Zhou Cangqi, Zhao Qianchuan, Lu Wenbo
Center for Intelligent and Networked Systems (CFINS), Department of Automation and TNList, Tsinghua University, Beijing, 100084, China.
PLoS One. 2015 Oct 14;10(10):e0140556. doi: 10.1371/journal.pone.0140556. eCollection 2015.
Clustered structure of social networks provides the chances of repeated exposures to carriers with similar information. It is commonly believed that the impact of repeated exposures on the spreading of information is nontrivial. Does this effect increase the probability that an individual forwards a message in social networks? If so, to what extent does this effect influence people's decisions on whether or not to spread information? Based on a large-scale microblogging data set, which logs the message spreading processes and users' forwarding activities, we conduct a data-driven analysis to explore the answer to the above questions. The results show that an overwhelming majority of message samples are more probable to be forwarded under repeated exposures, compared to those under only a single exposure. For those message samples that cover various topics, we observe a relatively fixed, topic-independent multiplier of the willingness of spreading when repeated exposures occur, regardless of the differences in network structure. We believe that this finding reflects average people's intrinsic psychological gain under repeated stimuli. Hence, it makes sense that the gain is associated with personal response behavior, rather than network structure. Moreover, we find that the gain is robust against the change of message popularity. This finding supports that there exists a relatively fixed gain brought by repeated exposures. Based on the above findings, we propose a parsimonious model to predict the saturated numbers of forwarding activities of messages. Our work could contribute to better understandings of behavioral psychology and social media analytics.
社交网络的聚集结构提供了反复接触携带相似信息者的机会。人们普遍认为，反复接触对信息传播的影响并非微不足道。这种影响会增加个体在社交网络中转发消息的概率吗？如果是这样，这种影响在多大程度上会影响人们关于是否传播信息的决策？基于一个记录了消息传播过程和用户转发活动的大规模微博数据集，我们进行了数据驱动的分析来探索上述问题的答案。结果表明，与仅单次接触的消息样本相比，绝大多数消息样本在反复接触下更有可能被转发。对于那些涵盖各种主题的消息样本，我们观察到当反复接触发生时，无论网络结构存在差异，传播意愿都有一个相对固定的、与主题无关的乘数。我们认为这一发现反映了普通人在反复刺激下的内在心理收益。因此，这种收益与个人反应行为而非网络结构相关是有道理的。此外，我们发现这种收益对消息热度的变化具有鲁棒性。这一发现支持反复接触会带来相对固定的收益。基于上述发现，我们提出了一个简约模型来预测消息转发活动的饱和数量。我们的工作有助于更好地理解行为心理学和社交媒体分析。
