Detecting Differences in Community Conversations with Epistemic Network Analysis

Current research around science education and communication on social media sites, particularly Twitter (also known as X), lacks holistic exploration and explanation of three key aspects: determining who is involved in conversations, the content people are communicating about, and reach of that content. To integrate these aspects and gain a holistic understanding of connections between learners, content, and the online social world, we used Epistemic Network Analysis (ENA). ENA is well-used in research on discussion boards where a) learners have an impetus for posting and b) there are fewer conversations which creates dense networks with low topic variation. We use ENA in a real-world setting where multiple conversations, large numbers of learners, and the voluntary nature of engagement create sparse networks with high topic variability. We use a pre-existing Twitter dataset from a community focused on paleontology. Data were collected from July 2017 through August 2018. Nearly 8,000 tweets were created by 1,290 entities. Findings indicate differences in individuals, conversation structure, and reach of conversations that were originally hidden when analyzed with social network analysis. With this research, we provide an integrated view of connections between learners, content, and their online social world. We highlight a need for employing ENA within real-world settings that include large numbers of users, variable topics, and multiple conversations.