چکیده مقاله
Recent advancements in artificial intelligence AI and natural language processing NLP have revolutionized the identification of Twitter users by leveraging profile information and tweet content AI and NLP technologies facilitate user identification, behavior analysis, and threat detection by scrutinizing profiles—such as usernames, biographies, and profile images—as well as tweet content This paper explores the integration of profile and tweet data for user identification, focusing on the combination of profile information and tweet content to improve accuracy The study also reviews gender and age classification research, noting remarkable improvements from classical methods to advanced deep learning approaches The study classifies Twitter users' genders Using a multi modal methodology that includes both image based VGG16 and text based LaBSE models The combined model demonstrated superior performance compared to single modal approaches, showcasing the effectiveness of feature fusion in enhancing classification precision This multi modal method improves valuable improvements for cybersecurity, marketing, and social analysis by providing more reliable user identification in the context of social media
کلیدواژهها
نویسندگان
Sara Bourbour Hosseinbeigi
Department of Information Technology Engineering, Tarbiat Modares University, Tehran, Iran
Amin Saeidi Kelishami
Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
Omid Ahmadi
Department of Information Technology Engineering, Tarbiat Modares University, Tehran, Iran
شیوه ارجاع
Bourbour Hosseinbeigi, Sara and Saeidi Kelishami, Amin and Ahmadi, Omid,1403,A Multi-Modal Approach to Twitter User’s Gender Classification,The 10th International Conference on Industrial and Systems Engineering,Mashhad
ارائهشده در
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