Automatic　rumor　detection　for　events　on　online　social　media　has　attracted　considerable　attention　in　recent　years.　Usually,　the　events　on　social　media　are　divided　into　several　time　segments,　and　for　each　segment,　corresponding　text　will　be　converted　as　vectors　for　various　neural　network　models　to　detect　rumors.　During　this　process,　however,　only　sentence-level　embedding　has　been　considered,　while　the　contextual　information　at　the　word　level　has　been　largely　ignored.　To　address　that　issue,　in　this　paper,　we　propose　a　novel　rumor　detection　method　based　on　a　hierarchical　recurrent　convolutional　neural　network,　which　integrates　contextual　information　for　rumor　detection.　Specifically,　with　dividing　events　on　social　media　into　time　segments,　recurrent　convolution　neural　network　is　adapted　to　learn　the　contextual　representation　information.　Along　this　line,　a　bidirectional　GRU　network　with　attention　mechanism　is　integrated　to　learn　the　time　period　information　via　combining　event　feature　vectors.　Experiments　on　real-world　data　sets　validate　that　our　solution　could　outperform　several　state-of-the-art　methods.　©　2019,　Springer　Nature　Switzerland　AG.