閱讀全文 | |
篇名 |
Cyber-Bullying and Cyber-Harassment Detection Using Supervised Machine Learning Techniques in Arabic Social Media Contents
|
---|---|
並列篇名 | Cyber-Bullying and Cyber-Harassment Detection Using Supervised Machine Learning Techniques in Arabic Social Media Contents |
作者 | Tarek Kanan、Amal Aldaaja、Bilal Hawashin |
英文摘要 | The social media has provided users with the chance to publish their written and multimedia content and express feelings and emotions about particular subjects via the internet. However, some users have abused these platforms by performing various acts such as Cyber- Bullying and Cyber-Harassment. These phenomena are dangerous and have negative psychological, health, and social effects. Although multiple works have focused on detecting these phenomena on English text, few works studied this phenomenon on Arabic. Moreover, these works used limited number of methods and datasets. Furthermore, there is a lack in Arabic datasets that are concerned with this topic. We propose the use of Machine Learning to detect such negative written acts. We apply various classification algorithms to the dataset, and we use various Arabic Natural Language Processing (NLP) tools. To evaluate the performance of the classifiers, we use Recall, Precision, and F1-Measure. The results show that the Random Forest algorithm yields the highest values of F1-Measure. The same results occurred when no stemming and no stop-word removal are applied. However, when separating datasets into Facebook Posts dataset and Twitter Tweets dataset, SVM gives the highest F1-Measure value. Significant tests were conducted to support our results. |
起訖頁 | 1409-1421 |
關鍵詞 | Social media content、Cyber-Bullying、Cyber-Harrasement、Machine learning、Natural language processing |
刊名 | 網際網路技術學刊 |
期數 | 202009 (21:5期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
|
QR Code | |
該期刊 上一篇
| Content Enrichment Using Linked Open Data for News Classification |
該期刊 下一篇
| A New RSS Distance Calculation Algorithm Based on Tree-ring Distance in APs Rich Indoor Localization Environments |