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АКТУАЛЬНЫЕ ТЕНДЕНЦИИ ЦИФРОВИЗАЦИИ ИНОЯЗЫЧНОГО ОБУЧЕНИЯ В НЕЯЗЫКОВОМ ВУЗЕ ... : CURRENT TRENDS IN DIGITALIZATION OF FOREIGN LANGUAGE EDUCATION IN A NON-LINGUISTIC UNIVERSITY ...
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Identifying Learners’ Interaction Patterns in an Online Learning Community
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 4; Pages: 2245 (2022)
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Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 9; Pages: 5705 (2022)
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Emotional and Social Engagement in the English Language Classroom for Higher Education Students in the COVID-19 Online Context
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In: Sustainability; Volume 14; Issue 8; Pages: 4527 (2022)
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Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain–Computer Interface
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In: Sensors; Volume 22; Issue 2; Pages: 535 (2022)
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A Combined Text-Based and Metadata-Based Deep-Learning Framework for the Detection of Spam Accounts on the Social Media Platform Twitter
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In: Processes; Volume 10; Issue 3; Pages: 439 (2022)
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Abstract:
Social networks have become an integral part of our daily lives. With their rapid growth, our communication using these networks has only increased as well. Twitter is one of the most popular networks in the Middle East. Similar to other social media platforms, Twitter is vulnerable to spam accounts spreading malicious content. Arab countries are among the most targeted, possibly due to the lack of effective technologies that support the Arabic language. In addition, as a complex language, Arabic has extensive grammar rules and many dialects that present challenges when extracting text data. Innovative methods to combat spam on Twitter have been the subject of many current studies. This paper addressed the issue of detecting spam accounts in Arabic on Twitter by collecting an Arabic dataset that would be suitable for spam detection. The dataset contained data from premium features by using Twitter premium API. Data labeling was conducted by flagging suspended accounts. A combined framework was proposed based on deep-learning methods with several advantages, including more accurate, faster results while demanding less computational resources. Two types of data were used, text-based data with a convolution neural networks (CNN) model and metadata with a simple neural networks model. The output of the two models combined identified accounts as spam or not spam. The results showed that the proposed framework achieved an accuracy of 94.27% with our combined model using premium feature data, and it outperformed the best models tested thus far in the literature.
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Keyword:
Arabic spam account; deep convolution neural networks; deep learning; online social network; spam detection
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URL: https://doi.org/10.3390/pr10030439
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University Students’ Classroom Emotional Climate and Attitudes during and after COVID-19 Lockdown
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In: Education Sciences; Volume 12; Issue 1; Pages: 31 (2022)
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An Improved Method for Online Teacher Training Courses for Bilingual Education Based on Cyber-Physical Systems
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In: Applied Sciences; Volume 12; Issue 5; Pages: 2346 (2022)
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English is Not Dead! Long Live English: Teaching the Evolution of English and Inclusive Communication Via Online, Face to Face or Hybrid Instruction
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In: Georgia International Conference on Information Literacy (2022)
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Difficulties and Challenges of Library and Information Science Students in Thesis Writing During the Pandemic
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In: Puissant ; 3 ; 353-365 (2022)
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ACADEMIC COACHES AND STUDENT SUCCESS IN HIGHER EDUCATION: A QUANTITATIVE STUDY
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In: School of Education Doctoral Projects (2022)
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How do L2 student moderators facilitate a peer-led discussion forum?
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Satisfacción de los estudiantes con la docencia online en tiempos de COVID-19
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In: Comunicar: Revista científica iberoamericana de comunicación y educación, ISSN 1134-3478, Nº 70, 2022 (Ejemplar dedicado a: Nuevos retos del profesorado ante la enseñanza digital), pags. 35-45 (2022)
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Teachers' perceptions of challenges in online learning: Voices from secondary EFL teachers
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In: Journal of Applied Linguistics and Literature, Vol 7, Iss 1, Pp 104-119 (2022) (2022)
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“Going down the Tower of Babel” through telecollaboration in the context of Internationalization at Home
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In: Domínios de Lingu@gem, Vol 16, Iss 1, Pp 7-40 (2022) (2022)
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Student Centered Language Teaching: A Focus on Student Identity
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In: All Graduate Plan B and other Reports (2022)
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Appraising Students’ Attitude towards Online Learning During Covid-19 Pandemic
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In: PAROLE: Journal of Linguistics and Education; Vol 12, No 1 (2022): Volume 12 Number 1 April 2022; 15-24 ; 23380683 ; 2087-345X (2022)
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The Learning of DaF Subject during the Covid-19 Pandemic: The Perspective of the Teachers
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In: PAROLE: Journal of Linguistics and Education; Vol 12, No 1 (2022): Volume 12 Number 1 April 2022; 1-14 ; 23380683 ; 2087-345X (2022)
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Scientific Approach-Based English Learning Strategy in Online Learning: Learners’ Satisfaction and Obstacles
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In: PAROLE: Journal of Linguistics and Education; Vol 12, No 1 (2022): Volume 12 Number 1 April 2022; 81-90 ; 23380683 ; 2087-345X (2022)
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