2016/03/14 by Przemyslaw A. Grabowicz, Grabowicz, Przemyslaw A., Niloy Ganguly +3
Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Physical sciences #H.3.5 #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Social Media and Politics #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1603.04425
openalex publication_date 2016/03/14 · openalex created_date 2022/11/05 · openalex updated_date 2026/07/28
A number of recent studies of information diffusion in social media, both\nempirical and theoretical, have been inspired by viral propagation models\nderived from epidemiology. These studies model the propagation of memes, i.e.,\npieces of information, between users in a social network similarly to the way\ndiseases spread in human society. Importantly, one would expect a meme to\nspread in a social network amongst the people who are interested in the topic\nof that meme. Yet, the importance of topicality for information diffusion has\nbeen less explored in the literature.\n Here, we study empirical data about two different types of memes (hashtags\nand URLs) spreading through the Twitter's online social network. For every\nmeme, we infer its topics and for every user, we infer her topical interests.\nTo analyze the impact of such topics on the propagation of memes, we introduce\na novel theoretical framework of information diffusion. Our analysis identifies\ntwo distinct mechanisms, namely topical and non-topical, of information\ndiffusion. The non-topical information diffusion resembles disease spreading as\nin simple contagion. In contrast, the topical information diffusion happens\nbetween users who are topically aligned with the information and has\ncharacteristics of complex contagion. Non-topical memes spread broadly among\nall users and end up being relatively popular. Topical memes spread narrowly\namong users who have interests topically aligned with them and are diffused\nmore readily after multiple exposures. Our results show that the topicality of\nmemes and users' interests are essential for understanding and predicting\ninformation diffusion.\n