This paper attempts to move beyond the left-right\u00a0characterization of political ideologies. We propose\u00a0a trait based probabilistic model for estimating\u00a0the manifold of political opinion. We demonstrate\u00a0the efficacy of our model on two novel and\u00a0large scale datasets of public opinion. Our experiments\u00a0show that although the political spectrum\u00a0is richer than a simple left-right structure, peoples\u2019\u00a0opinions on seemingly unrelated political\u00a0issues are very correlated, so fewer than 10 dimensions\u00a0are enough to represent peoples\u2019 entire\u00a0political opinion.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper attempts to move beyond the left-right\u00a0characterization of political ideologies. We propose\u00a0a trait based probabilistic model for estimating\u00a0the manifold of political opinion. We demonstrate\u00a0the efficacy of our model on two novel and\u00a0large scale datasets of public opinion. Our experiments\u00a0show that although the political spectrum\u00a0is richer than a simple left-right structure, peoples\u2019\u00a0opinions on seemingly unrelated […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13563,13559],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-249098","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-data-platform-analytics","msr-research-area-social-sciences","msr-locale-en_us"],"msr_publishername":"AUAI Press","msr_edition":"Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, (UAI), June 25-29, 2016, New York City, NY","msr_affiliation":"","msr_published_date":"2016-07-06","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"978-0-9966431-1-5","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"249101","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"main","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/07\/main-1.pdf","id":249101,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Yoad Lewenberg","user_id":0,"rest_url":false},{"type":"text","value":"Yoram Bachrach","user_id":0,"rest_url":false},{"type":"text","value":"Lucas Bordeaux","user_id":0,"rest_url":false},{"type":"user_nicename","value":"pkohli","user_id":33269,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=pkohli"}],"msr_impact_theme":[],"msr_research_lab":[199561],"msr_event":[],"msr_group":[],"msr_project":[169917],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":169917,"post_title":"Infer.NET","post_name":"infernet","post_type":"msr-project","post_date":"2008-10-15 01:55:31","post_modified":"2023-04-06 09:14:43","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/infernet\/","post_excerpt":"Infer.NET is a .NET library for machine learning. 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