{"title":"基于Logistic回归模型的乌尔都语假新闻自动识别","authors":"Rana Salahuddin, Muhammad Wasim","doi":"10.1109/ICOSST57195.2022.10016840","DOIUrl":null,"url":null,"abstract":"Social media offers a platform to disseminate information with family and friends quickly. The spread of fake news on social media has a significant social and economic impact. With the ever-increasing amount of social media data, it is challenging to quickly differentiate between real and fake news. In previous years, the research community focused on Fake news classification for the English language. However, many resource-poor languages, such as Urdu, still require efficient methods to classify and contain fake news. This study proposes a methodology to identify Urdu fake news based on machine learning techniques. Our proposed methodology uses the TF-IDF feature extraction technique and Logistic regression classifier to classify Urdu fake news automatically. The proposed approach outperforms the baseline with a 72%f1 score.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Automatic identification of Urdu fake news using Logistic Regression Model\",\"authors\":\"Rana Salahuddin, Muhammad Wasim\",\"doi\":\"10.1109/ICOSST57195.2022.10016840\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social media offers a platform to disseminate information with family and friends quickly. The spread of fake news on social media has a significant social and economic impact. With the ever-increasing amount of social media data, it is challenging to quickly differentiate between real and fake news. In previous years, the research community focused on Fake news classification for the English language. However, many resource-poor languages, such as Urdu, still require efficient methods to classify and contain fake news. This study proposes a methodology to identify Urdu fake news based on machine learning techniques. Our proposed methodology uses the TF-IDF feature extraction technique and Logistic regression classifier to classify Urdu fake news automatically. The proposed approach outperforms the baseline with a 72%f1 score.\",\"PeriodicalId\":238082,\"journal\":{\"name\":\"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSST57195.2022.10016840\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSST57195.2022.10016840","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic identification of Urdu fake news using Logistic Regression Model
Social media offers a platform to disseminate information with family and friends quickly. The spread of fake news on social media has a significant social and economic impact. With the ever-increasing amount of social media data, it is challenging to quickly differentiate between real and fake news. In previous years, the research community focused on Fake news classification for the English language. However, many resource-poor languages, such as Urdu, still require efficient methods to classify and contain fake news. This study proposes a methodology to identify Urdu fake news based on machine learning techniques. Our proposed methodology uses the TF-IDF feature extraction technique and Logistic regression classifier to classify Urdu fake news automatically. The proposed approach outperforms the baseline with a 72%f1 score.