From data to intelligence: A review of recent technological and theoretical advances in computational communication science

Against the backdrop of a platform society and the rise of multimodal content, communication processes are increasingly mediated by algorithms, while digital traces continue to expand in scale and granularity. Building on the 2009 and 2020 “computational social science” initiatives as an intellectual lineage, open science and testable evidence have gradually become field-wide norms. Building on prior work, this study traces a “actors–content–platforms/algorithms–audience–effects” pathway to clarify what computational communication studies and what its central questions are, and to review how artificial intelligence is used across the 5W elements of the pathway. We further review international institutionalization driven by scholarly societies and research centers, alongside the rapid growth of the Chinese scholarly community. Using government governance, journalism, and emergency management as illustrative scenarios, we outline evaluable application pathways and, grounded in the baseline of “comparability, reproducibility, and auditability”, argue for positioning norms and ethics as “research infrastructure”. Concretely, this includes building secure “data and compute” spaces, a platform-behavior observatory, and open evaluation and replication services, complemented by cross-disciplinary curricula and organizational mechanisms. Overall, in the intelligent era, computational communication science should shift from “demonstrable technologies” to “verifiable evidence”, progressively establishing a high-quality evidence production system that supports national governance and industry practice.