{"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/on-the-complexity-of-opinions-and-online","title":"On the Complexity of Opinions and Online Discussions","arxiv_id":"1802.06807","date":"2018-02-19","proceeding":null,"authors":["Utkarsh Upadhyay","Abir De","Aasish Pappu","Manuel Gomez-Rodriguez"],"abstract":"In an increasingly polarized world, demagogues who reduce complexity down to\nsimple arguments based on emotion are gaining in popularity. Are opinions and\nonline discussions falling into demagoguery? In this work, we aim to provide\ncomputational tools to investigate this question and, by doing so, explore the\nnature and complexity of online discussions and their space of opinions,\nuncovering where each participant lies.\n  More specifically, we present a modeling framework to construct latent\nrepresentations of opinions in online discussions which are consistent with\nhuman judgements, as measured by online voting. If two opinions are close in\nthe resulting latent space of opinions, it is because humans think they are\nsimilar. Our modeling framework is theoretically grounded and establishes a\nsurprising connection between opinions and voting models and the sign-rank of a\nmatrix. Moreover, it also provides a set of practical algorithms to both\nestimate the dimension of the latent space of opinions and infer where opinions\nexpressed by the participants of an online discussion lie in this space.\nExperiments on a large dataset from Yahoo! News, Yahoo! Finance, Yahoo! Sports,\nand the Newsroom app suggest that unidimensional opinion models may often be\nunable to accurately represent online discussions, provide insights into human\njudgements and opinions, and show that our framework is able to circumvent\nlanguage nuances such as sarcasm or humor by relying on human judgements\ninstead of textual analysis.","url_abs":"http://arxiv.org/abs/1802.06807v2","url_pdf":"http://arxiv.org/pdf/1802.06807v2.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":"on-the-complexity-of-opinions-and-online","repo_url":"https://github.com/Networks-Learning/discussion-complexity","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}