Skip to main content
SpringerLink
Log in

Understanding Interactions Between Municipal Police Departments and the Public on Twitter

  • Conference paper
  • First Online:
Transforming Digital Worlds (iConference 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10766))

Included in the following conference series:

Abstract

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 is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Survey on law enforcement’s use of social media (2014). http://www.iacpsocialmedia.org/Resources/Publications/2014SurveyResults.aspx. Accessed 22 Sept 2015

  2. Abramson, J.: 10 cities making real progress since the launch of the 21st century policing task force (2015)

    Google Scholar 

  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. Brainard, L., Edlins, M.: Top 10 US municipal police departments and their social media usage. Am. Rev. Public Adm. 45(6), 728–745 (2015)

    Article  Google Scholar 

  5. Community Policing Consortium, Publicity Manager, United States of America: Understanding community policing: a framework for action. BJA Monographs, 79 (1994)

    Google Scholar 

  6. Criado, J.I., Sandoval-Almazan, R., Gil-Garcia, J.R.: Government innovation through social media. Gov. Inf. Q. 30(4), 319–326 (2013)

    Article  Google Scholar 

  7. Crump, J.: What are the police doing on Twitter? Social media, the police and the public. Policy Internet 3(4), 1–27 (2011)

    Article  MathSciNet  Google Scholar 

  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. 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. Fox, J.: Applied Regression Analysis, Linear Models, and Related Methods. Sage Publications, Inc., Thousand Oaks (1997)

    Google Scholar 

  11. Frohlich, K., Hess, E.M.: The most dangerous cities in America (2014). http://www.msn.com/en-us/health/wellness/the-10-most-dangerous-cities-in-america/ss-AAfDo9R. Accessed 19 Nov 2015

  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)

    Article  Google Scholar 

  13. Grubbs, F.E.: Sample criteria for testing outlying observations. Ann. Math. Stat. 21, 27–58 (1950)

    Article  MathSciNet  MATH  Google Scholar 

  14. Hoffman, T.: NYPD turns to social media to strengthen community relations (2015). http://www.1to1media.com/view.aspx?docid=35385. Accessed 22 June 2015

  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. 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. 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. 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). http://doi.acm.org/10.1145/3085228.3085316

  19. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)

    Article  Google Scholar 

  20. Keyling, T., Jünger, J.: Facepager (version, fe 3.3). An application for generic data retrieval through APIs (2013)

    Google Scholar 

  21. Leichtle, A.: The Games-Howell Test in R (2012). http://www.gcf.dkf.unibe.ch/BCB/files/BCB_10Jan12_Alexander.pdf. Assessed 1 Oct 2012

  22. Meijer, A., Thaens, M.: Social media strategies: understanding the differences between North American police departments. Gov. Inf. Q. 30(4), 343–350 (2013)

    Article  Google Scholar 

  23. Mergel, I.: A framework for interpreting social media interactions in the public sector. Gov. Inf. Q. 30(4), 327–334 (2013)

    Article  Google Scholar 

  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. Newcombe, T.: Social media: big lessons from the Boston Marathon bombing (2014). http://www.govtech.com/public-safety/Social-Media-Big-Lessons-from-the-Boston-Marathon-Bombing.html. Accessed 24 Sept 2014

  26. Nexis: Survey of law enforcement personnel and their use of social media (2014). http://www.lexisnexis.com/risk/downloads/whitepaper/2014-social-media-use-in-law-enforcement.pdf. Accessed 1 Nov 2014

  27. Petrovic, S., Osborne, M., Lavrenko, V.: RT to win! predicting message propagation in Twitter. In: ICWSM (2011)

    Google Scholar 

  28. Pirie, W.: Spearman rank correlation coefficient. In: Encyclopedia of Statistical Sciences (1988)

    Google Scholar 

  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. 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). http://snap.stanford.edu/social2012/papers/spiro-dubois-butts.pdf

  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. Vargo, C.J.: Brand messages on Twitter: predicting diffusion with textual characteristics. The University of North Carolina at Chapel Hill (2014)

    Google Scholar 

  33. Viera, A.J., Garrett, J.M.: Understanding interobserver agreement: the Kappa statistic. Fam. Med. 37(5), 360–363 (2005)

    Google Scholar 

Download references

Acknowledgement

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.

Author information

Authors and Affiliations

  1. School of Information Studies, Syracuse University, Syracuse, USA

    Yun Huang & Qunfang Wu

Authors
  1. Yun Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qunfang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun Huang .

Editor information

Editors and Affiliations

  1. Northumbria University, Newcastle upon Tyne, United Kingdom

    Gobinda Chowdhury

  2. Northumbria University, Newcastle upon Tyne, United Kingdom

    Julie McLeod

  3. University of Sheffield, Sheffield, United Kingdom

    Val Gillet

  4. University of Sheffield, Sheffield, United Kingdom

    Peter Willett

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huang, Y., Wu, Q. (2018). Understanding Interactions Between Municipal Police Departments and the Public on Twitter. In: Chowdhury, G., McLeod, J., Gillet, V., Willett, P. (eds) Transforming Digital Worlds. iConference 2018. Lecture Notes in Computer Science(), vol 10766. Springer, Cham. https://doi.org/10.1007/978-3-319-78105-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-78105-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78104-4

  • Online ISBN: 978-3-319-78105-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation