Objective　To　address　the　complexities　of　distinguishing　truth　from　falsehood　in　the　context　of　the　COVID-19　infodemic,　this　paper　focuses　on　utilizing　deep　learning　models　for　infodemic　ternary　classification　detection.Methods　Eight　commonly　used　deep　learning　models　are　employed　to　categorize　collected　records　as　true,　false,　or　uncertain.　These　models　include　fastText,　three　models　based　on　recurrent　neural　networks,　two　models　based　on　convolutional　neural　networks,　and　two　transformer-based　models.Results　Precision,　recall,　and　F1-score　metrics　for　each　category,　along　with　overall　accuracy,　are　presented　to　establish　benchmark　results.　Additionally,　a　comprehensive　analysis　of　the　confusion　matrix　is　conducted　to　provide　insights　into　the　models＇　performance.Conclusion　Given　the　limited　availability　of　infodemic　records　and　the　relatively　modest　size　of　the　two　tested　data　sets,　models　with　pretrained　embeddings　or　simpler　architectures　tend　to　outperform　their　more　complex　counterparts.　This　highlights　the　potential　efficiency　of　pretrained　or　simpler　models　for　ternary　classification　in　COVID-19　infodemic　detection　and　underscores　the　need　for　further　research　in　this　area.