–350 (2013)CrossRefGoogle Scholar
  23. 23.
    Mergel, I.: A framework for interpreting social media interactions in the public sector. Gov. Inf. Q. 30(4), 327–334 (2013)CrossRefGoogle Scholar
  24. 24.
    Naveed, N., Gottron, T., Kunegis, J., Alhadi, A.C.: Bad news travel fast: a content-based analysis of interestingness on Twitter. In: Proceedings of the 3rd International Web Science Conference, p. 8. ACM (2011)Google Scholar
  25. 25.
    Newcombe, T.: Social media: big lessons from the Boston Marathon bombing (2014). Accessed 24 Sept 2014
  26. 26.
    Nexis: Survey of law enforcement personnel and their use of social media (2014). Accessed 1 Nov 2014
  27. 27.
    Petrovic, S., Osborne, M., Lavrenko, V.: RT to win! predicting message propagation in Twitter. In: ICWSM (2011)Google Scholar
  28. 28.
    Pirie, W.: Spearman rank correlation coefficient. In: Encyclopedia of Statistical Sciences (1988)Google Scholar
  29. 29.
    Satapathy, S.C., Avadhani, P., Udgata, S.K., Lakshminarayana, S.: ICT and critical infrastructure. In: Proceedings of the 48th Annual Convention of Computer Society of India, vol. 1, pp. 773–780. Springer (2013)Google Scholar
  30. 30.
    Spiro, E., Irvine, C., DuBois, C., Butts, C.: Waiting for a retweet: modeling waiting times in information propagation. In: 2012 NIPS Workshop of Social Networks and Social Media Conference, vol. 12 (2012).
  31. 31.
    Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In: 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 177–184. IEEE (2010)Google Scholar
  32. 32.
    Vargo, C.J.: Brand messages on Twitter: predicting diffusion with textual characteristics. The University of North Carolina at Chapel Hill (2014)Google Scholar
  33. 33.
    Viera, A.J., Garrett, J.M.: Understanding interobserver agreement: the Kappa statistic. Fam. Med. 37(5), 360–363 (2005)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information StudiesSyracuse UniversitySyracuseUSA

Personalised recommendations