验证日常社交媒体的情绪宏观
Validating daily social media macroscopes of emotions
摘要：
为了在宏观层面上研究情感，情感科学家们广泛使用社交媒体文本的情感分析。然而，这种方法可能会受到一系列方法问题的影响，涉及抽样偏差和计量误差。到目前为止，它还没有被验证，如果社会媒体情绪可以衡量一天到一天的时间动态的情绪聚集在整个在线社区的宏观层面。我们在一家在线报纸上进行了一项大规模的调查，收集用户每天的情感状态自我报告，并将这些报告与同一在线平台上用户讨论的情感分析汇总结果进行比较。此外，我们使用Twitter文本作为同一社区情绪的宏观范围，预先注册了我们研究的复制。对于这两个平台，我们发现文本分析结果与自我报告的情绪水平之间，以及两种测量值的日间变化之间存在很强的相关性。我们进一步表明，监督和无监督的文本分析方法的组合是最准确的方法来衡量情感聚合。我们举例说明了在研究新冠肺炎病例数量与情绪之间的关联时，这种社交媒体宏观镜的应用，表明使用调查数据时的关联强度与使用社交媒体数据时的关联强度相当。我们的研究结果表明，可以用社交媒体文本跟踪在线平台用户情感状态的宏观动态，当自我报告的数据不可用或难以收集时，可以补充调查。
英文摘要：
To study emotions at the macroscopic level, affective scientists have made extensive use of sentiment analysis on social media text. However, this approach can suffer from a series of methodological issues with respect to sampling biases and measurement error. To date, it has not been validated if social media sentiment can measure the day to day temporal dynamics of emotions aggregated at the macro level of a whole online community. We ran a large-scale survey at an online newspaper to gather daily self-reports of affective states from its users and compare these with aggregated results of sentiment analysis of user discussions on the same online platform. Additionally, we preregistered a replication of our study using Twitter text as a macroscope of emotions for the same community. For both platforms, we find strong correlations between text analysis results and levels of self-reported emotions, as well as between inter-day changes of both measurements. We further show that a combination of supervised and unsupervised text analysis methods is the most accurate approach to measure emotion aggregates. We illustrate the application of such social media macroscopes when studying the association between the number of new COVID-19 cases and emotions, showing that the strength of associations is comparable when using survey data as when using social media data. Our findings indicate that macro level dynamics of affective states of users of an online platform can be tracked with social media text, complementing surveys when self-reported data is not available or difficult to gather.
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。
