Using Social Network Analysis to Understand Public Discussions: The Case Study of #SaudiWomenCanDrive on Twitter

Social media analytics has experienced significant growth over the past few years due to the crucial importance of analyzing and measuring public social behavior on different social networking sites. Twitter is one of the most popular social networks and means of online news that allows users to express their views and participate in a wide range of different issues in the world. Expressed opinions on Twitter are based on diverse experiences that represent a broad set of valuable data that can be analyzed and used for many purposes. This study aims to understand the public discussions that are conducted on Twitter about essential topics and developing an analytics framework to analyze these discussions. The focus of this research is the analytical framework of Arabic public discussions using the hashtag #SaudiWomenCanDrive, as one of the hot trends of Twitter discussions. The proposed framework analyzed more than two million tweets using methods from social network analysis. The framework uses the metrics of graph centrality to reveal essential people in the discussion and community detection methods to identify the communities and topics used in the discussion. Results show that @SaudiNews50, @Algassabinasser, and @Abdulrahman were top users in two networks, while @KingSalman and @LoujainHathloul were the top two users in another network. Consequently, “King Salman” and “Loujain Hathloul” Twitter accounts were identified as influencers, whereas “Saudi News” and “Algassabi Nasser” were the leading distributors of the news. Therefore, similar phenomena could be analyzed using the proposed framework to analyze similar behavior on other public discussions.

@article{Jastania2020,
  title     = {Using Social Network Analysis to Understand Public Discussions: The Case Study of #SaudiWomenCanDrive on Twitter},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {2},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Zubaida Jastania and Mohammad Ahtisham Aslam and Rabeeh Ayaz Abbasi and Kawther Saeedi},
  doi       = {10.14569/IJACSA.2020.0110230},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110230}
}