教師資料查詢 | 類別: 期刊論文 | 教師: 鄭啟斌CHI-BIN CHENG (瀏覽個人網頁)

標題:Profanity and hate speech detection
學年109
學期2
出版(發表)日期2021/07/01
作品名稱Profanity and hate speech detection
作品名稱(其他語言)
著者Phoey Lee Teh; Chi-Bin Cheng
單位
出版者
著錄名稱、卷期、頁數International Journal of Information and Management Sciences 31(3), p.227-246
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.
關鍵字Profanity;hate speech;tweets;bayes theorem;deep learning
語言英文
ISSN1017-1819
期刊性質國外
收錄於EI;Scopus;
產學合作
通訊作者Chi-Bin Cheng
審稿制度
國別中華民國
公開徵稿
出版型式,電子版
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