People increasingly shape their opinions by accessing and discussing content shared on social net... more People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users' shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the effects of the combination of social influence and mass media influence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confidence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agent...
Introduction to Monte Carlo algorithms cluster algorithms optimized Monte Carlo methods Monte Car... more Introduction to Monte Carlo algorithms cluster algorithms optimized Monte Carlo methods Monte Carlo on parallel and vector computers error estimates on averages of correlated data stochastic differential equations frustrated systems - ground state properties molecular dynamics.
Bots in online social networks can be used for good or bad but their presence is unavoidable and ... more Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patt...
We study the robustness of an evolving system that is driven by successive inclusions of new elem... more We study the robustness of an evolving system that is driven by successive inclusions of new elements or constituents with <i>m</i> random interactions to older ones. Each constitutive element in the model stays either active or is temporarily inactivated depending upon the influence of the other active elements. If the time spent by an element in the inactivated state reaches <i>T</i><sub><i>W</i></sub>, it gets extinct. The phase diagram of this dynamic model as a function of <i>m</i> and <i>T</i><sub><i>W</i></sub> is investigated by numerical and analytical methods and as a result both growing (robust) as well as non-growing (volatile) phases are identified. It is also found that larger time limit <i>T</i><sub><i>W</i></sub> enhances the system's robustness against the inclusion of new elements, mainly due to the system's increased ability to reject 'falling-together' type attacks. Our results suggest that the ability of an element to survive in an unfavourable situation for a while, either as a minority or in a dormant state, could improve the robustness of the entire system.
People increasingly shape their opinions by accessing and discussing content shared on social net... more People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users' shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the effects of the combination of social influence and mass media influence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confidence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agent...
Introduction to Monte Carlo algorithms cluster algorithms optimized Monte Carlo methods Monte Car... more Introduction to Monte Carlo algorithms cluster algorithms optimized Monte Carlo methods Monte Carlo on parallel and vector computers error estimates on averages of correlated data stochastic differential equations frustrated systems - ground state properties molecular dynamics.
Bots in online social networks can be used for good or bad but their presence is unavoidable and ... more Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patt...
We study the robustness of an evolving system that is driven by successive inclusions of new elem... more We study the robustness of an evolving system that is driven by successive inclusions of new elements or constituents with <i>m</i> random interactions to older ones. Each constitutive element in the model stays either active or is temporarily inactivated depending upon the influence of the other active elements. If the time spent by an element in the inactivated state reaches <i>T</i><sub><i>W</i></sub>, it gets extinct. The phase diagram of this dynamic model as a function of <i>m</i> and <i>T</i><sub><i>W</i></sub> is investigated by numerical and analytical methods and as a result both growing (robust) as well as non-growing (volatile) phases are identified. It is also found that larger time limit <i>T</i><sub><i>W</i></sub> enhances the system's robustness against the inclusion of new elements, mainly due to the system's increased ability to reject 'falling-together' type attacks. Our results suggest that the ability of an element to survive in an unfavourable situation for a while, either as a minority or in a dormant state, could improve the robustness of the entire system.
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Papers by Janos Kertesz