Fig 1.
This illustration demonstrates a pattern of influence.
At the first time step, one among six neighbors of a user shared a message, in the following time step, another user shared the same message and in the final step, two additional users. The user is thus exposed to a “pattern” of social signals we represent as A = {1, 2, 4} for that message.
Table 1.
Table of symbols.
Fig 2.
Example screen in the cyber-defense provider selection task.
Participants in the Uniform Messages (UM) condition of the study have access to a screen that resembled this one. The Feedback section displays the number of attacks the participant prevented after each time step. The Decisions section displays the six provider choices that it has. Finally, the Messages section is displayed after Time Step 12, where the participants can view what their peers selected in the previous time step.
Fig 3.
The signal vs time step plots for the 4 patterns—Note that for the NM group (not shown here), no peer signal in the form of pre-selected sub-optimal technologies were sent to the participants at any time step.
Fig 4.
Illustration of the linear cascade diffusion.
The technology Cu chosen by us as the sub-optimal technology (influence decision for user u (in dots) cascades through the peers of u over the six time steps. Colored nodes denote the activated peers with respect to Cu (manually preprogrammed by us) at each time step. Note that although at time steps starting at 13 and ending at 18, there are subjects (uncolored) among peers who have not adopted Cu, their selections (which may not be Cu) are visible to u. However, which users among the peers have been preprogrammed manually is by default unknown to the target subject u.
Table 2.
Average number of attacks prevented by subjects in each group.
The lower attack numbers suggest participants deviated more from the optimal decision responding to social influence.
Fig 5.
P(di)—Proportion of users in each group who were administered technology or decision di as the influence decision.
Note that only the decisions that are not optimal are sent as prospective influence decisions/social signals in the second phase of the experiment.
Fig 6.
Probability of decisions made in time steps 13 to 18.
(a) Probability of making the optimal decision. (b) Probability of making the sub-optimal (other 5) decision. The error bars denote standard error over the distributions.
Fig 7.
Fraction of users in each group adopting the influence decision chosen by their peers.
Fig 8.
Plots of adoption under the influence of s exposures.
(a) For each signal quantity s, the proportion of individuals who made their first switch to their influence decision after being exposed to s signals, (b) The proportion of individuals who adopt their influence decision after being exposed to signals from s influence signals.
Table 3.
Results of Fixed Effects (FE) and Random Effects (RE) modeling.
“Inter”. denotes intercept in the regression models. Values in brackets denote standard errors.
Fig 9.
Success ratio.
Fig 10.
Agent based models for measuring social influence with multiple choices.
(a) Single agent behavior model, (b) Multi-agent behavior simulation.
Table 4.
Statistics of the data used for simulation relating the 3 events.
Fig 11.
Degree distribution of the follower networks.
Fig 12.
ABM results for the simulation using Algorithm 1 on three Twitter who-follows-whom network for the events (a) Charlie Hebdo, (b) Putin missing and (c) Ferguson unrest.