<p>In the era of Big Data, the proliferation of multi-source online information has made false information control a crucial element in sustaining a healthy digital ecosystem. The significant societal harm caused by misinformation has spurred academic interest in developing robust authenticity detection methods for online content. To date, three primary paradigms have emerged for authenticity detection: unimodal, multimodal, and external knowledge-based approaches. This work provides a detailed investigation into existing false information detection techniques, selecting representative studies to review the current research landscape. Furthermore, it organizes commonly used datasets and evaluation metrics in the field and identifies promising directions for future research in false information detection.</p>