The expansion of digital technologies, new data sources, and computational methods has facilitated the development of Computational Social Science (CSS) and diversified its research approaches. This study evaluates research patterns in CSS by examining scientific collaboration, conceptual and methodological structures, and citation relationships. Using a bibliometric approach, 926 English-language articles indexed in Scopus that explicitly referred to CSS were analyzed through co-authorship, keyword co-occurrence, co-citation, and bibliographic coupling analyses. The findings reveal that the scientific collaboration network consists of multiple, relatively fragmented research communities. At the conceptual level, terms related to social media, big data, machine learning, and natural language processing occupy central positions. Co-citation analysis reveals the field’s connections with journals from various disciplines, while bibliographic coupling highlights the prominence of interdisciplinary journals and those associated with data-driven and computational fields. Overall, the findings demonstrate the prominence of technological concepts and computational methods within the field’s conceptual structure, alongside diverse collaboration patterns and citation relationships. However, these results do not establish a causal relationship between technological advances and changes in research priorities or knowledge production mechanisms; rather, they reveal aspects of research patterns in CSS within the context of technological transformations.