Community　detection　in　online　social　networks　(OSNs)　has　attracted　a　great　deal　of　attention　in　recent　years　due　to　its　potential　prospects　in　applications　such　as　recommendation　systems,　spam　detection,　targeted　advertising,　etc.　However,　community　detection　methods　that　have　been　proposed　so　far　rely　mostly　on　static　information,　such　as　the　topology　of　the　network,　the　attributes　of　the　users,　etc.　In　this　paper,　we　propose　a　framework　that　enhances　community　detection　with　user　interaction　behaviors　in　OSNs　since　such　behaviors　should　be　important　factors　in　characterizing　the　communities.　In　the　framework,　the　various　features　of　user　interaction　behaviors　are　expressed　in　the　form　of　interaction　indices　through　utilizing　the　cumulative　distribution　function　and　interaction　divergence.　The　indices　are　then　transformed　into　edge　weights　through　a　logistic　regression　model　and　combined　with　the　static　information　to　construct　a　weighted　graph　for　the　OSN.　Finally,　community　detection　is　realized　by　applying　a　modularity　maximization　algorithm.　A　comprehensive　evaluation　using　diverse　real-world　datasets　shows　that　our　proposed　framework　has　superior　performance　over　comparable　ones　in　deriving　high-quality　communities.　Critical　metrics,　such　as　modularity,　average　clustering　coefficient,　and　average　conductance,　are　evaluated　to　demonstrate　the　effectiveness　of　our　approach.　Furthermore,　the　synergy　between　network　topology　and　user　interaction　behavior　in　community　detection　is　also　investigated.　©　2023　Elsevier　B.V.