新闻应用中用户属性之间信息采集偏差分析
Analysis of Bias in Gathering Information Between User Attributes in News Application
摘要：
在网络信息收集的过程中，确认偏差是已知存在的，例如回声室和过滤气泡等现象。我们的目的是揭示人们如何消费新闻，并讨论这些现象。在Web服务中，我们能够使用服务的动作日志来调查这些现象。然而，许多现有的研究对这些现象进行了问卷调查，并有很少的研究，使用行动日志。在本文中，我们试图发现偏见的信息收集，由于用户的人口统计属性，如年龄和性别的差异，从新闻发布服务的行为日志。首先，我们总结了每个用户属性在服务中的操作，并显示了用户行为的差异取决于属性。接下来，使用相关系数测量属性之间的相关程度，并且发现在属性之间的新闻文章的浏览倾向中存在强相关性。然后，发现属性间的关键词偏差，利用回归分析的参数找出属性间行为偏差的关键词。由于这些发现的关键字几乎可以用大新闻来解释，因此我们提出的方法在检测有偏见的关键字方面是有效的。
英文摘要：
In the process of information gathering on the web, confirmation bias is known to exist, exemplified in phenomena such as echo chambers and filter bubbles. Our purpose is to reveal how people consume news and discuss these phenomena. In web services, we are able to use action logs of a service to investigate these phenomena. However, many existing studies about these phenomena are conducted via questionnaires, and there are few studies using action logs. In this paper, we attempt to discover biases of information gathering due to differences in user demographic attributes, such as age and gender, from the behavior log of the news distribution service. First, we summarized the actions in the service for each user attribute and showed the difference of user behavior depending on the attributes. Next, the degree of correlation between the attributes was measured using the correlation coefficient, and a strong correlation was found to exist in the browsing tendency of the news articles between the attributes. Then, the bias of keywords between attributes was discovered, keywords with bias in behavior among the attributes were found using parameters of regression analysis. Since these discovered keywords are almost explainable by big news, our proposed method is effective in detecting biased keywords.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
