Muhammad Umer, Arwa A. Jamjoom, Shtwai Alsubai, Aisha Ahmed AlArfaj, E. Alabdulqader, I. Ashraf
{"title":"阿拉伯语假新闻检测的预测建模:利用语言模型嵌入和堆叠集合","authors":"Muhammad Umer, Arwa A. Jamjoom, Shtwai Alsubai, Aisha Ahmed AlArfaj, E. Alabdulqader, I. Ashraf","doi":"10.1145/3677016","DOIUrl":null,"url":null,"abstract":"The proliferation of fake news poses a substantial threat to information integrity, prompting the need for robust detection mechanisms. This study advances the research on Arabic fake news detection and overcomes the limitation of lower accuracy for fake news detection. This research addresses Arabic fake news detection using word embedding and a powerful stacking classifier. The proposed model combines bagging, boosting, and baseline classifiers, harnessing the strengths of each to create a robust ensemble. Extensive experiments are carried out to evaluate the proposed approach indicating remarkable results, with recall, F1 score, accuracy, and precision reaching 99%. The utilization of advanced stacking techniques, coupled with appropriate textual feature extraction, empowers the model to effectively detect Arabic fake news. Study results make a valuable contribution to fake news detection, particularly in the Arabic context, providing a valuable tool for enhancing information veracity and fostering a more informed public discourse. Furthermore, the proposed model’s accuracy is compared with other cutting-edge models from the existing literature to showcase its superior performance.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" December","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive Modeling for Arabic Fake News Detection: Leveraging Language Model Embeddings and Stacked Ensemble\",\"authors\":\"Muhammad Umer, Arwa A. Jamjoom, Shtwai Alsubai, Aisha Ahmed AlArfaj, E. Alabdulqader, I. Ashraf\",\"doi\":\"10.1145/3677016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The proliferation of fake news poses a substantial threat to information integrity, prompting the need for robust detection mechanisms. This study advances the research on Arabic fake news detection and overcomes the limitation of lower accuracy for fake news detection. This research addresses Arabic fake news detection using word embedding and a powerful stacking classifier. The proposed model combines bagging, boosting, and baseline classifiers, harnessing the strengths of each to create a robust ensemble. Extensive experiments are carried out to evaluate the proposed approach indicating remarkable results, with recall, F1 score, accuracy, and precision reaching 99%. The utilization of advanced stacking techniques, coupled with appropriate textual feature extraction, empowers the model to effectively detect Arabic fake news. Study results make a valuable contribution to fake news detection, particularly in the Arabic context, providing a valuable tool for enhancing information veracity and fostering a more informed public discourse. Furthermore, the proposed model’s accuracy is compared with other cutting-edge models from the existing literature to showcase its superior performance.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":\" December\",\"pages\":\"\"},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-07-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3677016\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3677016","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Predictive Modeling for Arabic Fake News Detection: Leveraging Language Model Embeddings and Stacked Ensemble
The proliferation of fake news poses a substantial threat to information integrity, prompting the need for robust detection mechanisms. This study advances the research on Arabic fake news detection and overcomes the limitation of lower accuracy for fake news detection. This research addresses Arabic fake news detection using word embedding and a powerful stacking classifier. The proposed model combines bagging, boosting, and baseline classifiers, harnessing the strengths of each to create a robust ensemble. Extensive experiments are carried out to evaluate the proposed approach indicating remarkable results, with recall, F1 score, accuracy, and precision reaching 99%. The utilization of advanced stacking techniques, coupled with appropriate textual feature extraction, empowers the model to effectively detect Arabic fake news. Study results make a valuable contribution to fake news detection, particularly in the Arabic context, providing a valuable tool for enhancing information veracity and fostering a more informed public discourse. Furthermore, the proposed model’s accuracy is compared with other cutting-edge models from the existing literature to showcase its superior performance.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.