ارائه رویکرد ترکیبی مبتنی بر یادگیری عمیق و یادگیری ماشین جهت تشخیص اخبار جعلی: مطالعه موردی اخبار فارسی در حوزه کرونا ویروس | ||
| علوم و فنون مدیریت اطلاعات | ||
| مقاله 11، دوره 8، شماره 3، مهر 1401، صفحه 283-316 اصل مقاله (2.91 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22091/stim.2021.7311.1640 | ||
| نویسندگان | ||
| وحید متقی1؛ مهدی اسماعیلی* 2؛ قاسمعلی بازایی3؛ محمدعلی افشارکاظمی4 | ||
| 1دانشجوی دکتری، گروه مدیریت فناوری اطلاعات، واحد قشم، دانشگاه آزاد اسلامی، قشم، ایران. | ||
| 2استادیار، گروه علوم کامپیوتر، واحد کاشان، دانشگاه آزاد اسلامی، کاشان، ایران | ||
| 3استادیار، گروه مدیریت، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران | ||
| 4دانشیار، گروه مدیریت، واحد تهران مرکز، دانشگاه آزاد اسلامی، تهران، ایران | ||
| چکیده | ||
| هدف: اطلاعات غلط یا تأیید نشده، دقیقاً مانند اطلاعات دقیق در وب منتشر میشوند. بنابراین، ممکن است ویروسی شوند و بر افکار عمومی و تصمیمات آن تأثیر بگذارند. اخبار جعلی و شایعات به ترتیب محبوبترین اشکال اطلاعات دروغ و تأیید نشده را نشان میدهند و برای جلوگیری از تأثیرات چشمگیر آنها باید در اسرع وقت کشف شوند. علاقه به تکنیکهای مؤثر در شناسایی، در سالهای اخیر بسیار سریع در حال افزایش است. مسئله تشخیص اخبار جعلی به عنوان یک مسئله طبقهبندی در پردازش زبان طبیعی و متنکاوی شناخته میشود و هدف آن تفکیک و تشخیص اخبار جعل از واقعی، در متنهای استخراج شده و بهبود در دقت تشخیص اخبار جعلی است. شبکههای عصبی کانولوشن به عنوان یکی از مهمترین مدلهای یادگیری عمیق دقت بالایی را بر روی این مسائل بدست آوردهاند. این شبکهها شامل مشکلاتی مثل عدم در نظر گرفتن موقعیت کلمات میباشند که مسأله مذکور با استفاده از شبکه کپسول برطرف گردیده و جهت حل مشکل پردازش سنگین لایههای تمام متصل و فضای پارامتریک الگوریتمهای XGBOOST و بهینهسازی ازدحام انبوه ذرات (PSO) برای دستیابی به دقت و صحّت بهینه پیشنهاد شده است. روش: مطالعه حاضر پژوهشی کاربردی بوده که در آن حدود 42000 اخبار فارسی از شهرهای مختلف ایران از توییتر جمعآوری شده و با استفاده از روشهای پاکسازی و پیشپردازش، اطلاعات اضافی حذف و پس از برچسب زدن، اخبار آماده بهکارگیری جهت رویکرد پیشنهادی با استفاده از نرمافزار پایتون و کتابخانههای مربوطه با الگوریتمهای یادگیری ماشین و یادگیری عمیق شد. یافتهها: طی بررسی، آزمایش و تست، برخی از الگوریتمهای یادگیری ماشین دارای قدرت بیشتری در مسائل طبقهبندی بودند، ولی با تغییرات و اعمال روشهای پیشنهادی که در ساختار الگوریتم شبکه کانولوشن و شبکه کپسول صورت گرفت، نتایج بهینه نسبت به الگوریتمهای یادگیری ماشین و سایر الگوریتمهای پایه و الگوریتمهای مورد ارزیابی بدست آمد. نتیجهگیری: راهکارهای پیشنهادی در این تحقیق در مقایسه با رویکردهای الگوریتمهای پایه و یا راهکارهای صورت گرفته جهت حل مشکلات مذکور بدون اضافه کردن سربار اضافی از لحاظ تعداد ویژگیها و عمق شبکه، با تغییر در ورودی توانسته است به نتایج بهتر و قابل قبول از سایر رویکردهای موجود در ادبیات دست یافته و به دقت و صحّت حدود 96 درصد دست یابد. | ||
| کلیدواژهها | ||
| پردازش زبان طبیعی؛ طبقهبندی متن؛ شبکههای عصبی کپسول؛ تشخیص اخبار جعل؛ کرونا ویروس؛ یادگیری عمیق؛ یادگیری ماشین؛ اخبار فارسی | ||
| عنوان مقاله [English] | ||
| Providing a Hybrid Approach Based on Deep learning and Machine Learning to Detect Fake News - A Case Study of Persian News in the Field of COVID-19 | ||
| نویسندگان [English] | ||
| Vahid Mottaghi1؛ Mahdi Esmaeili2؛ Ghasem Ali Bazaee3؛ Mohammad Ali Afshar Kazemi4 | ||
| 1PhD. Candidate in IT Management, Department of IT Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran. | ||
| 2Assistant Professor, Department of Computer Science, Kashan Branch, Islamic Azad University, Kashan, Iran | ||
| 3Assistant Professor, Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| 4Associate Professor, Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده [English] | ||
| Objectives: False or unconfirmed information is published on the web like accurate information, so it can become viral and influence public opinion and decisions. Fake news and gossip show the most popular forms of false and unverified information, respectively, and they should be detected as soon as possible to avoid significant effects Interest in effective identification techniques has been increasing in recent years.The problem of detecting fake news is known as a classification problem in natural language processing and text mining, and its purpose is to distinguish fake news from real and extracted texts, and to improve the accuracy of detecting fake news is the main issue of this research. Convolutional neural networks, as one of the most important models of deep learning, have gained high accuracy on these issues. These networks include problems such as not considering the position of words, which is solved by using the capsule network, and in order to achieve optimal accuracy, two problems of heavy processing of all connected layers and reducing the parametric space using the algorithm XGBOOST and particle swarm optimization (PSO) algorithm are proposed. Methods: This study is an applied research in which about 42,000 Persian news from different cities of Iran were collected from Twitter and using additional methods of cleaning and preprocessing, additional information was removed and after tagging, the news was ready to be used for the proposed approach using Python software and related libraries are equipped with machine learning and deep learning algorithms. Results: During testing, some machine learning algorithms had more power in classification problems, but with the changes in the structure of the convolutional network and Capsul network algorithm, better results were obtained than machine learning algorithms and other similar algorithms. Conclusions: The proposed solutions in this research in comparison with the approaches of basic algorithms or solutions to solve the mentioned problems by replacing the optimal classifier and reducing the parametric space, by changing the input has been able to achieve better and more acceptable results than other approaches. And achieve an accuracy of about 96%. | ||
| کلیدواژهها [English] | ||
| Natural Language Processing, Text Classification, Capsule Neural Networks, Fake News Detection, Corona Virus Fake News | ||
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