Opinion dynamics models based on the multi-agent method commonly assume that interactions between individuals in a social network result in changes in their opinions.However,formation of public opinion in a social net...Opinion dynamics models based on the multi-agent method commonly assume that interactions between individuals in a social network result in changes in their opinions.However,formation of public opinion in a social network is a macroscopic statistical result of opinions of all expressive individuals(corresponding to silent individuals).Therefore,public opinion can be manipulated not only by changing individuals'opinions,but also by changing their states of expression(or silence)which can be interpreted as the phenomenon"spiral of silence"in social psychology.Based on this theory,we establish a"dual opinion climate"model,involving social bots and mass media through a multi-agent method,to describe mechanism for manipulation of public opinion in social networks.We find that both social bots(as local variables)and mass media(as a global variable)can interfere with the formation of public opinion,cause a significant superposition effect when they act in the same direction,and inhibit each other when they act in opposite directions.展开更多
Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhib...Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.展开更多
The rise of online social platforms has enhanced connectivity and access to information.Still,it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order.In ...The rise of online social platforms has enhanced connectivity and access to information.Still,it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order.In this paper,we introduce a unified framework for defining and classifying malicious social bots along three dimensions:behavior,interaction,and operation.We then present a comprehensive review of social bot detection methods,tracing their evolution from traditional machine learning techniques to deep learning architectures and graph neural networks,with particular emphasis on recent advances in group-level detection.We also explore the emerging paradigm of Large Language Model(LLM)based bot detection.This paper reviews the current state of research,identifies key challenges,and outlines future directions.It provides a cohesive foundation for building more robust detection frameworks to counter the evolving threats posed by malicious social bots.展开更多
基金by the National Natural Science Foundation of China(Grant Nos.61976120 and 62006128)the Humanities and Social Science Fund of Ministry of Education of China(Grant No.21YJCZH013).
文摘Opinion dynamics models based on the multi-agent method commonly assume that interactions between individuals in a social network result in changes in their opinions.However,formation of public opinion in a social network is a macroscopic statistical result of opinions of all expressive individuals(corresponding to silent individuals).Therefore,public opinion can be manipulated not only by changing individuals'opinions,but also by changing their states of expression(or silence)which can be interpreted as the phenomenon"spiral of silence"in social psychology.Based on this theory,we establish a"dual opinion climate"model,involving social bots and mass media through a multi-agent method,to describe mechanism for manipulation of public opinion in social networks.We find that both social bots(as local variables)and mass media(as a global variable)can interfere with the formation of public opinion,cause a significant superposition effect when they act in the same direction,and inhibit each other when they act in opposite directions.
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62476045)the LiaoNing Revitalization Talents Program(Grant No.XLYC1807106)the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Liaoning(Grant No.LR2016070).
文摘Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.
基金supported by the National Natural Science Foundation of China(No.62302213)Key Laboratory of Social Computing and Cognitive Intelligence(Dalian University of Technology),Ministry of Education,China.
文摘The rise of online social platforms has enhanced connectivity and access to information.Still,it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order.In this paper,we introduce a unified framework for defining and classifying malicious social bots along three dimensions:behavior,interaction,and operation.We then present a comprehensive review of social bot detection methods,tracing their evolution from traditional machine learning techniques to deep learning architectures and graph neural networks,with particular emphasis on recent advances in group-level detection.We also explore the emerging paradigm of Large Language Model(LLM)based bot detection.This paper reviews the current state of research,identifies key challenges,and outlines future directions.It provides a cohesive foundation for building more robust detection frameworks to counter the evolving threats posed by malicious social bots.
