During　the　outbreak　of　the　COVID-19　(2019　coronavirus　disease),　misinformation　related　to　the　virus　spread　rapidly　online　and　have　led　to　serious　difficulties　in　controlling　the　disease.　The　term　infodemic　is　coined　to　outline　the　bad　effect　from　the　extensive　dissemination　of　misinformation　during　the　outbreak.　With　regards　to　this　phenomenon,　the　World　Health　Organization　emphasized　the　need　to　fight　against　infodemic　and　asked　all　countries　not　only　to　make　efforts　in　slowing　down　the　spread　of　the　COVID-19　but　also　in　countering　the　risk　caused　by　infodemic.　Due　to　its　negative　impact,　this　paper　analyzes　infodemic　on　Chinese　social　media　at　the　initial　stage　of　the　COVID-19　outbreak　and　presents　a　4P　framework　standing　for　the　four　features　of　Chinese　infodemic:　Prevention　Attention,　Problem　Orientation,　Patterns　Interaction　and　Points　Globalization.　Furthermore,　a　selective　review　of　existing　datasets　in　the　neural　networks　domain　is　synthesized　based　on　the　4P　framework.　Finally,　research　directions,　including　recommendations,　about　constructing　a　large-scale　dataset　for　Chinese　infodemic　automatic　detection　are　proposed.