Security Perception and People Well-Being

Abstract

This paper aims to present a methodology for monitoring and assessing the public perception of security on social media, given the great importance of security in determining the people’s well-being. The methodology is applied to different corpora obtained by collecting Twitter messages about three topics of the agenda setting related to security. It operates in two steps, in the first one, ETM is performed in order to identify topic representation and sentiment. In the second step, this information is transformed in a stream of numerical data and a composite index aggregates the information on the sentiment related to the three topics. The composite index embeds a penalty function, which reduces the weight of the sequences, which show the greatest volatility over time. Results show that this procedure allows for real-time measurement of the perception of security, which is mostly negative. It is net of the effect of communication hype, and it enables to quantify reliably any potential change in the public perception.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. 1.

    https://dati.istat.it/.

  2. 2.

    https://wearesocial.com/it/digital-2019-italia.

  3. 3.

    https://www.consumerbarometer.com/en/.

  4. 4.

    https://www.adsnotizie.it/_dati_certificati.asp.

  5. 5.

    The first eight words of the cluster ordered by their occurrences in the cluster are: minister, children, before, student, school, clandestine, protect, education.

  6. 6.

    The first ten words of the cluster ordered by their occurrences in the cluster are: editor, arrest, need, editor’s company, truth, know, cheat, government, country, spy.

  7. 7.

    The first ten words of the cluster ordered by their occurrences in the cluster are: war, humanity, to commit, to remember, to look, to pay, to find, trafficking in human beings, world, international.

References

  1. Alaimo, L. S., & Maggino, F. (2019). Sustainable development goals indicators at territorial level: conceptual and methodological issues—The Italian perspective. Social Indicators Research. https://doi.org/10.1007/s11205-019-02162-4.

    Article  Google Scholar 

  2. Alkire, S. (2003). A conceptual framework for human security. CRISE-Centre for Research on Inequality, Human Security and Ethnicity. Working paper. Oxford: Queen Elizabeth House, University of Oxford.

    Google Scholar 

  3. Balbi, S., Misuraca, M., & Scepi, G. (2018). Combining different evaluation systems on social media for measuring user satisfaction. Information Processing and Management, 54(4), 674–685.

    Google Scholar 

  4. Baumgartner, F., & Bryan, J. (1995). Agendas and instability in American politics. Chicago, IL: University of Chicago Press.

    Google Scholar 

  5. Blinder, S. (2015). Imagined immigration: The impact of different meanings of 'immigrants' in public opinion and policy debates in Britain. Political Studies, 63(1), 80–100.

    Google Scholar 

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

    Google Scholar 

  7. Brand, S., & Price, R. (2000). The economic and social costs of crime. Home Office Research Study, Nr. 217. London: Home Office.

    Google Scholar 

  8. Ceron, A., Curini, L., & Iacus, S. (2013). Social Media e Sentiment Analysis. L’evoluzione dei fenomeni sociali attraverso la Rete. Milano: Springer.

    Google Scholar 

  9. Ceron, A., Curini, L., & Iacus, S. M. (2016). ISA: A fast, scalable and accurate algorithm for sentiment analysis of social media content. Information Sciences, 367–368, 105–124.

    Google Scholar 

  10. Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340–358.

    Google Scholar 

  11. CNEL, & Istat. (2013). BES 2013. Il benessere equo e sostenibile n Italia. Roma: Istat, https://www.istat.it/it/archivio/84348.

  12. Cody, E. M., Reagan, A. J., Sheridan Dodds, P. & Danforth, C. M. (2016). Public opinion polling with twitter. Resource document. arXiv.org. https://arxiv.org/abs/1608.02024.

  13. Coluccia, A., Ferretti, F., Lorenzi, L., & Buracchi, T. (2008). Media e percezione della sicurezza. Analisi e riflessioni. Rassegna italiana di criminologia, 22, 326–336.

    Google Scholar 

  14. Cordella, B., Greco, F., Carlini, K., Greco, A., & Tambelli, R. (2018a). Infertilita e procreazione assistita: Evoluzione legislativa e culturale in Italia. Rassegna di Psicologia, 35(3), 45–56. https://doi.org/10.4458/1415-04.

    Article  Google Scholar 

  15. Cordella, B., Greco, F., Meoli, P., Palermo, V., & Grasso, M. (2018b). Is the educational culture in Italian Universities effective? A case study. In D. F. Iezzi, L. Celardo, & M. Misuraca (Eds.), JADT’ 18: Proceedings of the 14th International Conference on Statistical Analysis of Textual Data (pp. 157–164). Rome, IT: Universitalia.

  16. Cummins, R. A., Eckersley, R., Pallant, J., Van Vugt, J., & Misajon, R. (2003). Developing a national index of subjective well-being: The australian unity well-being index. Social Indicators Research, 64(2), 159–190.

    Google Scholar 

  17. Delbosc, A., & Currie, G. (2012). Modelling the causes and impacts of personal safety perceptions on public transport ridership. Transport Policy, 24, 302–309.

    Google Scholar 

  18. Detotto, C., & Otranto, E. (2010). Does crime affect economic growth? KYKLOS, 63(3), 330–345.

    Google Scholar 

  19. Diaz, F., Gamon, M., Hofman, J. M., Kıcıman, E., & Rothschild, D. (2016). Online and social media data as an imperfect continuous panel survey. PLoS ONE. https://doi.org/10.1371/journal.pone.0145406.

    Article  Google Scholar 

  20. Dichter, M., & Gelles, R. (2012). Women’s perceptions of safety and risk following police intervention for intimate partner violence. Violence Against Women, 18(1), 44–63.

    Google Scholar 

  21. Dodge, R., Daly, A., Huyton, J., & Sanders, L. (2012). The challenge of defining well-being. International Journal of Well-being, 2(3), 222–235. https://doi.org/10.5502/ijw.v2i3.4.

    Article  Google Scholar 

  22. Dunaway, J., Branton, R. P., & Abrajano, M. A. (2010). Agenda setting, public opinion, and the issue of immigration reform. Social Science Quarterly, 91(2), 359–378.

    Google Scholar 

  23. Florini, A., & Simmons, P. J. (1998). the new security thinking: A review of the North American literature. Project on world SECURITY. New York: Rockefeller Brothers Fund.

    Google Scholar 

  24. Fronzetti Colladon, A. (2018). The semantic brand score. Journal of Business Research, 88, 150–160.

    Google Scholar 

  25. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.

    Google Scholar 

  26. Garland, D. (2001). The culture of control. Chicago, IL: The University of Chicago Press.

    Google Scholar 

  27. Gentry, J. (2016). R Based Twitter Client. R package version 1.1.9. https://CRAN.R-project.org/package=twitteR.

  28. Giuliano, L., & La Rocca, G. (2010). Analisi automatica e semi-automatica dei dati testuali (Vol. II). Milano: Led.

    Google Scholar 

  29. Gloor, P. A. (2017). Sociometrics and human relationships: Analyzing social networks to manage brands, predict trends, and improve organizational performance. London, UK: Emerald Publishing Limited.

    Google Scholar 

  30. Grabisch, M. (1994). Characterization of fuzzy integrals viewed as aggregation operators. In F. L. Orlando (Ed.), Proceedings of 3rd IEEE Conference on Fuzzy Systems (pp. 1927–1932). IEEE.

  31. Grabisch, M., Marichal, J.-L., Mesiar, R., & Pap, E. (2009). Aggregation functions. Cambridge: Cambridge University Press.

    Google Scholar 

  32. Gray, E., Jackson, J., & Farrall, S. (2008). Reassessing the fear of crime. European Journal of Criminology, 5(3), 363–380.

    Google Scholar 

  33. Greco, F. (2016). Integrare la disabilità. Una metodologia interdisciplinare per leggere il cambiamento culturale. Milano: Franco Angeli.

    Google Scholar 

  34. Greco, F. (2019). Il dibattito sulla migrazione in campagna elettorale: Confronto tra il caso francese e italiano. Culture e Studi nel Sociale, 4(2) (in press).

  35. Greco, F., Alaimo, L. S., & Celardo, L. (2018a). Brexit and Twitter: The voice of people. In D. F. Iezzi, L. Celardo, & M. Misuraca (Eds.). JADT’ 18: Proceedings of the 14th International Conference on Statistical Analysis of Textual Data (pp. 327–334). Rome, IT: Universitalia.

  36. Greco, F., Celardo, L., & Alaimo, L. S. (2018b). Brexit in Italy: Text mining of social media. In A. Abbruzzo, D. Piacentino, M. Chiodi, & E. Brentari (Eds.), Book of short papers SIS 2018 (pp. 767–772). Milano: Pearson.

    Google Scholar 

  37. Greco, F., & Polli, A. (2019a). Anatomy of a government crisis. Political institutions, security, and consensus. In L. S. Alaimo, A. Arcagni, E. di Bella, F. Maggino & M. Trapani (Eds.), Libro dei Contributi Brevi: AIQUAV 2019, VI Convegno Nazionale dell’Associazione Italiana, per gli Studi sulla Qualità della Vita, Benessere Collettivo e Scelte Individuali, Fiesole (FI), 12–14 Dicembre 2019 (pp. 177–183). Genova: Genova University Press.

  38. Greco, F., & Polli, A. (2019b). Vaccines in Italy: The emotional text mining of social media. Rivista Italiana di Economia Demografia e Statistica, 73(1), 89–98.

    Google Scholar 

  39. Greco, F., & Polli, A. (2020a). Emotional text mining: Customer profiling in brand management. International Journal of Information Management., 51, 101934. https://doi.org/10.1016/j.ijinfomgt.2019.04.007.

    Article  Google Scholar 

  40. Greco, F., & Polli, A. (2020b). La sicurezza tra percezione pubblica e statistiche ufficiali. In U. Conti and C. Federici (Eds.), Vivere i territori mediani: identità territoriali, emergenze sociali e rigenerazione dei tessuti urbani. Roma: Meltemi (in press).

  41. Greco, F., Maschietti, D., & Polli, A. (2017). Emotional text mining of social networks: The French pre-electoral sentiment on migration. RIEDS, 71(2), 125–136.

    Google Scholar 

  42. Greco, F., Monaco, S., Di Tran, M., & Cordella, B. (2019). Emotional text mining and health psychology: the culture of organ donation in Spain. In M. Carpita & L. Fabbris (Eds.), ASA conference 2019—Book od short papers statistics for health and well-being, University of Brescia, September 25–27, 2019 (pp. 125–129). Padova: CLEUP.

    Google Scholar 

  43. Hopkins, D., & King, G. (2010). A method of automated nonparametric content analysis for social science. American Journal of Political Science, 54(1), 229–247.

    Google Scholar 

  44. Istat. (2018). La percezione della sicurezza. Statistiche report del 22 giugno 2018. Roma: Istat.

    Google Scholar 

  45. Iyengar, S., & Kinder, D. R. (1987). News that matters: Television and American opinion. Chicago, IL: University of Chicago Press.

    Google Scholar 

  46. Lancia, F. (2018). User’s Manual: Tools for text analysis. T-Lab version Plus 2018.

  47. Laricchiuta, D., Greco, F., Piras, F., Cordella, B., Cutuli, D., Picerni, E., et al. (2018). “The grief that doesn’t speak”: Text mining and brain structure. In D.F. Iezzi, L. Celardo, & M. Misuraca (Eds.). JADT’ 18: Proceedings of the 14th international conference on statistical analysis of textual data (pp. 419–427). Rome, IT: Universitalia.

  48. Lau, R. R., & Redlawsk, D. P. (2001). Advantages and disadvantages of cognitive heuristics in political decision making. American Journal of Political Science, 45(1), 951–971.

    Google Scholar 

  49. Lebart, L., & Salem, A. (1994). Statistique textuelle. Paris, FR: Dunod.

    Google Scholar 

  50. Lee, G. H. (2004). Reconciling cognitive priming versus obtrusive contingency hypothesis. Gazette: International Journal for Communication Studies, 66, 151–67.

    Google Scholar 

  51. Lee, N. J., McLeod, D. M., & Shah, D. V. (2008). Framing policy conflict: Issue dualism, journalistic frames, and opinions on controversial policy issues. Communication Research, 35, 695–718.

    Google Scholar 

  52. Levy, S., & Sabbagh, C. (2008). The well-being of the self's personality: A structural analysis. Social Indicators Research, 89(3), 473–485.

    Google Scholar 

  53. Liska, A. E., Lawrence, J. J., & Sanchirico, A. (1982). Fear of crime as a social fact. Social Forces, 60(3), 760–770.

    Google Scholar 

  54. Liu, B. (2012). Sentiment analysis: Mining opinions, sentiments, and emotions (pp. 1–367). San Rafael: Morgan & Claypool.

    Google Scholar 

  55. McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. The Public Opinion Quarterly, 36(2), 176–187.

    Google Scholar 

  56. Maggino, F. (2015). Subjective well-being and subjective aspects of well-being: Methodology and theory. Rivista Internazionale di Scienze Sociali, 1, 89–121.

    Google Scholar 

  57. McLuhan, M. (1964). Understanding media. New York: McGraw-Hill.

    Google Scholar 

  58. OECD. (2011). How's life? Measuring well-being. Paris: OECD Publishing. https://doi.org/10.1787/9789264121164-en.

    Google Scholar 

  59. Pearson, A. L., & Breetzke, G. D. (2014). The association between the fear of crime, and mental and physical well-being in New Zealand. Social Indicators Research, 119(1), 281–294.

    Google Scholar 

  60. Polli, A. (2005). Una tecnica di disaggregazione fuzzy. Statistica, LXV(4), 387–394.

    Google Scholar 

  61. Rotarou, E. S. (2018). Does municipal socioeconomic development affect public perceptions of crime? A multilevel logistic regression analysis. Social Indicators Research, 138(2), 705–724.

    Google Scholar 

  62. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., et al. (2008). Global sensitivity analysis: The primer. Chichester, UK: Wiley.

    Google Scholar 

  63. Savaresi, S. M., & Boley, D. L. (2004). A comparative analysis on the bisecting K-means and the PDDP clustering algorithms. Intelligent Data Analysis, 8(4), 345–362.

    Google Scholar 

  64. Schoen, H., Gayo-Avello, D., Metaxas, P., Mustafaraj, E., Strohmaier, M., & Gloor, P. (2013). The power of prediction with social media. Internet Research, 23(5), 528–543.

    Google Scholar 

  65. Shakhnarovish, G., Darrell, T., & Indyk, P. (2005). Nearest-neighbour methods in learning and vision. Cambridge, MA: The MIT Press.

    Google Scholar 

  66. Skogan, W. (1986). Fear of crime and neighbourhood change. In A. J. Reiss & M. Tonry (Eds.), Communities and crime. Chicago: University of Chicago Press.

    Google Scholar 

  67. Sorrentino, V. (2008). Governare la paura. Cosmopolis. Rivista di filosofia e teoria politica, 3(2). https://www.cosmopolisonline.it/articolo.php?numero=III22008&id=25.

  68. Steinbach, M., Karypis, G., & Kumar., V. (2000). A comparison of document clustering techniques. In KDD workshop on text mining, vol. 400 (pp. 525–526). Boston.

  69. UNDP. (1994). Human development report 1994. New York: Oxford University Press.

    Google Scholar 

  70. Zhao, Y., Yu, F., Jing, B., Hu, X., Luo, A., & Peng, K. (2019). An analysis of well-being determinants at the city level in china using big data. Social Indicators Research, 143(3), 973–994.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding authors

Correspondence to Francesca Greco or Alessandro Polli.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Greco, F., Polli, A. Security Perception and People Well-Being. Soc Indic Res 153, 741–758 (2021). https://doi.org/10.1007/s11205-020-02341-8

Download citation

Keywords

  • Public perception
  • Security
  • Wellbeing
  • Emotional text mining
  • Composite index