ABSTRACT

The influence of online comment sections on the news has increased based on the development of collective online behaviors in the digitalized news media era. In this study, we focus on the effect of comment order (e.g., sorting comments by the number of likes or by the time of posting) on the formation of public opinion. We explore whether reading comments sorted by number of likes (a) induces more comments from users, (b) increases the expression of user opinions in response to others’ comments through the action of liking or disliking comments and (c) consolidates user opinion. For the empirical verification of the effects of popularity metrics, we chose a common topic (increasing minimum wage), collected actual data (reviewing 3,251 articles and the numbers of associated comments, likes, and dislikes), and compared news categories based on the existence of popularity metrics. Semantic network analysis was conducted with UCINET and python for K-means clustering, and cosine similarity. Our results show how the comment order in the internet news environment affects the commenting behavior of news consumers.

摘要

随着数字化新闻媒体时代集体网络行为的发展，网络评论版块对新闻的影响力不断增强。在本研究中，我们关注评论顺序（例如，按点赞数或发布时间对评论进行排序）对舆论形成的影响。我们探讨阅读按喜欢数量排序的评论是否（a）引起用户更多评论，（b）通过喜欢或不喜欢评论的行为增加用户对他人评论的意见表达，以及（c）巩固用户意见。为了对流行度指标的效果进行实证验证，我们选择了一个共同的话题（提高最低工资），收集了实际数据（回顾了 3,251 篇文章以及相关评论、喜欢和不喜欢的数量），并根据流行度指标的存在来比较新闻类别。使用 UCINET 和 python 进行语义网络分析，以进行 K 均值聚类和余弦相似度。我们的结果显示了互联网新闻环境中的评论顺序如何影响新闻消费者的评论行为。