با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت مقاله خود را دریافت کنید (تا مشکل رفع گردد). با تشکر از صبوری شما!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
A real-time deep-learning approach for filtering Arabic low-quality content and accounts on Twitter
یک رویکرد یادگیری عمیق در زمان واقعی برای فیلتر کردن عربی با کیفیت پایین محتوا و حساب های کاربری در توییتر-2021
Social networks have generated immense amounts of data that have been successfully utilized for research and business purposes. The approachability and immediacy of social media have also allowed ill-intentioned users to perform several harmful activities that include spamming, promoting, and phishing. These activities generate massive amounts of low-quality content that often exhibits duplicate, automated, inappropriate, or irrelevant content that subsequently affects users’ satisfaction and imposes a significant challenge for other social media-based systems. Several real-time systems were developed to tackle this problem by focusing on filtering a specific kind of low-quality content. In this paper, we present a fine-grained real-time classification approach to identify several types of lowquality tweets (i.e., phishing, promoting, and spam tweets) written in Arabic. The system automatically extracts textual features using deep learning techniques without relying on hand-crafted features that are often time-consuming to be obtained and are tailored for a single type of low-quality content. This paper also proposes a lightweight model that utilizes a subset of the textual features to identify spamming Twitter accounts in a real-time setting. The proposed methods are evaluated on a real-world dataset (40, 000 tweets and 1, 000 accounts), showing superior performance in both models with accuracy and F1-scores of 0.98. The proposed system classifies a tweet in less than five milliseconds and an account in less than a second.
keywords: محتوای کم کیفیت در شبکه های اجتماعی | حساب های اسپم | سیستم تشخیص زمان واقعی | تکنیک های یادگیری عمیق | Low-quality content in social networks | Spam accounts | Real-time detection system | Deep learning techniques
Malicious accounts: Dark of the social networks
حساب های مخرب: تاریکی شبکه های اجتماعی-2017
Over the last few years, online social networks (OSNs), such as Facebook, Twitter and Tuenti, have experienced exponential growth in both profile registrations and social interactions. These networks allow people to share different information ranging from news, photos, videos, feelings, personal information or research activities. The rapid growth of OSNs has triggered a dramatic rise in malicious activities including spamming, fake accounts creation, phishing, and malware distribution. However, developing an efficient detection system that can identify malicious accounts, as well as their suspicious behaviors on the social networks, has been quite challenging. Researchers have proposed a number of features and methods to detect malicious accounts. This paper presents a comprehensive review of related studies that deal with detection of malicious accounts on social networking sites. The review focuses on four main categories, which include detection of spam accounts, fake accounts, compromised accounts, and phishing. To group the studies, the taxonomy of the different features and methods used in the literature to identify malicious accounts and their behaviors are proposed. The review considered only social networking sites and excluded studies such as email spam detection. The significance of proposed features and methods, as well as their limitations, are analyzed. Key issues and challenges that require substantial research efforts are discussed. In conclusion, the paper identifies the important future research areas with the aim of advancing the development of scalable malicious accounts detection system in OSNs.
Keywords: Online social network | Social spam | Malicious behavior | Fake account | Phishing detection | Sybil