Yook Soon-Hyung, Kim Yup
Department of Physics and Research Institute for Basic Sciences, Kyung Hee University, Seoul 130-701, Korea.
Phys Rev E. 2020 Jan;101(1-1):012312. doi: 10.1103/PhysRevE.101.012312.
We study the origin of the log-normal popularity distribution of trending memes observed in many real social networks. Based on a biological analogy, we introduce a fitness of each meme, which is a natural assumption based on sociological reasons. From numerical simulations, we find that the relative popularity distribution of the trending memes becomes a log-normal distribution when the fitness of the meme increases exponentially. On the other hand, if the fitness grows slowly, then the distribution significantly deviates from the log-normal distribution. This indicates that the fast growth of fitness is the necessary condition for the trending meme. Furthermore, we also show that the popularity of the trending topic grows linearly. These results provide a clue to understand long-lasting questions, such as what causes some memes to become extremely popular and how such memes are exposed to the public much longer than others.
我们研究了在许多真实社交网络中观察到的热门模因的对数正态流行度分布的起源。基于生物学类比，我们引入了每个模因的适应性，这是基于社会学原因的一个自然假设。通过数值模拟，我们发现当模因的适应性呈指数增长时，热门模因的相对流行度分布会变成对数正态分布。另一方面，如果适应性增长缓慢，那么分布会显著偏离对数正态分布。这表明适应性的快速增长是热门模因的必要条件。此外，我们还表明热门话题的流行度呈线性增长。这些结果为理解一些长期存在的问题提供了线索，比如是什么导致一些模因变得极其流行，以及这些模因如何比其他模因更长时间地暴露在公众面前。
