Since the Web 2.0 era, the Internet is a huge content editing place in which users contribute to the content they browse. Users do not just edit the content but they collaborate on this content. Such shared content can be edited by thousands of people. However, current consistency maintenance algorithms seem not to be adapted to massive collaborative updating. Shared data is usually fragmented into smaller atomic elements that can only be added or removed. Coarse-grained data leads to the possibility of conflicting updates while fine-grained data requires more metadata. In this paper we offer a solution for handling an adaptable granularity for shared data that overcomes the limitations of fixed-grained data approaches. Our approach defines data at a coarse granularity when it is created and refines its granularity only for facing possible conflicting updates on this data. We exhibit three implementations of our algorithm and compare their performances with other algorithms in various scenarios.