The automated detection of conspiracy theories online typically relies on
supervised learning. However, creating respective training data requires
expertise, time and mental resilience, given the often harmful content.
Moreover, available datasets are predominantly in English and often
keyword-based, introducing a token-level bias into the models. Our work
addresses the task of detecting conspiracy theories in German Telegram
messages. We compare the performance of supervised fine-tuning approaches using
BERT-like models with prompt-based approaches using Llama2, GPT-3.5, and GPT-4
which require little or no additional training data. We use a dataset of
$\sim\!\! 4,000$ messages collected during the COVID-19 pandemic, without the
use of keyword filters.
Our findings demonstrate that both approaches can be leveraged effectively:
For supervised fine-tuning, we report an F1 score of $\sim\!\! 0.8$ for the
positive class, making our model comparable to recent models trained on
keyword-focused English corpora. We demonstrate our model's adaptability to
intra-domain temporal shifts, achieving F1 scores of $\sim\!\! 0.7$. Among
prompting variants, the best model is GPT-4, achieving an F1 score of $\sim\!\!
0.8$ for the positive class in a zero-shot setting and equipped with a custom
conspiracy theory definition.

使用 BERT-like 模型和 prompt-based 方法（Llama2、GPT-3.5 和 GPT-4）来检测德语 Telegram 消息中的阴谋论，结果表明两种方法都是有效的，最佳模型是 GPT-4，具有自定义的阴谋论定义。