Mining government tweets to identify and predict citizens engagement
Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
ScienceDirect
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.
Description
Keywords
Dubai governmentTwitter dataPost engagementMining government tweetsMachine learningEnsemble learning models
Citation
Siyam, N., Alqaryouti, O. and Abdallah, S. (2020) “Mining government tweets to identify and predict citizens engagement,” Technology in Society, 60.