Social Multimedia, Diversity, and Global South Cities: A Double Blind Side

Abstract

Social media provides opportunities to examine urban phenomena at scale, and we believe that studying cities in the Global South through citizen-contributed data and AI-driven analytics should be a priority of multimedia research. However, little work has been done in our community, and we argue that this contributes to a double blind side problem. We exemplify this situation by studying Ma3Route, a mobile social media channel to crowdsource and broadcast transit reports in Nairobi, Kenya. Using multimedia data from its Twitter stream, we first conduct a descriptive analysis that shows an active community generating rich traffic-related reports, and then discover latent topics that identify both regular and ephemeral thematic clusters of reports involving accidents, traffic conditions, and attitudes of citizens towards authorities. In the second place, we conduct a deep learning-based analysis of Ma3Route images to understand the kind of visual content shared in the platform, and that shows limitations of using deep neural network models trained with data largely coming from the US and Europe, which do not fully match the reality and diversity of other world regions. We conclude by presenting a multidisciplinary research agenda for future work in this domain.