Detection of Conspiracy Theories Beyond Keyword Bias in German-Language Telegram Using Large Language Models

arXiv:2404.17985v1 Announce Type: new
Abstract: 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, …

abstract arxiv automated beyond bias conspiracy conspiracy theories cs.ai cs.cl data datasets detection english expertise german however language language models large language large language models resilience supervised learning telegram token training training data type