Results 171 to 180 of about 7,753,315 (190)

Boosting Bot Detection via Heterophily-Aware Representation Learning and Prototype-Guided Cluster Discovery

Knowledge Discovery and Data Mining
Detecting social media bots is essential for maintaining the security and trustworthiness of social networks. While contemporary graph-based detection methods demonstrate promising results, their practical application is limited by label reliance and ...
Buyun He, Xiaorui Jiang, Qi Wu, Hao Liu, Ying-Guang Yang, Yong Liao   +5 more
semanticscholar   +1 more source

Deep Learning Based Social Bot Detection on Twitter

IEEE Transactions on Information Forensics and Security, 2023
While social bots can be used for various good causes, they can also be utilized to manipulate people and spread malware. Therefore, it is crucial to detect bots running on social media platforms.
Efe Arin, Mucahid Kutlu
semanticscholar   +1 more source

CACL: Community-Aware Heterogeneous Graph Contrastive Learning for Social Media Bot Detection

Annual Meeting of the Association for Computational Linguistics
Social media bot detection is increasingly crucial with the rise of social media platforms. Existing methods predominantly construct social networks as graph and utilize graph neural networks (GNNs) for bot detection. However, most of these methods focus
Sirry Chen, Shuo Feng, Song-Song Liang, Chen-Chen Zong, Jing Li, Pi-Ji Li   +5 more
semanticscholar   +1 more source

BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers

International Joint Conference on Artificial Intelligence
Detecting social bots has evolved into a pivotal yet intricate task, aimed at combating the dissemination of misinformation and preserving the authenticity of online interactions. While earlier graph-based approaches, which leverage topological structure
Buyun He, Ying-Guang Yang, Qi Wu, Hao Liu, Ren-Yu Yang, Hao Peng, Xiang Wang, Yong Liao, P. Zhou   +8 more
semanticscholar   +1 more source

LGB: Language Model and Graph Neural Network-Driven Social Bot Detection

IEEE Transactions on Knowledge and Data Engineering
Malicious social bots achieve their malicious purposes by spreading misinformation and inciting social public opinion, seriously endangering social security, making their detection a critical concern.
Ming Zhou, Dan Zhang, Yuan-Dong Wang, Yangli-ao Geng, Yu-Xiao Dong, Jie Tang   +5 more
semanticscholar   +1 more source

BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning

arXiv.org
The detection of automated accounts, also known as"social bots", has been an increasingly important concern for online social networks (OSNs). While several methods have been proposed for detecting social bots, significant research gaps remain.
Mohammad Majid Akhtar, Navid Shadman Bhuiyan, R. Masood, Muhammad Ikram, S. Kanhere   +4 more
semanticscholar   +1 more source

Bot2Vec: A general approach of intra-community oriented representation learning for bot detection in different types of social networks

Information Systems, 2022
Loan T.T. Nguyen, Unil Yun
exaly  

Stabilizing a supervised bot detection algorithm: How much data is needed for consistent predictions?

Online Social Networks and Media, 2022
Lynnette Hui Xian Ng, Kathleen Carley, Dawn Robertson   +2 more
exaly  

DeBot: A deep learning-based model for bot detection in industrial internet-of-things

Computers and Electrical Engineering, 2022
Mauro Conti, Gulshan Kumar, Reji Thomas
exaly  

Multi-attributed heterogeneous graph convolutional network for bot detection

Information Sciences, 2020
Qiben Yan, Hao Peng, Minglai Shao
exaly