Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Social Media Unrest Prediction during the {COVID}-19 Pandemic: Neural
  Implicit Motive Pattern Recognition as Psychometric Signs of Severe Crises   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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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int