Large language models (LLMs) have significantly enhanced the fluency, consistency, and adaptability of social bots, raising new concerns about their ability to integrate into online communities. However, community integration requires more than text generation alone. Social bots often lack a systematic understanding of community culture, struggle to maintain consistency between persona settings and posting behavior, and have difficulty identifying users with higher interaction potential. To address these challenges, this paper proposes HSEF-CI, a human-like social bot enhancement framework for community integration. The framework constructs community profiles from target communities, reshapes bot identities and long-term memory, adopts a staged text generation workflow, and selects interaction targets via homophily-based matching. Experiments on multiple English-speaking communities show that the framework lowers detectability across several detectors, improves social bots’ ability to integrate into target communities, and increases users’ willingness to interact. These findings highlight the importance of jointly modeling community profiles, identity reshaping, adaptive text generation, and target selection in studying LLM-driven social bots for community integration. The proposed framework also helps reveal how social bots adapt to and integrate into online communities, and provides an empirical baseline for the future development of detectors targeting community integration behaviors.
Zhang et al. (Fri,) studied this question.