In the Digital Health Communication division, our research is conceptually oriented towards understanding the varying susceptibility of audiences to digital and mass media, particularly from a comparative perspective. We operate on the assumption that media effects depend on a complex interplay of structural, individual, and cultural factors, influencing the likelihood of these effects.

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Our research predominantly focuses on the preference-based reinforcement effects of digital health information. We take into account algorithm-driven micro- and macro-level distinctions. Key areas of interest include the customization of information through narrowcasting, self-confirmation, and the influence of homophilic health information environments, which tend to foster tailored persuasion. We explore various digital content elements and develop new methods for the automatic analysis of digital text and visuals. We monitor users' health information selection in new media environments and integrate algorithmic influences and individual predispositions into our understanding of reinforcing media effects over time and across cultures.

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Within the Digital Health Communication division, we address specific questions such as how new technologies (e.g., AI chatbots, wearables, health trackers), new environments for digital health information (e.g., TikTok, Instagram, YouTube), and advanced data processing possibilities (e.g., AI / artificial intelligence,?machine learning, automation) alter the perception, selection, processing, and impact of health messages.

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We seek to uncover and exploit novel opportunities for medical doctors, health professionals, and the healthcare industry through these developments.