  Despite the widespread use of Barabasi's scale-free networks and Erdos-Renyi
networks of which degree correlation (assortativity) is neutral, numerous
studies demonstrated that online social networks tend to show assortative
mixing (positive degree correlation), while non-social networks show a
disassortative mixing (negative degree correlation). First, we analyzed the
variability in the assortativity coefficients of different groups of the same
platform by using three different subreddits in Reddit. Our data analysis
results showed that Reddit is disassortative, and assortativity coefficients of
the aforementioned subreddits are computed as -0.0384, -0.0588 and -0.1107,
respectively. Motivated by the variability in the results even in the same
platform, we decided to investigate the sensitivity of dynamics of consensus
formation to the assortativity of the network. We concluded that the system is
more likely to reach a consensus when the network is disassortatively mixed or
neutral; however, the likelihood of the consensus significantly decreases when
the network is assortatively mixed. Surprisingly, the time elapsed until all
nodes fix their opinions is slightly lower when the network is neutral compared
to either assortative or disassortative networks. These results are more
pronounced when the thresholds of agents are more heterogeneously distributed.
