From 2,772 segments to five personas: Summarizing a diverse online audience by generating culturally adapted personas

Authors

  • Joni Salminen Qatar Computing Research Institute, Hamad Bin Khalifa University; and Turku School of Economics
  • Sercan Şengün Massachusetts Institute of Technology
  • Haewoon Kwak Qatar Computing Research Institute, Hamad Bin Khalifa University
  • Bernard J. Jansen Qatar Computing Research Institute, Hamad Bin Khalifa University
  • Jisun An Qatar Computing Research Institute, Hamad Bin Khalifa University
  • Soon-gyo Jung Qatar Computing Research Institute, Hamad Bin Khalifa University
  • Sarah Vieweg Qatar Computing Research Institute, Hamad Bin Khalifa University
  • D. Fox Harrell Massachusetts Institute of Technology

DOI:

https://doi.org/10.5210/fm.v23i6.8415

Keywords:

Personas, social media analytics, Middle East, mixed method study

Abstract

Understanding users in the era of social media is challenging, requiring organizations to adopt novel computation-aided approaches. To exemplify such an approach, we retrieved information on millions of interactions with YouTube video content from a major Middle Eastern media outlet, to automatically generate personas that capture how different audience segments interact with thousands of individual content pieces. Then, we used qualitative data to provide additional insights into the automatically generated persona profiles. Our findings provide insights into social media usage in the Middle East and demonstrate the application of a novel methodology that generates culturally adapted personas of social media audiences, summarizing complex social analytics data into human portrayals that are easy to understand by end users in real organizations.

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Published

2018-06-01

How to Cite

Salminen, J., Şengün, S., Kwak, H., Jansen, B. J., An, J., Jung, S.- gyo, … Harrell, D. F. (2018). From 2,772 segments to five personas: Summarizing a diverse online audience by generating culturally adapted personas. First Monday, 23(6). https://doi.org/10.5210/fm.v23i6.8415