2012/08/13 by Yanxiang Wang, Wang, Yanxiang, Hari Sundaram +3
Computer Science · Physics and Astronomy · Social Sciences · #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #H.3.3 #Information Retrieval (cs.IR) #Misinformation and Its Impacts #Multimedia (cs.MM) #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.IR #cs.MM #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1208.2547
ACM Multimedia 2012
arxiv created 2012/08/13 · openalex publication_date 2012/08/13 · arxiv updated 2015/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper focuses on detecting social, physical-world events from photos posted on social media sites. The problem is important: cheap media capture devices have significantly increased the number of photos shared on these sites. The main contribution of this paper is to incorporate online social interaction features in the detection of physical events. We believe that online social interaction reflect important signals among the participants on the "social affinity" of two photos, thereby helping event detection. We compute social affinity via a random-walk on a social interaction graph to determine similarity between two photos on the graph. We train a support vector machine classifier to combine the social affinity between photos and photo-centric metadata including time, location, tags and description. Incremental clustering is then used to group photos to event clusters. We have very good results on two large scale real-world datasets: Upcoming and MediaEval. We show an improvement between 0.06-0.10 in F1 on these datasets.