J. Golbeck, Zahra Ashktorab, Rashad O. Banjo, Alexandra Berlinger, Siddharth Bhagwan, C. Buntain, Paul Cheakalos, Alicia A. Geller, Quint Gergory, R. Gnanasekaran, Raja Rajan Gunasekaran, K. Hoffman, Jenny Hottle, Vichita Jienjitlert, Shivika Khare, Ryan Lau, Marianna J. Martindale, Shalmali Naik, Heather L. Nixon, P. Ramachandran, Kristine M. Rogers, Lisa Rogers, Meghna Sardana Sarin, Gaurav Shahane, Jayanee Thanki, Priyanka Vengataraman, Zijian Wan, D. Wu
{"title":"面向网络骚扰研究的大型标注语料库","authors":"J. Golbeck, Zahra Ashktorab, Rashad O. Banjo, Alexandra Berlinger, Siddharth Bhagwan, C. Buntain, Paul Cheakalos, Alicia A. Geller, Quint Gergory, R. Gnanasekaran, Raja Rajan Gunasekaran, K. Hoffman, Jenny Hottle, Vichita Jienjitlert, Shivika Khare, Ryan Lau, Marianna J. Martindale, Shalmali Naik, Heather L. Nixon, P. Ramachandran, Kristine M. Rogers, Lisa Rogers, Meghna Sardana Sarin, Gaurav Shahane, Jayanee Thanki, Priyanka Vengataraman, Zijian Wan, D. Wu","doi":"10.1145/3091478.3091509","DOIUrl":null,"url":null,"abstract":"A fundamental part of conducting cross-disciplinary web science research is having useful, high-quality datasets that provide value to studies across disciplines. In this paper, we introduce a large, hand-coded corpus of online harassment data. A team of researchers collaboratively developed a codebook using grounded theory and labeled 35,000 tweets. Our resulting dataset has roughly 15% positive harassment examples and 85% negative examples. This data is useful for training machine learning models, identifying textual and linguistic features of online harassment, and for studying the nature of harassing comments and the culture of trolling.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"146 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"170","resultStr":"{\"title\":\"A Large Labeled Corpus for Online Harassment Research\",\"authors\":\"J. Golbeck, Zahra Ashktorab, Rashad O. Banjo, Alexandra Berlinger, Siddharth Bhagwan, C. Buntain, Paul Cheakalos, Alicia A. Geller, Quint Gergory, R. Gnanasekaran, Raja Rajan Gunasekaran, K. Hoffman, Jenny Hottle, Vichita Jienjitlert, Shivika Khare, Ryan Lau, Marianna J. Martindale, Shalmali Naik, Heather L. Nixon, P. Ramachandran, Kristine M. Rogers, Lisa Rogers, Meghna Sardana Sarin, Gaurav Shahane, Jayanee Thanki, Priyanka Vengataraman, Zijian Wan, D. Wu\",\"doi\":\"10.1145/3091478.3091509\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A fundamental part of conducting cross-disciplinary web science research is having useful, high-quality datasets that provide value to studies across disciplines. In this paper, we introduce a large, hand-coded corpus of online harassment data. A team of researchers collaboratively developed a codebook using grounded theory and labeled 35,000 tweets. Our resulting dataset has roughly 15% positive harassment examples and 85% negative examples. This data is useful for training machine learning models, identifying textual and linguistic features of online harassment, and for studying the nature of harassing comments and the culture of trolling.\",\"PeriodicalId\":165747,\"journal\":{\"name\":\"Proceedings of the 2017 ACM on Web Science Conference\",\"volume\":\"146 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"170\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 ACM on Web Science Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3091478.3091509\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 ACM on Web Science Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3091478.3091509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Large Labeled Corpus for Online Harassment Research
A fundamental part of conducting cross-disciplinary web science research is having useful, high-quality datasets that provide value to studies across disciplines. In this paper, we introduce a large, hand-coded corpus of online harassment data. A team of researchers collaboratively developed a codebook using grounded theory and labeled 35,000 tweets. Our resulting dataset has roughly 15% positive harassment examples and 85% negative examples. This data is useful for training machine learning models, identifying textual and linguistic features of online harassment, and for studying the nature of harassing comments and the culture of trolling.
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