چکیده مقاله
This study aimed to review the impact of deep learning DL techniques on rumor detection in social media platforms, focusing on the distinctive features and user interactions on Twitter and Sina Weibo We have endeavored to compare the outcomes obtained from Recurrent Neural Networks RNN , Convolutional Neural Networks CNN , and Graph Neural Networks GNN Beyond a cursory review of existing methods, we briefly investigate the structure of two approaches, Graph Robot Aware SBAG and Graph Convolutional Rumor Detection System GCRES , both of which employ the Graph Neural Networks GNN method These two approaches are significant because, in addition to examining the content of rumors, they pay attention to the pattern of their spread through Graph Neural Networks GNN for rumor detection These advancements underscore the potential of DL and GNN in addressing the challenge of rumor detection in social media and emphasize the importance of continuing innovation in this rapidly evolving field
کلیدواژهها
نویسندگان
Seyed AliReza Moeini
Department of Computer Engineering, Persian Gulf University, Bushehr, Iran
Ebrahim Sahafizadeh
Department of Computer Engineering, Persian Gulf University, Bushehr, Iran
شیوه ارجاع
Moeini, Seyed AliReza and Sahafizadeh, Ebrahim,1403,Advanced Innovations in Social Media Rumor Detection: Integrating Graph Neural Networks and Deep Learning - A Review,1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر