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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

In: Sub-National Democracy and Politics Through Social Media

Author

Listed:
  • Mehmet Zahid Sobacı

    (Uludağ University)

  • İbrahim Hatipoğlu

    (Uludağ University)

  • Mehmet Fürkan Korkmaz

    (Uludağ University)

Abstract

Local governments use social media tools to inform citizens, promote themselves to their audiences, collect feedback from citizens and use this feedback as input in the process of improving public services and policies. To achieve these goals, local governments need to reach out to the greatest number of people using a successful communication strategy. Post characteristics such as post type and category have an effect on the number of citizens reached by local governments and the amount of feedback received from them (such as the number of reactions, comments and shares). Therefore, the basic research question of the present study is as follows: What type and content of posts would help local governments increase the level of interaction with their citizens? In this context, this chapter aims to analyze the relationship between the types and categories of Facebook posts sent by the metropolitan and provincial municipalities in the Marmara Region of Turkey and the level of citizen interaction. In this study, the methods of content analysis and the Kruskal-Wallis test are used. The results of our analysis reveal that there is a significant relationship between post type and post category and citizen interaction level.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:paitcp:978-3-319-73386-9_5
    DOI: 10.1007/978-3-319-73386-9_5
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    Cited by:

    1. Siyam, Nur & Alqaryouti, Omar & Abdallah, Sherief, 2020. "Mining government tweets to identify and predict citizens engagement," Technology in Society, Elsevier, vol. 60(C).

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