Modeling Multi-Platform Information Diffusion in Social Media: Data-Driven Observations
DescriptionAccurately modeling information diffusion within and across social media platforms has many practical applications, such as estimating the size of the audience exposed to a particular narrative or testing intervention techniques for addressing misinformation. However, it turns out that real data reveal phenomena that pose significant challenges to modeling: events in the physical world affect in varying ways conversations on different social media platforms; coordinated influence campaigns may swing discussions in unexpected directions; a platform’s algorithms direct who sees which message. This talk will discuss challenges in modeling social medial activity in various contexts, from political crises to coordinated disinformation campaigns.
TimeTuesday, June 2717:30 - 18:00 CEST
Computer Science, Machine Learning, and Applied Mathematics