Bias in social interactions and emergence of extremism in complex social networks

Emergence of extremism in social networks is among the most appealing topics of opinion dynamics in computational sociophysics in recent decades. Most of the existing studies presume that the initial existence of certain groups of opinion extremities and the intrinsic stubbornness in individuals' characteristics are the key factors allowing the tenacity or even prevalence of such extreme opinions. We propose a modification to the consensus making in bounded-confidence models where two interacting individuals holding not so different opinions tend to reach a consensus by adopting an intermediate opinion of their previous ones. We show that if individuals make biased compromises, extremism may still arise without a need of an explicit classification of extremists and their associated characteristics. With such biased consensus making, several clusters of diversified opinions are gradually formed up in a general trend of shifting toward the extreme opinions close to the two ends of the opinion range, which may allow extremism communities to emerge and moderate views to be dwindled. Furthermore, we assume stronger compromise bias near opinion extremes. It is found that such a case allows moderate opinions a greater chance to survive compared to that of the case where the bias extent is universal across the opinion space. As to the extreme opinion holders' lower tolerances toward different opinions, which arguably may exist in many real-life social systems, they significantly decrease the size of extreme opinion communities rather than helping them to prevail. Brief discussions are presented on the significance and implications of these observations in real-life social systems.Ministry of Education (MOE)National Research Foundation (NRF)Accepted versionThe research was partially supported by the Ministry of Education, Singapore, under Contract No. MOE2016-T2-1-119, Tsinghua University Initiative Scientific Research Program, and Tsinghua-Foshan Innovation Special Fund (TFISF). The work was also partially supported by the Fundamental Research Funds for the Central Universities, Sichuan University (Grant No. YJ201933), the China Postdoctoral Science Foundation (Grant No. 2019TQ0217), and the National Natural Science Foundation of China (NNSFC; Grant Nos. U1736212 and 61572334). This work was also partially supported by the Future Resilient Systems Project-Stage II at the Singapore-ETH Centre (SEC), which was funded by the National Research Foundation of Singapore (NRF)