Tracking the Diffusion of Named Entities
This work addresses a gap in understanding entity-level information spread for researchers and community analysts, though it is incremental as it applies existing diffusion concepts to a new data type.
The paper tackles the problem of tracking how named entities diffuse across social networks, a previously unexplored area due to computational challenges, by developing a method to extract entities from Reddit posts and modeling entity adoption probabilities, finding that prior user interactions, rather than previous entity adoptions, characterize influence.
Existing studies of how information diffuses across social networks have thus far concentrated on analysing and recovering the spread of deterministic innovations such as URLs, hashtags, and group membership. However investigating how mentions of real-world entities appear and spread has yet to be explored, largely due to the computationally intractable nature of performing large-scale entity extraction. In this paper we present, to the best of our knowledge, one of the first pieces of work to closely examine the diffusion of named entities on social media, using Reddit as our case study platform. We first investigate how named entities can be accurately recognised and extracted from discussion posts. We then use these extracted entities to study the patterns of entity cascades and how the probability of a user adopting an entity (i.e. mentioning it) is associated with exposures to the entity. We put these pieces together by presenting a parallelised diffusion model that can forecast the probability of entity adoption, finding that the influence of adoption between users can be characterised by their prior interactions -- as opposed to whether the users propagated entity-adoptions beforehand. Our findings have important implications for researchers studying influence and language, and for community analysts who wish to understand entity-level influence dynamics.