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<p>Our contribution is concerned with the increasing amount of conspiracy theories and other forms of mis- or disinformation spreading on social media. We address this challenge to democratic opinion formation with a&#xa0;two-pronged approach. On the one hand, we identify specific conspiracy narratives in a&#xa0;social media corpus; on the other hand, we look at general linguistic dimensions that contribute to the overall drivel-like quality of such texts regardless of the narratives involved. For the present contribution, six distinct dimensions, along with an overall measure of drivel-like quality, were assessed using a&#xa0;five-point Likert scale. A&#xa0;sample of approximately 2000 texts drawn from German Telegram was manually annotated. We present the calculation of inter-annotator agreement to evaluate annotation consistency, conduct correlation analyses to examine the relationships between the individual dimensions, and fit a&#xa0;linear model to predict the overall drivel-like quality of the texts based on the individual dimensions. In addition, we train ordinal regression models to predict the values of each dimension from bag-of-<i>n</i>-grams representations. Finally, an analysis of feature weights identifies which <i>n</i>-grams serve as the most reliable indicators of each dimension.</p>

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Narratives and Linguistic Features of Drivel

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月2日

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  • Philipp Heinrich,
  • Andreas Blombach,
  • Stephanie Evert,
  • Fabian Schäfer

摘要

Our contribution is concerned with the increasing amount of conspiracy theories and other forms of mis- or disinformation spreading on social media. We address this challenge to democratic opinion formation with a two-pronged approach. On the one hand, we identify specific conspiracy narratives in a social media corpus; on the other hand, we look at general linguistic dimensions that contribute to the overall drivel-like quality of such texts regardless of the narratives involved. For the present contribution, six distinct dimensions, along with an overall measure of drivel-like quality, were assessed using a five-point Likert scale. A sample of approximately 2000 texts drawn from German Telegram was manually annotated. We present the calculation of inter-annotator agreement to evaluate annotation consistency, conduct correlation analyses to examine the relationships between the individual dimensions, and fit a linear model to predict the overall drivel-like quality of the texts based on the individual dimensions. In addition, we train ordinal regression models to predict the values of each dimension from bag-of-n-grams representations. Finally, an analysis of feature weights identifies which n-grams serve as the most reliable indicators of each dimension.