Information Evolution in Social Networks
This research addresses how information evolves during diffusion in social networks, with implications for interpreting and harnessing online content, though it is incremental in applying existing evolutionary models.
The study measured the imperfect copying of information in social networks by analyzing thousands of memes replicated on Facebook, finding that mutation rates follow the Yule process and variants farther apart in diffusion have greater edit distances.
Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.