A quantitative approach to estimating bias, favoritism and distortion in scientific journalism

While traditionally not considered part of the scientific method, science communication is increasingly playing a pivotal role in shaping scientific practice. Researchers are now frequently compelled to publicise their findings in response to institutional impact metrics and competitive grant environments. This shift underscores the growing influence of media narratives on both scientific priorities and public perception. In a current trend of personality-driven reporting, we examine patterns in science communication that may indicate biases of different types, towards topics and researchers. We focused and applied our methodology to a corpus of media coverage from three of the most prominent media outlets in the scientific-tech area with the greatest international reach (digital or printed): Wired, Quanta, and New Scientist–spanning the past 5–10 years. By analysing mention distributions, title-level linguistic patterns, and topical emphasis, our objective was to quantify measurable dimensions of bias that may influence perceptions of credibility in science journalism. In doing so, we seek to illuminate the systemic features that shape science communication today and to interrogate their broader implications for epistemic integrity and public accountability in science. We present our results with anonymised journalist names and find evidence of uneven concentration in the visibility of scientists and topics across outlets, consistent with personality-driven patterns of coverage.