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Answers attract a diverse pool of users from many walks of life, other sites cater to a specific user pool. While identifying bad CQA content is generally important in order to improve sites' overall health and community knowledge\u2010sharing, examining educational CQAs is particularly urgent in order to help struggling students understand why their questions fail, re\u2010frame their inquiries in a more accurate manner based on feedback, and ultimately receive correct answers that facilitate their learning process. Otherwise, students' questions would merely be deleted, meaning they lose multiple opportunities to enrich their knowledge base. In this work, we focus on questions posted to Brainly, the largest educational CQA site, in order to first identify \u201cbad\u201d questions and next understand what textual (content\u2010based) features contribute to such questions' poor quality. Using a sample of 1,000 questions\u2013500 of which were deemed \u201cgood\u201d and 500 of which were deemed \u201cbad\u201d by site moderators\u2013 we attempt to automatically classify question quality in order to label which questions would be deleted and therefore go unanswered. We then use human assessment to expand upon a typology to classify poor quality questions based on 14 textual features in order to identify why they have been marked for deletion. Finally, we propose a method to automatically identify questions' problematic textual features in order to provide feedback to students posting \u201cbad\u201d questions and ensure that they are given the opportunity to revise and improve their inquiries to obtain accurate answers that resolve their information needs.<\/jats:p>","DOI":"10.1002\/pra2.2017.14505401036","type":"journal-article","created":{"date-parts":[[2017,10,24]],"date-time":"2017-10-24T03:35:36Z","timestamp":1508816136000},"page":"327-336","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Identifying the reasons contributing to question deletion in educational Q&amp;A"],"prefix":"10.1002","volume":"54","author":[{"given":"Manasa","family":"Rath","sequence":"first","affiliation":[{"name":"School of Communication &amp; Information (SC&amp;I)  USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chirag","family":"Shah","sequence":"additional","affiliation":[{"name":"School of Communication &amp; Information (SC&amp;I)  USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diana","family":"Floegel","sequence":"additional","affiliation":[{"name":"School of Communication &amp; Information (SC&amp;I)  USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2017,10,24]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"crossref","unstructured":"Adamic L. A. Zhang J. Bakshy E. andAckerman M. S.(2008).Knowledge sharing and yahoo answers: Everyone know something. In Proceedings of the 17th international conference on World Wide Web pages665\u2013674.ACM.'","DOI":"10.1145\/1367497.1367587"},{"key":"e_1_2_12_3_1","doi-asserted-by":"crossref","unstructured":"Agichtein E. Castillo C. Donato D. Gionis A. andMishne G.(2008).Finding high\u2010quality content in social media. In Proceedings of the 2008 international conference on web search and data mining pages183\u2013194.ACM.","DOI":"10.1145\/1341531.1341557"},{"key":"e_1_2_12_4_1","unstructured":"Angell D. F.andHeslop B.(1994).Elements of e\u2010mail international conference on World Wide Webpages665\u2013674.ACM.\u2019"},{"key":"e_1_2_12_5_1","doi-asserted-by":"crossref","unstructured":"Aritajati C.andNarayanan N. H.(2013).Facilitating students' collaboration and learning in a question and answer system. 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