شناسایی شایعات در شبکه‌های اجتماعی در زمینه بیماری‌های همه‌گیر با استفاده از تکنیک‌های یادگیری عمیق

نوع مقاله : مقاله پژوهشی فارسی

نویسندگان

1 دانشکده مهندسی برق و کامپیوتر، دانشگاه کاشان، کاشان، ایران.

2 دانشکده مهندسی برق و کامپیوتر، واحد ایلام، دانشگاه آزاد اسلامی، ایلام، ایران.

3 دانشکده مهندسی کامپیوتر، واحد آبادان، دانشگاه آزاد اسلامی، آبادان، ایران.

چکیده

یکی از مهمترین مسائل در شبکههای اجتماعی حجم بالای شایعاتی است که توسط عوامل انسانی و یا ماشینی منتشر می‌شوند. در ‏چنین شرایطی، تشخیص خودکار شایعات برای ایمن نگهداشتن افکار عمومی در برابر خطرات بالقوه آنها؛ از اهمیت بالایی برخوردار ‏است. در این پژوهش، با استفاده از تکنیکهای یادگیری عمیق یک راهکار جدید برای تشخیص خودکار شایعاتِ مرتبط با بیماری‏های همهگیر در شبکههای اجتماعی ارائه شده است. در روش پیشنهادی، ابتدا محتوای پیامهای موجود برای پردازش در گامهای ‏بعدی آمادهسازی میشوند. همچنین از قالب ماتریس وزنی برای توصیف خصوصیات محتوایی استفاده شده است. سپس در گام دوم ‏روش پیشنهادی، از شبکه عصبی کانولوشن به منظور استخراج مجموعه ویژگیهای مناسب از ماتریس خصوصیات حاصل از گام قبل ‏استفاده میشود. بدین ترتیب، ماتریس خصوصیات محتوایی به عنوان ورودی شبکه عصبی عمیق بکار میرود و مقادیر وزنی به ‏دست آمده در آخرین لایه تماماً متصل این شبکه عصبی به عنوان ویژگیهای استخراج شده از آن مورد استفاده قرار میگیرد. ‏درنهایت، از تجمیع چند طبقهبند دودویی به منظور تشخیص شایعات و طبقهبندی ویژگیهای استخراج شده از طریق شبکه عصبی ‏کانولوشن استفاده میشود. بدین منظور، ویژگیهای استخراج شده به‌صورت همزمان توسط چندین مدل یادگیری پردازش شده و ‏خروجی نهایی سیستم پیشنهادی از طریق رایگیری خروجیهای این سه الگوریتم تعیین میشود. نتایج حاصل از این تحقیق نشان ‏میدهد که با استفاده از روش پیشنهادی میتوان شایعات را با میانگین دقت 98.8 درصد تشخیص داد که نشان از بهبود حداقل ‏‏2.4 درصدی دقت تشخیص نسبت به روشهای پیشین دارد.‏

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