  Conversational prompt-engineering-based large language models (LLMs) have
enabled targeted control over the output creation, enhancing versatility,
adaptability and adhoc retrieval. From another perspective, digital
misinformation has reached alarming levels. The anonymity, availability and
reach of social media offer fertile ground for rumours to propagate. This work
proposes to leverage the advancement of prompting-dependent LLMs to combat
misinformation by extending the research efforts of the RumourEval task on its
Twitter dataset. To the end, we employ two prompting-based LLM variants
(GPT-3.5-turbo and GPT-4) to extend the two RumourEval subtasks: (1) veracity
prediction, and (2) stance classification. For veracity prediction, three
classifications schemes are experimented per GPT variant. Each scheme is tested
in zero-, one- and few-shot settings. Our best results outperform the precedent
ones by a substantial margin. For stance classification,
prompting-based-approaches show comparable performance to prior results, with
no improvement over finetuning methods. Rumour stance subtask is also extended
beyond the original setting to allow multiclass classification. All of the
generated predictions for both subtasks are equipped with confidence scores
determining their trustworthiness degree according to the LLM, and post-hoc
justifications for explainability and interpretability purposes. Our primary
aim is AI for social good.
