text: 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.
  @cite_5: This report finds that American young men are especially likely to visit the “front page of the internet.” Overview Six percent of online adults are reddit users, according to a nationally representative survey by the Pew Research Center’s Internet & American Life Project. This is our first survey that specifically examines reddit as a standalone platform, and it finds that young men are especially likely to visit the site. Some 15 of male internet users ages 18-29 say that they use reddit, compared with 5 of women in the same age range and 8 of men ages 30-49. About the survey The results in this report are based on data from telephone interviews conducted by Princeton Survey Research Associates International from April 17 to May 19, 2013, among a sample of 2,252 adults, age 18 and older. Telephone interviews were conducted in English and Spanish by landline (1,125) and cell phone (1,127, including 571 without a landline phone). For results based on the total sample, one can say with 95 confidence that the error attributable to sampling is plus or minus 2.3 percentage points. For results based on Internet users (n=1,895), the margin of sampling error is plus or minus 2.5 percentage points.
  @cite_4: The real-time information on news sites, blogs and social networking sites changes dynamically and spreads rapidly through the Web. Developing methods for handling such information at a massive scale requires that we think about how information content varies over time, how it is transmitted, and how it mutates as it spreads. We describe the News Information Flow Tracking, Yay! (NIFTY) system for large scale real-time tracking of "memes" - short textual phrases that travel and mutate through the Web. NIFTY is based on a novel highly-scalable incremental meme-clustering algorithm that efficiently extracts and identifies mutational variants of a single meme. NIFTY runs orders of magnitude faster than our previous Memetracker system, while also maintaining better consistency and quality of extracted memes. We demonstrate the effectiveness of our approach by processing a 20 terabyte dataset of 6.1 billion blog posts and news articles that we have been continuously collecting for the last four years. NIFTY extracted 2.9 billion unique textual phrases and identified more than 9 million memes. Our meme-tracking algorithm was able to process the entire dataset in less than five days using a single machine. Furthermore, we also provide a live deployment of the NIFTY system that allows users to explore the dynamics of online news in near real-time.
summary: Studies of information diffusion have largely concentrated on signals of diffusion such as tracking URLs, hashtags, quotes @cite_4 , and adoption behaviour (e.g. group signups); however to the best of our knowledge such studies have yet to focus on how entities diffuse. We now focus on key pieces of work that are closely-aligned to the study of entity-diffusion in the context of social networks -- should the reader wish to know more about information diffusion models, and in greater detail, then please refer to 's @cite_5 comprehensive survey of such models.