ABSTRACT Online dissemination of conspiracy theories (CTs) during epidemics poses significant risks to public health. This paper addresses the problem of detecting CTs in social media posts with an emphasis on the resource‐constrained scenarios characterized by the scarcity of labelled datasets and expert annotations, and the lack of computational budget for large‐scale LLM inference. To address these challenges, we investigate resource‐efficient methods for CT detection across multiple epidemics. We construct a novel dataset of CT‐labelled social media posts covering four major epidemics from the past decade: Ebola, Zika, COVID‐19 and Monkeypox. We conduct extensive experiments addressing four research questions: (1) the performance of BERT‐like models on individual epidemics, (2) the ability to transfer knowledge from past epidemics to new ones, (3) the efficacy of zero‐shot classification using Large Language Models (LLMs) and (4) the feasibility of training BERT‐like models on LLM‐labelled datasets. Our findings indicate that BERT‐like models exhibit highly variable performance across epidemics. Transfer learning from prior epidemics can be effective and their performance can be improved with the number of prior datasets. Zero‐shot LLM classifiers, including ensemble methods, achieve performance that matches or surpasses that of fine‐tuned BERT‐like models. Finally, we demonstrate that BERT‐like models trained on LLM‐labelled datasets achieve results close to the models trained on expert‐annotated data, offering a practical alternative when expert labelling is infeasible. While automated methods can be useful for data analysis, we caution against automatization of content filtering due to the inherent difficulty of CT detection and the potential biases of language models.

In the age of social media and generative AI, the ability to automatically assess the credibility of online content has become increasingly critical, complementing traditional approaches to false information detection. Credibility assessment relies on aggregating diverse credibility signals—small units of information, such as content subjectivity, bias or a presence of persuasion techniques—into a final credibility label/score. However, current research in automatic credibility assessment and credibility signals detection remains highly fragmented, with many signals studied in isolation and lacking integration. Notably, there is a scarcity of approaches that detect and aggregate multiple credibility signals simultaneously. These challenges are further exacerbated by the absence of a comprehensive and up-to-date overview of research works that connects these research efforts under a common framework and identifies shared trends, challenges and open problems. In this survey, we address this gap by presenting a systematic and comprehensive literature review of 175 research papers, focusing on textual credibility signals within the field of Natural Language Processing (NLP), which undergoes a rapid transformation due to advancements in Large Language Models (LLMs). While positioning the NLP research into the broader multidisciplinary landscape, we examine both automatic credibility assessment methods as well as the detection of nine categories of credibility signals. We provide an in-depth analysis of three key categories: (1) factuality, subjectivity and bias, (2) persuasion techniques and logical fallacies and (3) check-worthy and fact-checked claims. In addition to summarising existing methods, datasets and tools, we outline future research direction and emerging opportunities, with particular attention to evolving challenges posed by generative AI.