{"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/learning-from-various-labeling-strategies-for","title":"Learning from various labeling strategies for suicide-related messages on social media: An experimental study","arxiv_id":"1701.08796","date":"2017-01-30","proceeding":null,"authors":["Tong Liu","Qijin Cheng","Christopher M. Homan","Vincent M. B. Silenzio"],"abstract":"Suicide is an important but often misunderstood problem, one that researchers\nare now seeking to better understand through social media. Due in large part to\nthe fuzzy nature of what constitutes suicidal risks, most supervised approaches\nfor learning to automatically detect suicide-related activity in social media\nrequire a great deal of human labor to train. However, humans themselves have\ndiverse or conflicting views on what constitutes suicidal thoughts. So how to\nobtain reliable gold standard labels is fundamentally challenging and, we\nhypothesize, depends largely on what is asked of the annotators and what slice\nof the data they label. We conducted multiple rounds of data labeling and\ncollected annotations from crowdsourcing workers and domain experts. We\naggregated the resulting labels in various ways to train a series of supervised\nmodels. Our preliminary evaluations show that using unanimously agreed labels\nfrom multiple annotators is helpful to achieve robust machine models.","url_abs":"http://arxiv.org/abs/1701.08796v1","url_pdf":"http://arxiv.org/pdf/1701.08796v1.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":"learning-from-various-labeling-strategies-for","repo_url":"https://github.com/Homan-Lab/pldl_data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.08796","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}