Mining Dubai Government Tweets to Analyze Citizens’ Engagement

  • Zainab AlkashriEmail author
  • Omar Alqaryouti
  • Nur Siyam
  • Khaled Shaalan
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 295)


Social media plays a critical role in the public sector as it allows the government to interact with the citizens. With the United Arab Emirates being active on social media platforms, this study aims to identify the level of citizen engagement in Dubai government’s Twitter through the use of data mining techniques. Post engagement is the total number of citizens’ interactions with a tweet and can be measured using different tweet attributes including retweets, mentions, and likes. Moreover, this study investigates the impact of the twitter post characteristics on the citizens’ engagements level. Thus, we collected, prepared and processed 74,037 tweets that represents all tweets for Dubai government twitter accounts during 2018. These tasks were followed by statistical analyses of the impact of post characteristics on the citizens’ engagement level. Next, we implemented various machine learning models to evaluate the performance of using the post characteristics and post content to predict the engagement level of citizens. Results indicate that citizen engagement level in Dubai government’s Twitter is significantly impacted by all post characteristics. It is also revealed in the study that citizen engagement is higher during weekdays compared to weekends. Furthermore, the machine learning models achieved promising results to predict the citizens’ engagement with highest accuracy for Random Forest and Linear Support Vector Machine of 78.3% and 78.2% respectively.


Data mining Government twitter Dubai government Machine learning Twitter data analysis Citizens’ engagement 


  1. 1.
    Siddiqui, S., Singh, T.: Social media its impact with positive and negative aspects. Int. J. Comput. Appl. Technol. Res. 5(2), 71–75 (2016)Google Scholar
  2. 2.
    Mishaal, D., Abu-Shanab, E.: The effect of using social media in governments: framework of communication success. In: ICIT 2015 The 7th International Conference on Information Technology. Amman, Jordan, 12–15 May (2015)Google Scholar
  3. 3.
    Chukwuere, J.E. Chukwuere, P.C.: The impact of social media on social lifestyle: a case study of university female students. Gend. Behav. 9928–9940 (2017)Google Scholar
  4. 4.
    Akram, W., Kumar, R.: A Study on Positive and Negative Effects of Social Media on Society. Int. J. Comput. Sci. Eng. 5(10), 347–354 (2017)Google Scholar
  5. 5.
    Karakiza, M.: The impact of social media in the public sector. Procedia—Soc. Behav. Sci. 175, 384–392 (2015)CrossRefGoogle Scholar
  6. 6.
    Sanford, C., Rose, J.: Characterizing eParticipation. Int. J. Inf. Manage. 27, 406–421 (2007)CrossRefGoogle Scholar
  7. 7.
    Kassen, M.: E-participation actors: understanding roles, connections, partnerships. Knowl. Manag. Res. Pract. 1–22 (2018)Google Scholar
  8. 8.
    Pushpam, C.A., Jayanthi, J.G.: Overview on data mining in social media. Int. J. Comput. Sci. Eng. 5(11), 147–157 (2017)Google Scholar
  9. 9.
    Injadat, M.N., Salo, F., Nassif, A.B.: Data mining techniques in social media: a survey. Neurocomputing 214, n.p (2016)Google Scholar
  10. 10.
    Zatari, T.: Data mining in social media. Int. J. Sci. Eng. Res. 6(7), 152–154 (2015)Google Scholar
  11. 11.
    Siyam, N., Alqaryouti, O., Abdalla, S.: Mining government tweets to identify and predict citizens engagement. Technol. Soc. (2019). Scholar
  12. 12.
    Kavitha, D.: Survey of data mining techniques for social networking websites. Int. J. Comput. Sci. Mob. Comput. 6(4), 418–426 (2017)MathSciNetGoogle Scholar
  13. 13.
    Azevedo, A.I.R.L., Santos, M.F.: KDD, SEMMA and CRISP-DM: a parallel overview. IADS—DM (2008). Accessed 12 Oct 2019
  14. 14.
    Government of Dubai, Government Entities: (2019). Accessed 26 April 2019
  15. 15.
    Urdan, T.: Statistics in Plain English, 2nd edn. Psychology Press, New Jersey (2005)CrossRefGoogle Scholar
  16. 16.
    Alqaryouti, O., Siyam, N., Abdel Monemb, A., Shaalan, K.: Aspect-based sentiment analysis using smart government review data. Appl. Comput. Inf. (2019). Scholar
  17. 17.
    Alqaryouti, O., Khwileh, H., Farouk, T., Nabhan, A., Shaalan, K.: Graph-based keyword extraction. In: Intelligent Natural Language Processing: Trends and Applications, pp. 159–172. Springer, Cham (2018)Google Scholar
  18. 18.
    Al-Badi, A.H.: The adoption of social media in government agencies: Gulf Cooperation Council case study. J. Technol. Res. 5, 1–26 (2013)Google Scholar
  19. 19.
    Mhamdi, C., Al-Emran, M., Salloum, S.A.: Text mining and analytics: a case study from news channels posts on Facebook. In: Intelligent Natural Language Processing: Trends and Applications, pp. 399–415. Springer, Cham (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.The British University in DubaiDubaiUnited Arab Emirates
  2. 2.School of InformaticsUniversity of EdinburghEdinburghUK

Personalised recommendations