Xiong Fei, Liu Yun
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China.
Chaos. 2014 Mar;24(1):013130. doi: 10.1063/1.4866011.
Opinion exchange models aim to describe the process of public opinion formation, seeking to uncover the intrinsic mechanism in social systems; however, the model results are seldom empirically justified using large-scale actual data. Online social media provide an abundance of data on opinion interaction, but the question of whether opinion models are suitable for characterizing opinion formation on social media still requires exploration. We collect a large amount of user interaction information from an actual social network, i.e., Twitter, and analyze the dynamic sentiments of users about different topics to investigate realistic opinion evolution. We find two nontrivial results from these data. First, public opinion often evolves to an ordered state in which one opinion predominates, but not to complete consensus. Second, agents are reluctant to change their opinions, and the distribution of the number of individual opinion changes follows a power law. Then, we suggest a model in which agents take external actions to express their internal opinions according to their activity. Conversely, individual actions can influence the activity and opinions of neighbors. The probability that an agent changes its opinion depends nonlinearly on the fraction of opponents who have taken an action. Simulation results show user action patterns and the evolution of public opinion in the model coincide with the empirical data. For different nonlinear parameters, the system may approach different regimes. A large decay in individual activity slows down the dynamics, but causes more ordering in the system.
意见交流模型旨在描述公众舆论形成的过程，试图揭示社会系统中的内在机制；然而，模型结果很少使用大规模实际数据进行实证验证。在线社交媒体提供了大量关于意见互动的数据，但意见模型是否适用于刻画社交媒体上的意见形成问题仍有待探索。我们从一个实际的社交网络（即推特）收集了大量用户互动信息，并分析了用户对不同话题的动态情绪，以研究现实中的意见演变。我们从这些数据中发现了两个重要结果。第一，公众舆论往往会演变成一种有序状态，其中一种意见占主导地位，但并非达成完全共识。第二，个体不愿意改变自己的意见，个体意见变化次数的分布遵循幂律。然后，我们提出了一个模型，其中个体根据其活跃度采取外部行动来表达其内部意见。相反，个体行动会影响邻居的活跃度和意见。个体改变意见的概率非线性地取决于采取行动的反对者的比例。模拟结果表明，模型中的用户行动模式和公众舆论演变与实证数据一致。对于不同的非线性参数，系统可能会趋近于不同的状态。个体活跃度的大幅下降会减缓动态变化，但会使系统更加有序。
