175,471 research outputs found
Echo chamber effects based on a novel three-dimensional Deffuant-Weisbuch model
In order to solve the problem of opinion polarization and distortion caused
by echo chamber effect in the evolution process of online public opinion,a
three-dimensional Deffuant-Weisbuch model is proposed to study the formation
and elimination of echo chamber effect in this paper. Firstly, the original
pairwise interaction model is generalized to three-point interaction model.
Secondly, we consider individual psychological mechanism and introduce
individual emotional factor into the trust threshold of original model.
Finally, the natural evolution coefficient of opinion is introduced to modify
the model. The improved model is used to conduct simulation experiments on
social networks with different structures, and opinion leaders and active
agents are introduced into the network, so as to study the corresponding
generation and breaking mechanism of echo chamber. The experimental results
show that the change of network structure cannot eliminate the echo chamber
effect, and the increase of network stability and connectivity can only slow
down the echo chamber effect. Opinion leaders can aggregate opinions within
their scope of influence and have a guiding effect on opinions. Therefore, if
opinion leaders can change their opinions over time, they can well guide
opinions to converge to neutral opinions, thus achieving the purpose of
breaking the echo chamber. Active agents can lead the opinions in the network
to converge to the neutral, and active agents with high stubbornness can lead
the free views to converge to the neutral, thus achieving the purpose of
breaking the echo chamber effect.Comment: 34pages 57figure
The strength of weak bots
Some fear that social bots, automated accounts on online social networks, propagate falsehoods that can harm public opinion formation and democratic decision-making. Empirical research, however, resulted in puzzling findings. On the one hand, the content emitted by bots tends to spread very quickly in the networks. On the other hand, it turned out that bots’ ability to contact human users tends to be very limited. Here we analyze an agent-based model of social influence in networks explaining this inconsistency. We show that bots may be successful in spreading falsehoods not despite their limited direct impact on human users, but because of this limitation. Our model suggests that bots with limited direct impact on humans may be more and not less effective in spreading their views in the social network, because their direct contacts keep exerting influence on users that the bot does not reach directly. Highly active and well-connected bots, in contrast, may have a strong impact on their direct contacts, but these contacts grow too dissimilar from their network neighbors to further spread the bot\u27s content. To demonstrate this effect, we included bots in Axelrod\u27s seminal model of the dissemination of cultures and conducted simulation experiments demonstrating the strength of weak bots. A series of sensitivity analyses show that the finding is robust, in particular when the model is tailored to the context of online social networks. We discuss implications for future empirical research and developers of approaches to detect bots and misinformatio
The strength of weak bots
Some fear that social bots, automated accounts on online social networks, propagate falsehoods that can harm public opinion formation and democratic decision-making. Empirical research, however, resulted in puzzling findings. On the one hand, the content emitted by bots tends to spread very quickly in the networks. On the other hand, it turned out that bots’ ability to contact human users tends to be very limited. Here we analyze an agent-based model of social influence in networks explaining this inconsistency. We show that bots may be successful in spreading falsehoods not despite their limited direct impact on human users, but because of this limitation. Our model suggests that bots with limited direct impact on humans may be more and not less effective in spreading their views in the social network, because their direct contacts keep exerting influence on users that the bot does not reach directly. Highly active and well-connected bots, in contrast, may have a strong impact on their direct contacts, but these contacts grow too dissimilar from their network neighbors to further spread the bot\u27s content. To demonstrate this effect, we included bots in Axelrod\u27s seminal model of the dissemination of cultures and conducted simulation experiments demonstrating the strength of weak bots. A series of sensitivity analyses show that the finding is robust, in particular when the model is tailored to the context of online social networks. We discuss implications for future empirical research and developers of approaches to detect bots and misinformatio
Early fragmentation in the adaptive voter model on directed networks
We consider voter dynamics on a directed adaptive network with fixed
out-degree distribution. A transition between an active phase and a fragmented
phase is observed. This transition is similar to the undirected case if the
networks are sufficiently dense and have a narrow out-degree distribution.
However, if a significant number of nodes with low out degree is present, then
fragmentation can occur even far below the estimated critical point due to the
formation of self-stabilizing structures that nucleate fragmentation. This
process may be relevant for fragmentation in current political opinion
formation processes.Comment: 9 pages, 8 figures as published in Phys. Rev.
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