This poster was presented at Computer Graphics & Visual Computing (CGVC24) at City St George's, University of London.
Bdour Alwuqaysi, Alfie Abdul-Rahman, Rita Borgo
In this work, we present the first stage of our ongoing research aimed at developing content-based categorization for social media (SM) platforms. In this work, we present the first stage of our ongoing research aimed at visualizing the content-based categorization for SM platforms to understand how users utilize them. Different content types attract distinct audiences with unique expectations. This can help categorize platforms based on user demands and interests, benefiting researchers, marketers, and individuals seeking to use SM effectively. We surveyed 194 social media users, primarily focusing on those from the United Kingdom, who comprised 76.29% of the sample in 2023. We ask users to categorize the most commonly used SM platforms by choosing from the following categories, image-based, text-based, image-text, and not familiar, aiming to capture the dynamic nature of user content-based interactions. Our findings suggest that users categorize social media based on their usage patterns, regardless of intended purpose. Our next step is to study how usage varies across demographics and impacts individuals