Tracking the Diffusion of Named Entities

TL;DR

This paper explores how real-world entities spread on social media, particularly Reddit, by developing methods for entity recognition and a diffusion model that predicts entity adoption based on user interactions.

Contribution

It introduces a novel approach to extract and analyze the diffusion of named entities on social media, with a parallelized model for forecasting entity adoption.

Findings

Entity adoption influenced by prior user interactions

Proposed diffusion model accurately forecasts entity spread

Highlights importance of interaction history over propagation history

Abstract

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.