Social Multimedia, Diversity, and Global South Cities

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. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing design and evaluation methods.

Zurich Like New

“…On the other hand, the ResNet50 that extracts the semantic visual features is likely to contain bias towards western countries, as is the case with several the popular image databases used for deep learning [16]. This is a problem recently illustrated in [8] in the context of African cities. This situation calls for careful thinking about what parts of the methodology would have to be adapted or retrained to capture the large diversity in cities, specifically in the Global South.…”

“…Citizen reports contain time, location, text descriptions, and photos. Some works have studied the application of machine learning methods on these multimodal data sources to characterize reported issues, ranging from heavy traffic [8] to so-called urban micro-events [19]. Santani et al [8] studied Ma3Route, a mobile social media channel to crowdsource traffic reports in the city of Nairobi.…”

“…Some works have studied the application of machine learning methods on these multimodal data sources to characterize reported issues, ranging from heavy traffic [8] to so-called urban micro-events [19]. Santani et al [8] studied Ma3Route, a mobile social media channel to crowdsource traffic reports in the city of Nairobi. Sukel et al [19] used over 500K citizen reports from the city of Amsterdam and implemented a classification task of urban micro-events, e.g., loud boat, [20].…”

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning

“…Knowing that a small portion (<1%) of the road network hosts 50% of RTCs in the crowdsourced data can help reduce an intractable problem to a more manageable one. This analysis shows the potential for using this data to complement road safety diagnostics and to guide investments and planning in road safety in Kenya and in other contexts, especially those with similar data deficiencies and with sufficient social media density like India and the Philippines [35].…”

Applying Machine Learning and Geolocation Techniques to Social Media Data (Twitter) to Develop a Resource for Urban Planning

“…Knowing that a small portion (<1%) of the road network hosts 50% of RTCs in the crowdsourced data can help reduce an intractable problem to a more manageable one. This analysis shows the potential for using these data to complement road safety diagnostics and to guide investments and planning in road safety in Kenya and in other contexts, especially those with similar data deficiencies and with sufficient social media density like India and the Philippines [35].…”