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Automated cyberbullying detection methods can help prevent cyberbullying before the harm is done on the victim. In this study, we analyze two corpora of cyberbullying tweets from similar incidents to construct and validate an automated detection model. Our method emphasizes the two claims that are supported by our results. First, despite other approaches that assume that cyberbullying instances use vulgar or profane words, we show that they do not necessarily contain negative words. Second, we highlight the importance of context and the characteristics of actors involved and their position in the network structure in detecting cyberbullying rather than only considering the textual content in our analysis.<\/jats:p>","DOI":"10.1145\/3290838","type":"journal-article","created":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T13:39:21Z","timestamp":1545399561000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["A Socio-Contextual Approach in Automated Detection of Public Cyberbullying on Twitter"],"prefix":"10.1145","volume":"1","author":[{"given":"Nargess","family":"Tahmasbi","sequence":"first","affiliation":[{"name":"Pennsylvania State University, PA, USA"}]},{"given":"Elham","family":"Rastegari","sequence":"additional","affiliation":[{"name":"University of Nebraska Omaha, NE, USA"}]}],"member":"320","published-online":{"date-parts":[[2018,12,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-psych-010213-115030"},{"key":"e_1_2_1_2_1","article-title":"Definition and measurement of cyberbullying. 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