Measuring Geographic Sentiment toward Police Using Social Media Data

Abstract

Using Twitter messages published online from October 2018 to June 2019, and opinion mining (OM) technology, the current study analyzes the geographic sentiments toward police in 82 metropolitan areas within the United States. Building on the frameworks of the neighborhood social contextual models, the construct validity of “sentiment toward the police” is assessed via its relationship with the features of various metropolitan areas. Results of the regression analysis indicate that the violent crime rate, racial heterogeneity, and economic disadvantage significantly affect sentiment toward the police. Our results suggest that opinion mining of social media can be an important instrument to understand public sentiment toward the police.

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Notes

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    For more information, refer the website https://developer.twitter.com/en/docs/tutorials/filtering-tweets-by-location.html.

References

  1. Apple, N., & O’Brien, D. (1983). Neighborhood racial composition and residents’ evaluation of police performance. Journal of Police Science and Administration, 11(1), 76–84.

    Google Scholar 

  2. Berthelot, E. R., McNeal, B. A., & Baldwin, J. M. (2018). Relationships between agency-specific contact, victimization type, and trust and confidence in the police and courts. American Journal of Criminal Justice, 43(4), 768–791.

    Article  Google Scholar 

  3. Blau, P. M. (1977). Inequality and heterogeneity: A primitive theory of social structure (Vol. 7). New York: Free Press.

    Google Scholar 

  4. Bolger, M. A., Lytle, D. J., & Bolger, P. C. (2021). What matters in citizen satisfaction with police: A meta-analysis. Journal of Criminal Justice, 72, 101760.

    Article  Google Scholar 

  5. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.

    Article  Google Scholar 

  6. Brandl, S. G., Frank, J., Worden, R. E., & Bynum, T. S. (1994). Global and specific attitudes toward the police: Disentangling the relationship. Justice Quarterly, 11(1), 119–134.

    Article  Google Scholar 

  7. Brown, B., & Benedict, W. R. (2002). Perceptions of the police. Policing: An International Journal of Police Strategies & Management., 25(3), 543–580.

    Article  Google Scholar 

  8. Browning, S. L., & Cao, L. (1992). The impact of race on criminal justice ideology. Justice Quarterly, 9(4), 685–701.

    Article  Google Scholar 

  9. Callegaro, M., & Yang, Y. (2018). The role of surveys in the era of “big data”. In D. Vannette & J. Krosnick (Eds.), The Palgrave handbook of survey research (pp. 175–192). Cham: Palgrave Macmillan.

    Google Scholar 

  10. Cao, L. (2011). Visible minorities and confidence in the police. Canadian Journal of Criminology and Criminal Justice, 53(1), 1–26.

    Article  Google Scholar 

  11. Cao, L., Frank, J., & Cullen, F. T. (1996). Race, community context and confidence in the police. American Journal of Police, 15(3), 3–22.

    Article  Google Scholar 

  12. Cao, L., & Stack, S. (2005). Confidence in the police between America and Japan. Policing: An International Journal of Police Strategies & Management, 28(1), 139–151.

    Article  Google Scholar 

  13. Dai, M., & Jiang, X. (2016). A comparative study of satisfaction with the police in the United States and Australia. Australian & New Zealand Journal of Criminology, 49(1), 30–52.

    Article  Google Scholar 

  14. Dai, M., & Johnson, R. R. (2009). Is neighborhood context a confounder? Policing: An International Journal of Police Strategies & Management, 32(4), 595–612.

    Article  Google Scholar 

  15. Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web (pp. 519–528).

  16. Davies, P., & Francis, P. (2018). Doing criminological research. Thousand Oaks, CA: SAGE.

    Google Scholar 

  17. Ferguson, A. G. (2019). The rise of big data policing: Surveillance, race, and the future of law enforcement. New York, NY: NYU Press.

    Google Scholar 

  18. Flexon, J. L., Lurigio, A. J., & Greenleaf, R. G. (2009). Exploring the dimensions of trust in the police among Chicago juveniles. Journal of Criminal Justice, 37(2), 180–189.

    Article  Google Scholar 

  19. Gau, J. M., & Brunson, R. K. (2010). Procedural justice and order maintenance policing: A study of inner-city young men’s perceptions of police legitimacy. Justice Quarterly, 27(2), 255–279.

    Article  Google Scholar 

  20. Hecht, B., Hong, L., Suh, B., & Chi, E. H. (2011, May). Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles. In: Proceedings of the SIGCHI conference on human factors in computing systems (pp. 237–246). https://doi.org/10.1145/1978942.1978976.

  21. Hridoy, S. A. A., Ekram, M. T., Islam, M. S., Ahmed, F., & Rahman, R. M. (2015). Localized twitter opinion mining using sentiment analysis. Decision Analytics, 2(1), 1–19.

    Article  Google Scholar 

  22. Huang, W. W., & Vaughn, M. S. (1996). Support and confidence: Public attitudes toward the police. In T. J. Flanagan & D. R. Longmire (Eds.), Americans View Crime and Justice: A National Public Opinion Survey. Thousand oaks, CA: Sage.

    Google Scholar 

  23. Jang, H., Joo, H.-J., & Zhao, J. S. (2010). Determinants of public confidence in police: An international perspective. Journal of Criminal Justice, 38(1), 57–68.

    Article  Google Scholar 

  24. Jockers, M. L. (2017). Syuzhet: An R package for the extraction of sentiment and sentiment-based plot arcs from text. Retrieved from https://github.com/mjockers/syuzhet. Accessed 1 Mar 2019.

  25. Lee, H. D., Boateng, F. D., Kim, D., & Binning, C. (2020). Residential stability and Trust in the Police: An understudied area of police attitudinal research. American Journal of Criminal Justice, 45(1), 88–101.

    Article  Google Scholar 

  26. Lee, J., Zhang, Y., & Hoover, L. T. (2013). Police response to domestic violence: Multilevel factors of arrest decision. Policing: An International Journal of Police Strategies & Management, 36(1), 157–114.

    Article  Google Scholar 

  27. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.

    Article  Google Scholar 

  28. Liu, Y., Huang, X., An, A., & Yu, X. (2007). ARSA: A sentiment-aware model for predicting sales performance using blogs. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 607–614).

  29. Lund, S. (2018). Good cop, bad cop?: A corpus analysis on the semantic prosody of the noun cop. Karlstads: Karlstads University.

    Google Scholar 

  30. Luo, F., Ren, L., & Zhao, J. S. (2017). The effect of micro-level disorder incidents on public attitudes toward the police. Policing: An International Journal of Police Strategies & Management, 40(2), 395–409.

    Article  Google Scholar 

  31. Mahmud, J., Nichols, J., & Drews, C. (2014). Home location identification of twitter users. ACM Transactions on Intelligent Systems and Technology (TIST), 5(3), 1–21.

    Article  Google Scholar 

  32. Markowitz, F. E., Bellair, P. E., Liska, A. E., & Liu, J. (2001). Extending social disorganization theory: Modeling the relationships between cohesion, disorder, and fear. Criminology, 39(2), 293–319.

    Article  Google Scholar 

  33. Maxson, C. L., Hennigan, K., & Sloane, D. C. (2003). Factors that influence public opinion of the police. Washington, DC: US Department of Justice, Office of Justice Programs, National Institute of Justice.

    Google Scholar 

  34. Morenoff, J. D., Sampson, R. J., & Raudenbush, S. W. (2001). Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology, 39(3), 517–558.

    Article  Google Scholar 

  35. Nasukawa, T., & Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing. Proceedings of the 2nd International Conference on Knowledge Capture (pp. 70–77).

  36. O’Brien, D. T., Sampson, R. J., & Winship, C. (2015). Ecometrics in the age of big data: Measuring and assessing “broken windows” using large-scale administrative records. Sociological Methodology, 45(1), 101–147.

    Article  Google Scholar 

  37. O’Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. (2010, May). From tweets to polls: Linking text sentiment to public opinion time series. In: Proceedings of the International AAAI Conference on Web and Social Media.

  38. Oglesby-Neal, A., Tiry, E., & Kim, K. (2019). Public perceptions of police on social media. Washington, DC: Urban Institute.

    Google Scholar 

  39. Oh, G., Ren, L., & He, P. (2019). Social disorder and residence-based fear of crime: The differential mediating effects of police effectiveness. Journal of Criminal Justice, 63, 1–11.

    Article  Google Scholar 

  40. Othman, R., Belkaroui, R., & Faiz, R. (2017). Extracting product features for opinion mining using public conversations in twitter. Procedia Computer Science, 112, 927–935.

    Article  Google Scholar 

  41. Parker, K. D., Onyekwuluje, A. B., & Murty, K. S. (1995). African Americans’ attitudes toward the local police: A multivariate analysis. Journal of Black Studies, 25(3), 396–409.

    Article  Google Scholar 

  42. Raudenbush, S. W., & Sampson, R. J. (1999). Ecometrics: toward a science of assessing ecological settings, with application to the systematic social observation of neighborhoods. Sociological methodology, 29(1), 1–41.

    Article  Google Scholar 

  43. Reisig, M. D., & Parks, R. B. (2000). Experience, quality of life, and neighborhood context: A hierarchical analysis of satisfaction with police. Justice Quarterly, 17(3), 607–630.

    Article  Google Scholar 

  44. Ren, L., Cao, L., Lovrich, N., & Gaffney, M. (2005). Linking confidence in the police with the performance of the police: Community policing can make a difference. Journal of Criminal Justice, 33(1), 55–66.

    Article  Google Scholar 

  45. Sampson, R. J., & Bartusch, D. J. (1998). Legal cynicism and (subcultural?) tolerance of deviance: The neighborhood context of racial differences. Law and Society Review, 32, 777–804.

    Article  Google Scholar 

  46. Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277(5328), 918–924.

    Article  Google Scholar 

  47. Schafer, J. A., Huebner, B. M., & Bynum, T. S. (2003). Citizen perceptions of police services: Race, neighborhood context, and community policing. Police Quarterly, 6(4), 440–468.

    Article  Google Scholar 

  48. Schuman, H., & Gruenberg, B. (1972). Dissatisfaction with city services: Is race an important factor? People and Politics in Urban Society, 6, 369–392.

    Google Scholar 

  49. Sharma, P., & Moh, T.-S. (2016). Prediction of Indian election using sentiment analysis on Hindi twitter. 2016 IEEE international conference on big data (big data), 1966–1971.

  50. Skogan, W. G. (2009). Concern about crime and confidence in the police: Reassurance or accountability? Police Quarterly, 12(3), 301–318.

    Article  Google Scholar 

  51. Smith, S. K., Steadman, G. W., Minton, T. D., & Townsend, M. (1999). Criminal victimization and perceptions of community safety in 12 cities, 1998. Washington, DC: Bureau of Justice Statistics and Office of Community Oriented Policing Services, US Department of Justice.

    Google Scholar 

  52. Sprott, J. B., & Doob, A. N. (2014). Confidence in the police: Variation across groups classified as visible minorities. Canadian Journal of Criminology and Criminal Justice, 56(3), 367–379.

    Article  Google Scholar 

  53. Stack, S. J., & Cao, L. (1998). Political conservatism and confidence in the police: A comparative analysis. Journal of Crime and Justice, 21(1), 71–76.

    Article  Google Scholar 

  54. Taylor, T. J., Turner, K. B., Esbensen, F.-A., & Winfree Jr., L. T. (2001). Coppin’an attitude: Attitudinal differences among juveniles toward police. Journal of Criminal Justice, 29(4), 295–305.

    Article  Google Scholar 

  55. Tims, A. R., Fan, D. P., & Freeman, J. R. (1989). The cultivation of consumer confidence: A longitudinal analysis of news media influence on consumer sentiment. Advances in Consumer Research, 16, 758–770.

    Google Scholar 

  56. Van Craen, M. (2013). Explaining majority and minority trust in the police. Justice Quarterly, 30(6), 1042–1067.

    Article  Google Scholar 

  57. Weitzer, R., & Tuch, S. A. (2002). Perceptions of racial profiling: Race, class, and personal experience. Criminology, 40(2), 435–456.

    Article  Google Scholar 

  58. Weitzer, R., & Tuch, S. A. (2005). Racially biased policing: Determinants of citizen perceptions. Social Forces, 83(3), 1009–1030.

    Article  Google Scholar 

  59. Williams, M. L., Burnap, P., & Sloan, L. (2017). Crime sensing with big data: The affordances and limitations of using open-source communications to estimate crime patterns. The British Journal of Criminology, 57(2), 320–340.

    Google Scholar 

  60. Worrall, J. L. (1999). Public perceptions of police efficacy and image: The “fuzziness” of support for the police. American Journal of Criminal Justice, 24(1), 47–66.

    Article  Google Scholar 

  61. Wu, Y., & Sun, I. Y. (2009). Citizen trust in police: The case of China. Police Quarterly, 12(2), 170–191.

    Article  Google Scholar 

  62. Wu, Y., Sun, I. Y., & Triplett, R. A. (2009). Race, class or neighborhood context: Which matters more in measuring satisfaction with police? Justice Quarterly, 26(1), 125–156.

    Article  Google Scholar 

  63. Zhao, J. S., Tsai, C. F., Ren, L., & Lai, Y. L. (2014). Public satisfaction with police control of disorder crime: does the public hold police accountable? Justice Quarterly, 31(2), 394–420.

    Article  Google Scholar 

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Correspondence to Gyeongseok Oh.

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Oh, G., Zhang, Y. & Greenleaf, R.G. Measuring Geographic Sentiment toward Police Using Social Media Data. Am J Crim Just (2021). https://doi.org/10.1007/s12103-021-09614-z

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Keywords

  • Opinion mining
  • Sentiment analysis
  • Twitter messages
  • Attitude towards police