{"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/classifying-the-ideological-orientation-of","title":"Classifying the Ideological Orientation of User-Submitted Texts in Social Media","arxiv_id":null,"date":"2022-12-12","proceeding":"IEEE International Conference on Machine Learning and Applications (ICMLA) 2022 12","authors":["Kamalakkannan Ravi","Adan Ernesto Vela","Rickard Ewetz"],"abstract":"With the long-term goal of understanding how language is used and evolves within online communities, this work explores the application of natural language processing techniques to classify text articles according to their ideological orientation (i.e., conservative or liberal). We first collect a balanced corpus of text articles posted to the online communities r/Liberal and r/Conservative from the social media website Reddit. Using the corpus, we develop and apply three classifiers. The baseline classifier is a Bayes model that accounts for each text article’s web domain, as such, classification is independent of content. Next, we develop a support vector machine (SVM) model with term frequency-inverse document frequency (TF-IDF) features; this approach highlight differences in language using a count-based feature-space to differentiate text articles. Last, we evaluate the context-based transformer (RoBERTa) model and discuss its under-performance relative to the baseline and SVM models.","url_abs":"https://ieeexplore.ieee.org/document/10069289","url_pdf":"https://ieeexplore.ieee.org/document/10069289","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":"classifying-the-ideological-orientation-of","repo_url":"https://github.com/ADCLab/RedditIdeologyDB","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"news-classification","task_name":"News Classification"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roberta","method_name":"RoBERTa"},{"method_slug":"svm","method_name":"SVM"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[{"slug":"reddit-ideology-database","name":"Reddit Ideology Database","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/classification-on-reddit-ideology-database","task":"Classification","dataset":"Reddit Ideology Database","model":"SVM","rank_in_archive_order":1,"of":2,"metrics":{"F1-score (Weighted)":"86.19"},"uses_additional_data":false},{"leaderboard":"/sota/classification-on-reddit-ideology-database","task":"Classification","dataset":"Reddit Ideology Database","model":"RoBERTa","rank_in_archive_order":2,"of":2,"metrics":{"F1-score (Weighted)":"78.13"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}