Misinformation　posted　on　social　media　during　COVID-19　is　one　main　example　of　infodemic　data.　This　phenomenon　was　prominent　in　China　when　COVID-19　happened　at　the　beginning.　While　a　lot　of　data　can　be　collected　from　various　social　media　platforms,　publicly　available　infodemic　detection　data　remains　rare　and　is　not　easy　to　construct　manually.　Therefore,　instead　of　developing　techniques　for　infodemic　detection,　this　paper　aims　at　constructing　a　Chinese　infodemic　dataset,　＂infodemic　2019＂,　by　collecting　widely　spread　Chinese　infodemic　during　the　COVID-19　outbreak.　Each　record　is　labeled　as　true,　false　or　questionable.　After　a　four-time　adjustment,　the　original　imbalanced　dataset　is　converted　into　a　balanced　dataset　by　exploring　the　properties　of　the　collected　records.　The　final　labels　achieve　high　intercoder　reliability　with　healthcare　workers＇　annotations　and　the　high-frequency　words　show　a　strong　relationship　between　the　proposed　dataset　and　pandemic　diseases.　Finally,　numerical　experiments　are　carried　out　with　RNN,　CNN　and　fastText.　All　of　them　achieve　reasonable　performance　and　present　baselines　for　future　works.