آمار
| تعداد نشریات | 36 |
| تعداد شماره‌ها | 1,192 |
| تعداد مقالات | 8,579 |
| تعداد مشاهده مقاله | 6,988,709 |
| تعداد دریافت فایل اصل مقاله | 4,032,557 |
تشخیص شایعه در شبکه های اجتماعی مبتنی بر تحلیل الگوی فراوانی درجه رئوس در زیرگراف های گام به گام انتشار | ||
| پدافند الکترونیکی و سایبری | ||
| دوره 10، شماره 3 - شماره پیاپی 39، دی 1401، صفحه 93-105 اصل مقاله (1.12 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| نویسندگان | ||
| مریم خسروی1؛ حسین شیرازی* 2؛ کوروش دادش‌تبار احمدی3؛ سید علیرضا هاشمی گلپایگانی4 | ||
| 1دانشجوی دکترا، مجتمع دانشگاهی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر، تهران، ایران | ||
| 2استاد، مجتمع دانشگاهی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر، تهران، ایران | ||
| 3استادیار، مجتمع دانشگاهی برق و کامپیوتر، دانشگاه صنعتی مالک اشتر، تهران، ایران | ||
| 4استادیار، دانشکده کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی امیرکبیر، تهران، ایران | ||
| تاریخ دریافت: 17 آذر 1400، تاریخ بازنگری: 02 اسفند 1400، تاریخ پذیرش: 18 مرداد 1401 | ||
| چکیده | ||
| با گسترش شبکه‌های اجتماعی و افزایش تعداد کاربران آن‌ها. چالش‌های جدیدی در این فضا ایجاد شده است. یکی از مهم‌ترین چالش‌ها انتشار شایعات و اطلاعات نادرست است که گسترش آن‌ها می‌تواند تأثیرات مخرب زیادی را بر جوامع انسانی بگذارد و گاهی عواقب جبران‌ناپذیری را نیز به بار آورد. به همین دلیل امروزه پژوهش‌های فراوانی به تشخیص شایعات در این شبکه‌ها می‌پردازند. در اکثر پژوهش‌هایی که از روش بررسی گراف انتشار برای تشخیص شایعات استفاده کرده‌اند، نیاز به درگیرشدن با پیچیدگی‌های پردازش زبان یا تحلیل ویژگی‌های کاربر است و به دلیل پیچیدگی تحلیل گراف‌های انتشار شایعات تا کنون از این روش به‌تنهایی برای تشخیص شایعه استفاده نشده و نیاز به استفاده از سایر ویژگی‌ها یا تحلیل متن بوده است. از این رو هدف از این مقاله این است که روش جدیدی ارائه شود که بدون نیاز به اطلاعات کاربر و تحلیل محتوای منتشر شده، و تنها باتوجه‌به زیرگراف انتشار پست، قادر به تشخیص شایعات باشد؛ بنابراین فراوانی درجه‌ی رئوس گراف‌های انتشار در مدل‌های شایعه و غیر شایعه مورد بررسی قرار گرفت و یک بردار ۸ تایی با توجه به این ویژگی زیرگراف‌های انتشار استخراج شد. سپس از دسته‌بندی‌کننده‌های مختلف به‌منظور تشخیص تمایز بین این دو حالت با توجه به بردار ۸ تایی استفاده شد. پس از ارزیابی، مشخص شد که دسته‌بندی‌کننده‌ی جنگل تصادفی بر روی مجموعه‌داده‌ی PHEME نتیجه‌ی بهتر و دقتی حدود ۸۴/۰ دارد. ازآنجایی‌که این روش نهایتاً در ۴ گام پس از انتشار قادر به تشخیص است، از لحاظ زمانی نیز کارایی مناسبی دارد. | ||
| کلیدواژه‌ها | ||
| شبکه های اجتماعی؛ شایعه؛ گراف انتشار؛ توزیع درجه | ||
| عنوان مقاله [English] | ||
| Rumor Detection on Social Networks Based on the Degree Distribution Analysis in Step-by-Step Propagation Subgraphs | ||
| نویسندگان [English] | ||
| Maryam Khosravi1؛ Hossein Shirazi2؛ Kourosh dadahtabar3؛ َAlireza Hashemi Gholpayghani4 | ||
| 1PhD student, Electrical and Computer University Complex, Malik Ashtar University of Technology, Tehran, Iran | ||
| 2Professor, Electrical and Computer University Complex, Malik Ashtar University of Technology, Tehran, Iran | ||
| 3Assistant Professor, Electrical and Computer University Complex, Malik Ashtar University of Technology, Tehran, Iran | ||
| 4Assistant Professor, Faculty of Computer and Information Technology, Amirkabir University of Technology, Tehran, Iran | ||
| چکیده [English] | ||
| With the expansion of social networks and the increase in their users, these networks have become an effective medium for publishing news and various content. Therefore, new challenges have been created in this space, one of the most important of which is spreading rumors and false information. Rumors are moving at an incredible rate in society due to their appeal and attraction. Their spread can have many destructive effects on human societies and sometimes have irreparable consequences. For this reason, many researchers today deal with rumors in these networks. The purpose of this article is to provide a new method that can detect rumors without user information and post content analysis, and only according to the post propagation subgraph. Therefore, the degree distribution of the propagation graphs in the rumored and non-rumored models is examined. Then different classifiers were used to distinguish between these two modes. The Random Forest classifier gives better results than others. Since this method can finally detect rumors within four steps after propagation, this method has a good performance in terms of time. | ||
| کلیدواژه‌ها [English] | ||
| rumor, propagation graph, degree distribution, social network | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 123 تعداد دریافت فایل اصل مقاله: 128 |
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