A graph mining method for characterizing and measuring user engagement in Twitter

Ioannis Karamitsos, Alaa Mohasseb, Andreas Kanavos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the modern world, social media plays a crucial role in the interchange of information and socialization with users. Twitter is a known social media platform that allows users to make relationships with others and express their opinions. The current work aims to identify the level of user engagement on Twitter with the use of graph mining. User engagement concerns the number of user connections with a tweet and can be measured using different tweet attributes including retweets, replies, etc. Specifically, this study investigates the variety of edges strength that user connections can implement in Twitter networks. Next, we employed various weights in the graph mining models to evaluate the score of each connection. These tasks were followed by statistical analysis to measure the similarity between the two user profiles as well as attributes like friendship, following and interaction in the Twitter social network. Results indicate that closely linked groups can be revealed and thus, a need for examining both group and individual behavior, will arise.
Original languageEnglish
Title of host publicationProceedings of the 17th International Workshop on Semantic and Social Media Adaptation & Personalization
PublisherIEEE
Publication statusAccepted for publication - 1 Oct 2022
Event17th International Workshop on Semantic and Social Media Adaptation & Personalization - Online
Duration: 3 Nov 20224 Nov 2022

Workshop

Workshop17th International Workshop on Semantic and Social Media Adaptation & Personalization
Period3/11/224/11/22

Keywords

  • Community Analysis
  • Graph Mining
  • Hemophilia
  • Social Media
  • Twitter
  • Twitter Analytics
  • User Engagement

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