Online　social　networks　serve　as　platforms　facilitating　communication,　collaboration,　and　information　exchange　among　hackers.　Consequently,　the　detection　and　monitoring　of　hacker　communities　within　these　networks　have　emerged　as　significant　research　areas　in　the　field　of　cybersecurity.　Numerous　methods　have　been　employed　to　detect　these　communities,　with　some　primarily　utilizing　network　structural　information　and　others　incorporating　user-generated　content　topics.　However,　most　existing　methods　rely　on　probabilistic　models　such　as　TF-IDF　or　LDA　to　analyze　textual　similarity,　resulting　in　limited　accuracy.　In　this　paper,　we　introduce　a　Siamese　Attention-augmented　Recurrent　Convolutional　Neural　Network　aimed　at　analyzing　text　similarity　among　users　and　evaluating　the　significance　of　different　users　by　comparing　their　text　similarity　with　seed　users.　Subsequently,　a　weighted　network　is　constructed,　and　community　detection　is　performed　using　a　modularity　maximization　algorithm.　To　validate　the　proposed　framework,　we　collected　Twitter　friendship　data　and　user-generated　content　from　a　pre-existing　regional　hacker　group.　A　comprehensive　series　of　experiments　were　conducted　on　this　dataset.　The　experimental　results　provide　strong　evidence　of　the　effectiveness　of　our　proposed　framework,　showcasing　its　remarkable　performance　across　various　indicators　such　as　community　quality　and　topic　relevance.　Consequently,　our　framework　exhibits　promising　prospects　for　practical　applications　in　the　field　of　hacker　community　detection.　©　2023　SPIE.