论文标题
社交媒体的动荡预测{covid} -19大流行：神经隐式动力模式识别是严重危机的心理测量迹象
Social Media Unrest Prediction during the {COVID}-19 Pandemic: Neural Implicit Motive Pattern Recognition as Psychometric Signs of Severe Crises
论文作者
论文摘要
共同的19日大流行造成了国际社会紧张和动荡。除了危机本身，世界各地社会的冲突潜力不断增加的迹象。全球情绪变化的指标很难检测到，直接问卷会遭受社会可取性偏见的影响。但是，所谓的隐式方法可以揭示人类的内在欲望，例如社交媒体文本。我们提出了经过心理验证的社会动荡预测指标，并复制可扩展和自动化的预测，在最近的德国共享任务数据集上设置了新的最新技术。我们采用该模型来调查在2019年大流行期间的语言变化向社会动荡的变化，通过将2019年春季推文样本与2020年春季推文的样本进行比较，结果表明冲突表明心理计量学的冲突显着增加。通过这项工作，我们证明了基于NLP的自动化方法在定量心理学研究中的适用性。
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.
