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International Journal of Information and Communication Technology Research (IJICT، جلد ۱۶، شماره ۴، صفحات ۰-۰
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Persian Rumor Detection Using a Multi-Classifier Fusion Approach |
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| چکیده انگلیسی مقاله |
During the last few years, rumor and its rapid diffusion via social media have affected public opinions, even in some important such as presidential elections. One of the main approaches for rumor detection methods is based on content and natural language processing. Despite considerable improvement made in this regard in English language, unfortunately, we have not witnessed enough progress in Persian language, mainly due to a lack of datasets in this area. The main novelty of this paper is combining different learning methods to achieve a more performance outcome in comparison with each method. The methods we used in this study include three classifiers based on lingual-based features, word frequency-based, and word embedding-based features. The results show that combined different methods using the weighted majority fusion method, a significant improvement in the results is achieved. |
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| کلیدواژه‌های انگلیسی مقاله |
Rumor detection, Machine learning, Content-based text classification, Deep learning, Multi-classification |
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| نویسندگان مقاله |
| Alireza Mansouri
Faculty of Information Technology, ICT Research Institute
| Maryam Mahmoudi
Faculty of Information Technology, ICT Research Institute
| Mojgan Farhoodi
Faculty of Information Technology, ICT Research Institute
| Seyed Mohammadreza Mirsarraf
Faculty of Information Technology, ICT Research Institute
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| نشانی اینترنتی |
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-4466-2&slc_lang=en&sid=1 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
فناوری اطلاعات |
| نوع مقاله منتشر شده |
پژوهشی |
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