On Designing a Time Sensitive Interaction Graph to Identify Twitter Opinion Leaders

Abstract

What happened on social media during the recent pandemic? Who was the opinion leader of the conversations? Who influenced whom? Were they medical doctors, ordinary people, scientific experts? Did health institutions play an important role in informing and updating citizens? Identifying opinion leaders within social platforms is of particular importance and, in this paper, we introduce the idea of a time sensitive interaction graph to identify opinion leaders within Twitter conversations. To evaluate our proposal, we focused on all the tweets posted on Twitter in the period 2020-21 and we considered just the ones that were Italian-written and were related to COVID-19. After mapping these tweets into the graph, we applied the PageRank algorithm to extract the opinion leaders of these conversations. Results show that our approach is effective in identifying opinion leaders and therefore it might be used to monitor the role that specific accounts (i.e., health authorities, politicians, city administrators) have within specific conversations.