Intelligent Monitoring and Controlling of Public Policies Using Social Media and Cloud Computing

  • Prabhsimran SinghEmail author
  • Yogesh K. Dwivedi
  • Karanjeet Singh Kahlon
  • Ravinder Singh Sawhney
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 533)


Lack of public participation in various policy making decision has always been a major cause of concern for government all around the world while formulating as well as evaluating such policies. With availability of latest IT infrastructure and the migration of government think-tank towards realizing more efficient cloud based e-government, this problem has been partially answered, but this predicament still persists. However, the exponential rise in usage of social media platforms by general public has given the government a wider insight to overcome this long pending dilemma. This paper presents a pragmatic approach that combines the capabilities of cloud computing and social media analytics towards efficient monitoring and controlling of public policies. The proposed arrangement has provided us some encouraging results, when tested for the policy of the century i.e. GST implementation by Indian government and established that proposed system can be successfully implemented for efficient policy making and implementation.


Cloud computing E-Government GST Sentiment analysis Social media analytics Twitter 


  1. 1.
    Severo, M., Feredj, A., Romele, A.: Soft data and public policy: can social media offer alternatives to official statistics in urban policymaking? Policy Internet 8(3), 354–372 (2016)CrossRefGoogle Scholar
  2. 2.
    Hibbing, J.R., Theiss-Morse, E.: Introduction: studying the American people’s attitudes toward government. In: What is it About Government that Americans Dislike, pp. 1–7 (2001)Google Scholar
  3. 3.
    Rosenstone, S.J., Hansen, J.: Mobilization, Participation, and Democracy in America. Macmillan Publishing Company, New York (1993)Google Scholar
  4. 4.
    Janssen, M., Rana, N.P., Slade, E.L., Dwivedi, Y.K.: Trustworthiness of digital government services: deriving a comprehensive theory through interpretive structural modelling. Public Manage. Rev. 20(5), 647–671 (2018)CrossRefGoogle Scholar
  5. 5.
    Shareef, M.A., Dwivedi, Y.K., Kumar, V., Kumar, U.: Reformation of public service to meet citizens’ needs as customers: evaluating SMS as an alternative service delivery channel. Comput. Hum. Behav. 61, 255–270 (2016)CrossRefGoogle Scholar
  6. 6.
    Dwivedi, Y.K., Akhter Shareef, M., Simintiras, A.C., Lal, B., Weerakkody, V.: A generalised adoption model for services: a cross-country comparison of mobile health (m-health). Gov. Inf. Q. 33(1), 174–187 (2016)CrossRefGoogle Scholar
  7. 7.
    Mohammed, F., Ibrahim, O., Ithnin, N.: Factors influencing cloud computing adoption for e-government implementation in developing countries: instrument development. J. Syst. Inf. Technol. 18(3), 297–327 (2016)CrossRefGoogle Scholar
  8. 8.
    Sharma, R., Sharma, A., Singh, R.R.: E-governance & cloud computing: technology oriented government policies. Int. J. Res. IT Manag. 2(2), 584–593 (2012)Google Scholar
  9. 9.
    Grubmüller, V., Götsch, K., Krieger, B.: Social media analytics for future oriented policy making. Eur. J. Futures Res. 1(1), 20 (2013)CrossRefGoogle Scholar
  10. 10.
    Ahmad, E., S. Poddar: GST reforms and intergovernmental considerations in India (2009)Google Scholar
  11. 11.
    Joseph, N., Grover, P., Rao, P.K., Ilavarasan, P.V.: Deep analyzing public conversations: insights from Twitter analytics for policy makers. In: Kar, A.K., et al. (eds.) I3E 2017. LNCS, vol. 10595, pp. 239–250. Springer, Cham (2017). Scholar
  12. 12.
    Jeong, K.-H.: E-Government, the Road to Innovation; Principles and Experiences in Korea. Gil-Job-E Media (2006)Google Scholar
  13. 13.
    Rana, N.P., Dwivedi, Y.K., Williams, M.D.: Analysing challenges, barriers and CSF of egov adoption. Transf. Gov. People Process Policy 7(2), 177–198 (2013)Google Scholar
  14. 14.
    Sadiku, M.N., Musa, S.M., Momoh, O.D.: Cloud computing: opportunities and challenges. IEEE Potentials 33(1), 34–36 (2014)CrossRefGoogle Scholar
  15. 15.
    Clohessy, T., Acton, T., Morgan, L.: Smart City as a Service (SCaaS): a future roadmap for e-government smart city cloud computing initiatives. In: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, pp. 836–841. IEEE Computer Society (2014)Google Scholar
  16. 16.
    Kapoor, K.K., Tamilmani, K., Rana, N.P., Patil, P., Dwivedi, Y.K., Nerur, S.: Advances in social media research: past, present and future. Inf. Syst. Front. 20(3), 531–558 (2018)CrossRefGoogle Scholar
  17. 17.
    Hossain, M.A., Dwivedi, Y.K., Chan, C., Standing, C., Olanrewaju, A.S.: Sharing political content in online social media: A planned and unplanned behaviour approach. Inf. Syst. Front. 20(3), 485–501 (2018)CrossRefGoogle Scholar
  18. 18.
    Bertot, J.C., Jaeger, P.T., Hansen, D.: The impact of polices on government social media usage: issues, challenges, and recommendations. Gov. Inf. Q. 29(1), 30–40 (2012)CrossRefGoogle Scholar
  19. 19.
    Ceron, A., Fedra, N.: The “Social Side” of public policy: monitoring online public opinion and its mobilization during the policy cycle. Policy Internet 8(2), 131–147 (2016)CrossRefGoogle Scholar
  20. 20.
    Stieglitz, S., Dang-Xuan, L.: Social media and political communication: a social media analytics framework. Soc. Netw. Anal. Min. 3(4), 1277–1291 (2013)CrossRefGoogle Scholar
  21. 21.
    Purohit, H., Hampton, A., Shalin, V.L., Sheth, A.P., Flach, J., Bhatt, S.: What kind of# conversation is Twitter? Mining# psycholinguistic cues for emergency coordination. Comput. Hum. Behav. 29(6), 2438–2447 (2013)CrossRefGoogle Scholar
  22. 22.
    Chae, B.K.: Insights from hashtag# supplychain and Twitter analytics: considering Twitter and Twitter data for supply chain practice and research. Int. J. Prod. Econ. 165, 247–259 (2015)CrossRefGoogle Scholar
  23. 23.
    Shuai, X., Pepe, A., Bollen, J.: How the scientific community reacts to newly submitted preprints: article downloads, Twitter mentions, and citations. PLoS ONE 7(11), e47523 (2012)CrossRefGoogle Scholar
  24. 24.
    McNaught, C., Lam, P.: Using Wordle as a supplementary research tool. Qual. Rep. 15(3), 630 (2010)Google Scholar
  25. 25.
    Liu, B.: Sentiment analysis and opinion mining. Synthesis lectures on human language technologies 5(1), 1–167 (2012)CrossRefGoogle Scholar
  26. 26.
    Llewellyn, C., Grover, C., Alex, B., Oberlander, J., Tobin, R.: Extracting a topic specific dataset from a Twitter archive. In: Kapidakis, S., Mazurek, C., Werla, M. (eds.) TPDL 2015. LNCS, vol. 9316, pp. 364–367. Springer, Cham (2015). Scholar
  27. 27.
    Cohen, R., Ruths, D.: Classifying political orientation on Twitter: it’s not easy! In: ICWSM (2013)Google Scholar
  28. 28.
    Hale, S.A.: Global connectivity and multilinguals in the Twitter network. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 833–842. ACM (2014)Google Scholar
  29. 29.
    HerdaĞdelen, A., Zuo, W., Gard-Murray, A., Bar-Yam, Y.: An exploration of social identity: the geography and politics of news-sharing communities in twitter. Complexity 19(2), 10–20 (2013)CrossRefGoogle Scholar
  30. 30.
    Walther, M., Kaisser, M.: Geo-spatial event detection in the Twitter stream. In: Serdyukov, P., et al. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 356–367. Springer, Heidelberg (2013). Scholar
  31. 31.
    Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 1155–1158. ACM (2010)Google Scholar
  32. 32.
    Singh, P., Sawhney, R.S., Kahlon, K.S.: Sentiment analysis of demonetization of 500 & 1000 rupee banknotes by Indian government. ICT Express (2017).
  33. 33.
    AWS - Amazon EC2.
  34. 34.
    Van den Broeck, J., Cunningham, S.A., Eeckels, R., Herbst, K.: Data cleaning: detecting, diagnosing, and editing data abnormalities. PLoS Med. 2(10), 267 (2005)Google Scholar
  35. 35.
    Haddi, E., Liu, X., Shi, Y.: The role of text pre-processing in sentiment analysis. Procedia Comput. Sci. 17, 26–32 (2013)CrossRefGoogle Scholar
  36. 36.
    Singh, P., Sawhney, R.S., Kahlon, K.S.: Forecasting the 2016 US presidential elections using sentiment analysis. In: Kar, A.K., et al. (eds.) I3E 2017. LNCS, vol. 10595, pp. 412–423. Springer, Cham (2017). Scholar
  37. 37.
    Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 26–34. Association for Computational Linguistics (2010)Google Scholar
  38. 38.
    Ou, G., et al.: Exploiting community emotion for microblog event detection. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1159–1168 (2014)Google Scholar
  39. 39.
  40. 40.
  41. 41.
  42. 42.
  43. 43.
    Amirkhanyan, A., Meinel, C.: Density and intensity-based spatiotemporal clustering with fixed distance and time radius. In: Kar, A.K., et al. (eds.) I3E 2017. LNCS, vol. 10595, pp. 313–324. Springer, Cham (2017). Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Prabhsimran Singh
    • 1
    Email author
  • Yogesh K. Dwivedi
    • 4
  • Karanjeet Singh Kahlon
    • 2
  • Ravinder Singh Sawhney
    • 3
  1. 1.Department of Computer Engineering & TechnologyGuru Nanak Dev UniversityAmritsarIndia
  2. 2.Department of Computer ScienceGuru Nanak Dev UniversityAmritsarIndia
  3. 3.Department of Electronics TechnologyGuru Nanak Dev UniversityAmritsarIndia
  4. 4.School of Management, Emerging Market Research Center (EMaRC)Swansea UniversitySwanseaUK

Personalised recommendations