The Life and Times of Information in Networks

Cascades of information-sharing are a primary mechanism by which content reaches its audience on social media. In this talk, I will describe three large-scale analyses of reshare cascades on Facebook, which were performed in aggregate using de-identified data. The first study aims to understand how predictable the growth of cascades is. We formulate the problem as one of predicting whether a cascade will double in size, and find that the prediction accuracy increases the longer a cascade has been observed. Furthermore, temporal and structural features of the cascade, as well as properties of its origin and content, along with the characteristics of those participating, are all useful in predicting how much more a cascade will grow. If we examine these cascades over significantly longer time scales, we find that many large cascades recur, exhibiting multiple bursts of popularity with periods of quiescence in between. We characterize recurrence by measuring the time elapsed between bursts, their overlap and proximity in the social network, and the diversity in the demographics of individuals participating in each peak. We discover that content virality, as revealed by its initial popularity, is a main driver of recurrence, with the availability of multiple copies of that content helping to spark new bursts. Still, beyond a certain popularity of content, the rate of recurrence drops as cascades start exhausting the population of interested individuals. We reproduce these observed patterns in a simple model of content recurrence simulated on a real social network. Using only characteristics of a cascade’s initial burst, we demonstrate strong performance in predicting whether it will recur in the future. Finally, I will discuss not just how information is transmitted perfectly, but how it evolves as changes are made as it is copied. Using a dataset of thousands of memes collectively replicated hundreds of millions of times, we find that 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.