Planting memes: the shape of information flow in social networks

The movement of information through virtual social networks has proven to be a primary factor in people's knowledge of, perspective on, and engagement with local and global events; understanding the function of these evolving infrastructures is paramount. Although recent work has studied the spread of (mis)information through hashtags and metadata and theories of information diffusion have been developed, the behavior of networks depends sensitively on their topology, which is difficult to access and characterize. We study the informatic impulse of image-based internet memes through social networks by applying principles of analysis inspired by condensed matter physics. Internet memes - which contain an identifiable image that is unchanging with time and text containing evolving sentiments - are an excellent parcel of information that can be tracked to examine network topology. Memes are scraped from social media networks, and the underlying images and texts are categorized using support vector and text recognition algorithms, respectively. Sharing metadata is used to construct the branching path of information transfer. This way we treat each new meme species as an informatic impulse into a network and observe its propagation, decay, and robustness within the network topology.