English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 62567/95223 (66%)
造訪人次 : 2519329      線上人數 : 284
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/121941

    題名: Profanity and hate speech detection
    作者: Teh, Phoey Lee;Cheng, Chi-Bin
    關鍵詞: Profanity;hate speech;tweets;bayes theorem;deep learning
    日期: 2020-09
    上傳時間: 2022-01-13 12:14:01 (UTC+8)
    摘要: Profanity, often found in today's online social media, has been used to detect online hate speech. The aims of this study were to investigate the profanity usage on Twitter by different groups of users, and to quantify the effectiveness of using profanity in detecting hate speech. Tweets from three English-speaking countries, Australia, Malaysia, and the United States, were collected for data analysis. Statistical hypothesis tests were performed to justify the difference of profanity usage among the three countries, and a probability estimation procedure was formulated based on Bayes theorem to quantify the effectiveness of profanity- based methods in hate speech detection. Three deep learning methods, long short-term memory (LSTM), bidirectional LSTM (BLSTM), and bidirectional encoder representations from transformers (BERT) are further used to evaluate the effect of profanity screening on building classification model. Our experimental results show that the effectiveness of using profanity in detecting hate speech is questionable. Nevertheless, the results also show that for Australia tweets, where profanity is more associated with hatred, profanity-based methods in hate speech detection could be effective and profanity screening can address the class imbalance issue in hate speech detection. This is evidenced by the performances of using deep learning methods on the profanity screened data of Australia data, which achieved a classification f1-score of 0.84.
    關聯: International Journal of Information and Management Sciences 31(3), p.227-246
    DOI: 10.6186%2fIJIMS.202009_31(3).0002
    顯示於類別:[資訊管理學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋