{"paper":{"title":"Classify Sina Weibo users into High or Low happiness Groups Using Linguistic and Behavior Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Bibo Hao, Jingying Wang, Lei Zhang, Tianli Liu, Tingshao Zhu, Zhenxiang Chen","submitted_at":"2015-07-07T13:24:34Z","abstract_excerpt":"It's of great importance to measure happiness of social network users, but the existing method based on questionnaires suffers from high costs and low efficiency. This paper aims at identifying social network users' happiness level based on their Web behavior. We recruited 548 participants to fill in the Oxford Happiness Inventory (OHI) and divided them into two groups with high/low OHI score. We downloaded each Weibo user's data by calling API, and extracted 103 linguistic and behavior features. 24 features are identified with significant difference between high and low happiness groups. We t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.01796","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}