The　emergence　and　popularity　of　social　media　have　made　large-scale　group　decision-making　(LSGDM)　problems　increasingly　common,　resulting　in　significant　research　interest　in　this　field.　LSGDM　involves　numerous　evaluators,　which　can　lead　to　disagreements　and　hesitancy　among　them.　Hesitant　fuzzy　sets　(HFSs)　become　crucial　in　this　context　as　they　capture　the　uncertainty　and　hesitancy　among　evaluators.　On　the　other　hand,　research　on　the　Consensus　Reaching　Process　(CRP)　becomes　particularly　important　in　dealing　with　the　inevitable　differences　among　the　great　number　of　evaluators.　Ways　to　mitigate　these　differences　to　reach　an　agreement　are　a　crucial　area　of　study.　For　this　reason,　this　paper　presents　a　new　CRP　model　to　deal　with　LSGDM　problems　in　hesitant　fuzzy　environments.　First,　HFSs　and　Normal-type　Hesitant　Fuzzy　Sets　(N-HFSs)　are　introduced　to　integrate　evaluators＇　subgroup　and　collective　opinions,　aiming　to　preserve　as　much　decision　information　as　possible　while　reducing　computational　complexity.　Subsequently,　a　CRP　with　a　detailed　feedback　suggestion　generation　mechanism　is　developed,　which　considers　the　willingness　of　evaluators　to　modify　their　opinions,　thereby　improving　the　effectiveness　of　reaching　an　agreement.　Finally,　a　LSGDM　framework　that　does　not　require　any　normalization　process　is　proposed,　and　its　feasibility　and　robustness　are　demonstrated　through　a　numerical　example.