{ COVID }-19大流行期间的社交媒体动荡预测：神经内隐动机模式识别作为严重危机的心理测量学标志
Social Media Unrest Prediction during the {COVID}-19 Pandemic: Neural Implicit Motive Pattern Recognition as Psychometric Signs of Severe Crises
摘要：
COVID-19疫情引发国际社会紧张及动荡。除了危机本身，越来越多的迹象表明，世界各地社会的冲突可能性正在上升。全球情绪变化的指标很难检测，直接问卷也存在社会期望偏差。然而，所谓的隐式方法可以从例如社交媒体文本中揭示人类的内在欲望。我们提出了经过心理验证的社会动荡预测因子，并复制了可扩展和自动化的预测，在最近的德国共享任务数据集上建立了一个新的艺术状态。我们使用这个模型，通过比较2019年春季和2020年春季推文样本的既定心理预测因素，研究了COVID-19大流行期间语言对社会动荡的变化。结果表明，冲突指示心理测量学显着增加。通过这项工作，我们证明了基于NLP的自动化方法对定量心理学研究的适用性。
英文摘要：
The COVID-19 pandemic has caused international social tension and unrest. Besides the crisis itself, there are growing signs of rising conflict potential of societies around the world. Indicators of global mood changes are hard to detect and direct questionnaires suffer from social desirability biases. However, so-called implicit methods can reveal humans intrinsic desires from e.g. social media texts. We present psychologically validated social unrest predictors and replicate scalable and automated predictions, setting a new state of the art on a recent German shared task dataset. We employ this model to investigate a change of language towards social unrest during the COVID-19 pandemic by comparing established psychological predictors on samples of tweets from spring 2019 with spring 2020. The results show a significant increase of the conflict indicating psychometrics. With this work, we demonstrate the applicability of automated NLP-based approaches to quantitative psychological research.
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