{"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/homogeneity-based-transmissive-process-to","title":"Homogeneity-Based Transmissive Process to Model True and False News in Social Networks","arxiv_id":"1811.09702","date":"2018-11-16","proceeding":null,"authors":["Jooyeon Kim","Dongkwan Kim","Alice Oh"],"abstract":"An overwhelming number of true and false news stories are posted and shared\nin social networks, and users diffuse the stories based on multiple factors.\nDiffusion of news stories from one user to another depends not only on the\nstories' content and the genuineness but also on the alignment of the topical\ninterests between the users. In this paper, we propose a novel Bayesian\nnonparametric model that incorporates homogeneity of news stories as the key\ncomponent that regulates the topical similarity between the posting and sharing\nusers' topical interests. Our model extends hierarchical Dirichlet process to\nmodel the topics of the news stories and incorporates Bayesian Gaussian process\nlatent variable model to discover the homogeneity values. We train our model on\na real-world social network dataset and find homogeneity values of news stories\nthat strongly relate to their labels of genuineness and their contents.\nFinally, we show that the supervised version of our model predicts the labels\nof news stories better than the state-of-the-art neural network and Bayesian\nmodels.","url_abs":"http://arxiv.org/abs/1811.09702v1","url_pdf":"http://arxiv.org/pdf/1811.09702v1.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":"homogeneity-based-transmissive-process-to","repo_url":"https://github.com/dongkwan-kim/HBTP","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}