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Department: search.filters.department.Department of Computer EngineeringSubject: search.filters.subject.Social networks

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    A reputation-based privacy management system for social networking sites
    (TÜBİTAK, 2013) Yüksel, Mehmet Erkan; Yüksel, Asım Sinan; Department of Computer Engineering; Faculty Member; Department of Computer Engineering; College of Engineering; 8693
    Social networking sites form a special type of virtual community where we share our personal information with people and develop new relationships on the Internet. These sites allow the users to share just about everything, including photos, videos, favorite music, and games, and record all user interactions and retain them for potential use in social data mining. This storing and sharing of large amounts of information causes privacy problems for the users of these websites. In order to prevent these problems, we have to provide strict privacy policies, data protection mechanisms, and trusted and built-in applications that help to protect user privacy by limiting the people who get access to a user's personal information. Thus, the privacy problem has prompted us to provide a solution that o ers the users of these social networking websites an opportunity to protect their information. In this paper, a social networking application and its system design, algorithm, and database structure are described. Our application o ers a reputation-based trusted architecture to social network users. It creates and monitors social reputations, nds social circles, and helps the users to group their friends easily, meaningfully, and automatically to protect their privacy. This system provides the grouping of users through an automated system into di erent social circles by analyzing the user's social connections depending on what common information or application they share that should not be accessed by other users.
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    KUISAIL at SemEval-2020 Task 12: BERT-CNN for offensive speech identification in social media
    (International Committee for Computational Linguistics, 2020) Department of Computer Engineering; N/A; N/A; Yüret, Deniz; Safaya, Ali; Isentemiz, Moutasem; Faculty Member; PhD Student; Master Student; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 179996; N/A; N/A
    In this paper, we describe our approach to utilize pre-trained BERT models with Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language Identification shared task (OffensEval 2020), which is a part of the SemEval 2020. We show that combining CNN with BERT is better than using BERT on its own, and we emphasize the importance of utilizing pre-trained language models for downstream tasks. Our system, ranked 4th with macro averaged F1-Score of 0.897 in Arabic, 4th with score of 0.843 in Greek, and 3rd with score of 0.814 in Turkish. Additionally, we present ArabicBERT, a set of pre-trained transformer language models for Arabic that we share with the community.