{"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/predicting-twitter-user-socioeconomic","title":"Predicting Twitter User Socioeconomic Attributes with Network and Language Information","arxiv_id":"1804.04095","date":"2018-04-11","proceeding":null,"authors":["Nikolaos Aletras","Benjamin Paul Chamberlain"],"abstract":"Inferring socioeconomic attributes of social media users such as occupation\nand income is an important problem in computational social science. Automated\ninference of such characteristics has applications in personalised recommender\nsystems, targeted computational advertising and online political campaigning.\nWhile previous work has shown that language features can reliably predict\nsocioeconomic attributes on Twitter, employing information coming from users'\nsocial networks has not yet been explored for such complex user\ncharacteristics. In this paper, we describe a method for predicting the\noccupational class and the income of Twitter users given information extracted\nfrom their extended networks by learning a low-dimensional vector\nrepresentation of users, i.e. graph embeddings. We use this representation to\ntrain predictive models for occupational class and income. Results on two\npublicly available datasets show that our method consistently outperforms the\nstate-of-the-art methods in both tasks. We also obtain further significant\nimprovements when we combine graph embeddings with textual features,\ndemonstrating that social network and language information are complementary.","url_abs":"http://arxiv.org/abs/1804.04095v1","url_pdf":"http://arxiv.org/pdf/1804.04095v1.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":"predicting-twitter-user-socioeconomic","repo_url":"https://github.com/melifluos/income-prediction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1804.04095","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}