论文标题
激进研究的纵向情感分析：社交媒体平台上的跨颞动态及其含义
Longitudinal Sentiment Analyses for Radicalization Research: Intertemporal Dynamics on Social Media Platforms and their Implications
论文作者
论文摘要
本讨论的论文展示了在社交媒体平台上可以描绘纵向分析的纵向情感分析，固有的挑战以及如何从纵向的角度受益的挑战。此外，由于对情感分析的工具应简化和加速有关可接受的评估者可靠性可接受数据的分析过程，因此将检查其在激进研究中的适用性，以了解2021年1月6日收集的推文，即华盛顿美国国会大厦的日期。因此，将在三个不同的序列中均匀分析49,350条推文：在华盛顿美国国会大厦之前，之中和之后。这些序列在社交媒体平台上的评论中突出了跨期的动态，以及使用条件均值和条件差异时纵向观点的可能好处。还将证明有关识别此类事件和相关仇恨言论以及常见应用程序错误的限制。结果，只有在某些条件下，纵向情感分析才能在激进研究的背景下提高基于证据的预测的准确性。
This discussion paper demonstrates how longitudinal sentiment analyses can depict intertemporal dynamics on social media platforms, what challenges are inherent and how further research could benefit from a longitudinal perspective. Furthermore and since tools for sentiment analyses shall simplify and accelerate the analytical process regarding qualitative data at acceptable inter-rater reliability, their applicability in the context of radicalization research will be examined regarding the Tweets collected on January 6th 2021, the day of the storming of the U.S. Capitol in Washington. Therefore, a total of 49,350 Tweets will be analyzed evenly distributed within three different sequences: before, during and after the U.S. Capitol in Washington was stormed. These sequences highlight the intertemporal dynamics within comments on social media platforms as well as the possible benefits of a longitudinal perspective when using conditional means and conditional variances. Limitations regarding the identification of supporters of such events and associated hate speech as well as common application errors will be demonstrated as well. As a result, only under certain conditions a longitudinal sentiment analysis can increase the accuracy of evidence based predictions in the context of radicalization research.
