{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tracking-the-diffusion-of-named-entities","title":"Tracking the Diffusion of Named Entities","arxiv_id":"1712.08349","date":"2017-12-22","proceeding":null,"authors":["Leon Derczynski","Matthew Rowe"],"abstract":"Existing studies of how information diffuses across social networks have thus\nfar concentrated on analysing and recovering the spread of deterministic\ninnovations such as URLs, hashtags, and group membership. However investigating\nhow mentions of real-world entities appear and spread has yet to be explored,\nlargely due to the computationally intractable nature of performing large-scale\nentity extraction. In this paper we present, to the best of our knowledge, one\nof the first pieces of work to closely examine the diffusion of named entities\non social media, using Reddit as our case study platform. We first investigate\nhow named entities can be accurately recognised and extracted from discussion\nposts. We then use these extracted entities to study the patterns of entity\ncascades and how the probability of a user adopting an entity (i.e. mentioning\nit) is associated with exposures to the entity. We put these pieces together by\npresenting a parallelised diffusion model that can forecast the probability of\nentity adoption, finding that the influence of adoption between users can be\ncharacterised by their prior interactions -- as opposed to whether the users\npropagated entity-adoptions beforehand. Our findings have important\nimplications for researchers studying influence and language, and for community\nanalysts who wish to understand entity-level influence dynamics.","url_abs":"http://arxiv.org/abs/1712.08349v2","url_pdf":"http://arxiv.org/pdf/1712.08349v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tracking-the-diffusion-of-named-entities","repo_url":"https://github.com/mrowebot/NER-Diff-Paper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"entity-extraction","task_name":"Entity Extraction using GAN"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}