IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v60y2020ics0160791x19302040.html
   My bibliography  Save this article

Mining government tweets to identify and predict citizens engagement

Author

Listed:
  • Siyam, Nur
  • Alqaryouti, Omar
  • Abdallah, Sherief

Abstract

The rise of social media offered new channels of communication between a government and its citizens. The social media channels are interactive, inclusive, low-cost, and unconstrained by time or place. This two-way communication between governments and citizens is referred to as electronic citizen participation, or e-participation. E-participation in the age of technology is considered as a mean for citizens to express their opinions and as a new input to be integrated by policy makers to take decisions. Governments and policy makers always aim to increase such participation not only to utilize public expertise and experience, but also to increase the transparency, trust, and acceptability of government decisions. In this research we investigate how governments can increase citizens e-participation on social media. We collected 55,809 tweets over a period of one year from Twitter accounts of a progressive government in the Arab world. This was followed by statistical analysis of posts characteristics (Type, Day, Time) and their impact on citizens' engagement. Then, we evaluated how well can different machine learning techniques predict user engagement. Results of the statistical analysis confirmed that post type (video, image, link, and status) impacted citizens' engagement, with videos and images having the highest positive impact on engagement. Furthermore, posting government tweets on weekdays obtained higher citizens’ engagement than weekends. Conversely, time of post had a weak effect on engagement. The results from the machine learning experiments show that two techniques (Random Forest and Adaboost) produced more accurate predictions, particularly when tweet textual contents were also used in the prediction. These results can help governments increase the engagement of their citizens.

Suggested Citation

  • Siyam, Nur & Alqaryouti, Omar & Abdallah, Sherief, 2020. "Mining government tweets to identify and predict citizens engagement," Technology in Society, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:teinso:v:60:y:2020:i:c:s0160791x19302040
    DOI: 10.1016/j.techsoc.2019.101211
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X19302040
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2019.101211?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mehmet Zahid Sobacı & İbrahim Hatipoğlu & Mehmet Fürkan Korkmaz, 2018. "The Effect of Post Type and Post Category on Citizen Interaction Level on Facebook: The Case of Metropolitan and Provincial Municipalities in the Marmara Region of Turkey," Public Administration and Information Technology, in: Mehmet Zahid Sobacı & İbrahim Hatipoğlu (ed.), Sub-National Democracy and Politics Through Social Media, chapter 0, pages 91-105, Springer.
    2. Moro, Sérgio & Rita, Paulo & Vala, Bernardo, 2016. "Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach," Journal of Business Research, Elsevier, vol. 69(9), pages 3341-3351.
    3. Georgios Lappas & Amalia Triantafillidou & Anastasia Deligiaouri & Alexandros Kleftodimos, 2018. "Facebook Content Strategies and Citizens’ Online Engagement: The Case of Greek Local Governments," The Review of Socionetwork Strategies, Springer, vol. 12(1), pages 1-20, June.
    4. Singh, Tanuja & Schoenbachler, Denise D., 2001. "Communication strategies for technology products in Singapore: a content analysis," International Business Review, Elsevier, vol. 10(5), pages 551-570, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jain, Lokesh, 2022. "An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders," Technology in Society, Elsevier, vol. 70(C).
    2. Wen Deng & Yi Yang, 2021. "Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo," IJERPH, MDPI, vol. 18(12), pages 1-20, June.
    3. Liao, Shu-Hsien & Widowati, Retno & Hsieh, Yu-Chieh, 2021. "Investigating online social media users’ behaviors for social commerce recommendations," Technology in Society, Elsevier, vol. 66(C).
    4. Lian, Ying & Dong, Xuefan, 2021. "Exploring social media usage in improving public perception on workplace violence against healthcare workers," Technology in Society, Elsevier, vol. 65(C).
    5. Tumanyan Garnik (Туманян Г.В.), 2020. "Electronic Participatory Management And Its Impact On The Culture Of Civic Participation [Электронное Партисипативное Управление И Его Влияние На Культуру Гражданского Участия]," State and Municipal Management Scholar Notes, Russian Presidential Academy of National Economy and Public Administration, vol. 2, pages 298-302.
    6. Carvajal Bermúdez, Juan Carlos & König, Reinhard, 2021. "The role of technologies and citizen organizations in decentralized forms of participation. A case study about residential streets in Vienna," Technology in Society, Elsevier, vol. 66(C).
    7. Polyzos, Efstathios & Fotiadis, Anestis & Huan, Tzung-Cheng, 2023. "From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes," Technology in Society, Elsevier, vol. 72(C).
    8. Ostheimer, Julia & Chowdhury, Soumitra & Iqbal, Sarfraz, 2021. "An alliance of humans and machines for machine learning: Hybrid intelligent systems and their design principles," Technology in Society, Elsevier, vol. 66(C).
    9. Mohammed, Abdulalem & Ferraris, Alberto, 2021. "Factors influencing user participation in social media: Evidence from twitter usage during COVID-19 pandemic in Saudi Arabia," Technology in Society, Elsevier, vol. 66(C).
    10. Anestis Kousis & Christos Tjortjis, 2023. "Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis," Future Internet, MDPI, vol. 16(1), pages 1-39, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Navdeep Bohra & Vishal Bhatnagar, 2021. "Group level social media popularity prediction by MRGB and Adam optimization," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 328-347, February.
    2. Shijie Guo & Shufeng Tang & Dongsheng Zhang, 2019. "A Recognition Methodology for the Key Geometric Errors of a Multi-Axis Machine Tool Based on Accuracy Retentivity Analysis," Complexity, Hindawi, vol. 2019, pages 1-21, November.
    3. He, Yi & You, Ya & Chen, Qimei, 2020. "Our conditional love for the underdog: The effect of brand positioning and the lay theory of achievement on WOM," Journal of Business Research, Elsevier, vol. 118(C), pages 210-222.
    4. Mustofa Rochman Hadi, 2020. "Is Big Data Security Essential for Students to Understand?," HOLISTICA – Journal of Business and Public Administration, Sciendo, vol. 11(2), pages 161-170, August.
    5. Moro, Sérgio & Pires, Guilherme & Rita, Paulo & Cortez, Paulo, 2020. "A cross-cultural case study of consumers' communications about a new technological product," Journal of Business Research, Elsevier, vol. 121(C), pages 438-447.
    6. Moro, Sérgio & Pires, Guilherme & Rita, Paulo & Cortez, Paulo, 2019. "A text mining and topic modelling perspective of ethnic marketing research," Journal of Business Research, Elsevier, vol. 103(C), pages 275-285.
    7. Shahbaznezhad, Hamidreza & Dolan, Rebecca & Rashidirad, Mona, 2021. "The Role of Social Media Content Format and Platform in Users' Engagement Behavior," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 47-65.
    8. Annika Miller & Stefan Heiland, 2021. "#ProtectNature—How Characteristics of Nature Conservation Posts Impact User Engagement on Facebook and Twitter," Sustainability, MDPI, vol. 13(22), pages 1-13, November.
    9. Othman Boujena & Isabelle Ulrich & Aikaterini Manthiou & Bruno Godey, 2021. "Customer engagement and performance in social media: a managerial perspective," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(4), pages 965-987, December.
    10. Shianghau Wu, 2020. "A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan," Mathematics, MDPI, vol. 8(11), pages 1-18, October.
    11. Kaiser, Carolin & Ahuvia, Aaron & Rauschnabel, Philipp A. & Wimble, Matt, 2020. "Social media monitoring: What can marketers learn from Facebook brand photos?," Journal of Business Research, Elsevier, vol. 117(C), pages 707-717.
    12. Sabih Ahmad Khan & Hsien-Tsung Chang, 2019. "Comparative analysis on Facebook post interaction using DNN, ELM and LSTM," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-26, November.
    13. Szu-Chuang Li & Yu-Ching Chen & Yi-Wen Chen & Yennun Huang, 2022. "Predicting Advertisement Revenue of Social-Media-Driven Content Websites: Toward More Efficient and Sustainable Social Media Posting," Sustainability, MDPI, vol. 14(7), pages 1-20, April.
    14. Lauri Valkonen & Jouni Helske & Juha Karvanen, 2023. "Estimating the causal effect of timing on the reach of social media posts," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 493-507, June.
    15. Schaefers, Tobias & Falk, Tomas & Kumar, Ashish & Schamari, Julia, 2021. "More of the same? Effects of volume and variety of social media brand engagement behavior," Journal of Business Research, Elsevier, vol. 135(C), pages 282-294.
    16. Bahaee, Mahmood & Pisani, Michael J., 2009. "Iranian consumer animosity and U.S. products: A witch's brew or elixir?," International Business Review, Elsevier, vol. 18(2), pages 199-210, April.
    17. Chawla, Yash & Chodak, Grzegorz, 2021. "Social media marketing for businesses: Organic promotions of web-links on Facebook," Journal of Business Research, Elsevier, vol. 135(C), pages 49-65.
    18. Rakshit, Sandip & Islam, Nazrul & Mondal, Sandeep & Paul, Tripti, 2022. "An integrated social network marketing metric for business-to-business SMEs," Journal of Business Research, Elsevier, vol. 150(C), pages 73-88.
    19. Priporas, Constantinos-Vasilios & Stylos, Nikolaos & Kamenidou, Irene (Eirini), 2020. "City image, city brand personality and generation Z residents' life satisfaction under economic crisis: Predictors of city-related social media engagement," Journal of Business Research, Elsevier, vol. 119(C), pages 453-463.
    20. Mitra, Satanik & Jenamani, Mamata, 2020. "OBIM: A computational model to estimate brand image from online consumer review," Journal of Business Research, Elsevier, vol. 114(C), pages 213-226.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:60:y:2020:i:c:s0160791x19302040. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.