The　COVID-19　infodemic,　characterized　by　the　rapid　spread　of　misinformation　and　unverified　claims　related　to　the　pandemic,　presents　a　significant　challenge.　This　paper　presents　a　comparative　analysis　of　the　COVID-19　infodemic　in　the　English　and　Chinese　languages,　utilizing　textual　data　extracted　from　social　media　platforms.　To　ensure　a　balanced　representation,　two　infodemic　datasets　were　created　by　augmenting　previously　collected　social　media　textual　data.　Through　word　frequency　analysis,　the　30　most　frequently　occurring　infodemic　words　are　identified,　shedding　light　on　prevalent　discussions　surrounding　the　infodemic.　Moreover,　topic　clustering　analysis　uncovers　thematic　structures　and　provides　a　deeper　understanding　of　primary　topics　within　each　language　context.　Additionally,　sentiment　analysis　enables　comprehension　of　the　emotional　tone　associated　with　COVID-19　information　on　social　media　platforms　in　English　and　Chinese.　This　research　contributes　to　a　better　understanding　of　the　COVID-19　infodemic　phenomenon　and　can　guide　the　development　of　strategies　to　combat　misinformation　during　public　health　crises　across　different　languages.