Yang Yi, Ta Na, Li Kaiyu, Jiao Fang, Hu Baijing, Li Zhanghao
School of Chinese Culture and Communication, Beijing International Studies University, Beijing, China.
School of Journalism and Communication, Renmin University of China, Beijing, China.
Front Psychol. 2021 Oct 5;12:740065. doi: 10.3389/fpsyg.2021.740065. eCollection 2021.
Under the uncertainty led by the decentralized information on social media, people seek homogeneity in either opinions or affection to establish group identity to better understand the information. This also means they are easily polarized, not only ideologically but also in their actions. Affective polarization is the emotional tendency for people to show animosity toward opposing partisans while seeking homogeneity from fellow partisans. Much research into online affective polarization has focused on quantifying anxiety at an individual level while neglecting that on a collective basis. Therefore, this paper examined the polarization of collective anxiety in topic-based communities on Weibo. We aim to interpret correlations between collective anxiety online and topic characteristics, user competence, as well as the proportion of influencers of Weibo topic-based communities. Our neural networks model and statistical analysis were based on 200 communities with 403,380 personal accounts and 1,012,830 messages. Collective anxiety levels are correlated to (1) the extent to which a topic captures public interest, (2) how community members articulate topics on social network platforms, and (3) the ratio of influencers in the community. Specifically, people's conflicting perceptions and articulations of topics might increase collective anxiety, while the extent to which a topic is of the public interest and the number of influencers engaged in a topic account for any decline in its ranking. Furthermore, familiarity with a topic does not help predict collective anxiety levels. There are no significant links between community size or interactivity dynamics and the level of collective anxiety in the topic-based community. Our computational model has 85.00% precision and 87.00% recall. This study found the collective anxiety augment due to topic proximities to public interest and members' lack of declarative knowledge on topics, while to decline with an increasing portion of online influencers. These findings indicate that collective anxiety is induced due to a lack of credibility. Also, the amount of conflicting information shared by different people places them in a state of flux. Therefore, a community with more influencers may be more likely to experience anxiety polarization, bringing forth the issue of layered information and inequality.
在社交媒体分散信息所导致的不确定性之下，人们在观点或情感上寻求同质化，以建立群体认同，从而更好地理解信息。这也意味着他们很容易两极分化，不仅在意识形态上，而且在行动上。情感两极分化是指人们在寻求与同党派者同质化的同时，对对立党派表现出敌意的情感倾向。许多关于网络情感两极分化的研究都集中在量化个体层面的焦虑，而忽略了集体层面的焦虑。因此，本文研究了微博上基于话题的社区中集体焦虑的两极分化情况。我们旨在解读网络集体焦虑与话题特征、用户能力以及微博基于话题的社区中影响者比例之间的相关性。我们的神经网络模型和统计分析基于200个社区，涉及403380个个人账户和1012830条信息。集体焦虑水平与以下因素相关：（1）一个话题引起公众兴趣的程度；（2）社区成员在社交网络平台上阐述话题的方式；（3）社区中影响者的比例。具体而言，人们对话题的冲突认知和阐述可能会增加集体焦虑，而一个话题的公众兴趣程度以及参与该话题的影响者数量则会导致其排名下降。此外，对一个话题的熟悉程度无助于预测集体焦虑水平。社区规模或互动动态与基于话题的社区中的集体焦虑水平之间没有显著联系。我们的计算模型精确率为85.00%，召回率为87.00%。本研究发现，集体焦虑因话题与公众兴趣的接近程度以及成员对话题缺乏陈述性知识而加剧，而随着网络影响者比例的增加而下降。这些发现表明，集体焦虑是由于缺乏可信度而引发的。此外，不同人分享的冲突信息量使他们处于一种变化的状态。因此，一个有更多影响者的社区可能更容易经历焦虑两极分化，从而引发分层信息和不平等的问题。
