تشخیص قلدری سایبری در شبکه های اجتماعی با یادگیری عمیق مبتنی بر شبکه عصبی CNN و LSTM
الموضوعات : فناوری های نوین در سیستم های توزیع شده و محاسبات الگوریتمی
محسن اقبالی
1
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کمال میرزائی
2
,
رضا عزیزی
3
1 - دانشجوی دکتری مهندسی کامپیوتر، گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
2 - استادیار گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
3 - استادیار گروه کامپیوتر، گروه مهندسی کامپیوتر، واحد میبد، دانشگاه آزاد اسلامی، میبد، ایران
الکلمات المفتاحية: شبکه اجتماعی, قلدری سایبری, یادگیری عمیق, شبکه عصبی کانولوشن, شبکه عصبی LSTM,
ملخص المقالة :
یکی از رویکردهای امیدوارکننده در تشخیص زورگویی سایبری استفاده از الگوریتم‌های یادگیری ماشین و یادگیری عمیق است. با این حال، تشخیص آزار سایبری در شبکه های اجتماعی پیچیده است و یک الگوریتم یادگیری ماشین و یادگیری عمیق به تنهایی توانایی زیادی برای تشخیص دقیق زورگویی سایبری ندارند. در این مقاله برای تشخیص زورگویی سایبری در ابتدا با سه روش استخراج ویژگیGloVe ، Word2Vec و TF-IDF ویژگی های اولیه متن استخراج می شود. در مرحله دوم انتخاب ویژگی با استفاده از الگوریتم JSO انجام می شود و در نهایت ویژگی های مهم به عنوان ورودی روش 1DCNN و LSTM در نظر گرفته می شود. آزمایشات در مجموعه داده توئیتر و فیس بوک برای تشخیص زورگویی سایبری انجام می شود. آزمایشات نشان می دهد دقت، حساسیت و صحت روش پیشنهادی در تشخیص زورگویی سایبری در مجموعه داده توئیتر به ترتیب برابر 23/98 درصد، 86/97 درصد و 73/97 درصد است. نتایج نشان می دهد روش پیشنهادی نسبت به روشهای CNN، LSTM و BERT در تشخیص زورگویی سایبری دارای دقت بیشتری است.
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