Individuals　use　Twitter　for　personal　communication,　whereas　businesses,　politicians　and　celebrities　use　Twitter　for　branding　purposes.　Distinguishing　Personal　from　Branding　Twitter　accounts　is　important　for　Twitter　analytics.　Existing　studies　of　Twitter　account　classification　apply　classical　supervised　learning,　which　requires　intensive　manual　annotation　for　training.　In　this　paper,　we　propose　CDS　(Collaborative　Distant　Supervision),　a　novel　learning　scheme　for　Twitter　account　classification　that　does　not　require　intensive　manual　labelling.　Twitter　accounts　are　automatically　labelled　using　heuristics　for　distant　supervision　learning.　To　achieve　effective　learning　from　heuristic　labels,　active　learning　is　applied　to　identify　and　correct　false　positive　labels,　and　semi-supervised　learning　is　applied　to　further　use　false　negatives　missed　by　labelling　heuristics　for　learning.　Extensive　experiments　on　Twitter　data　showed　that　CDS　achieved　high　classification　accuracy.　(C)　2017　Elsevier　Ltd.　All　rights　reserved.