<![CDATA[The present study provides a bibliometric analysis of research in viral marketing and identifies its intellectual structure and evolution of topics, along with scholarly impact. Bibliometric data were obtained from a scientific database (e.g., Scopus), which was subsequently analyzed with the application of bibliometric methods, namely, co-occurrence analysis, co-authorship mapping, citation analysis, and visualization of keywords via VOSviewer. As per the findings of the analysis, viral marketing research can be said to have a foundation in computational network science, namely influence maximization, social network analysis, and data-driven diffusion models. In addition, a second stream of research concentrates on behavioral and communicative aspects, such as word-of-mouth, emotional motives behind sharing content online, and user engagement. Finally, there is a clear temporal trend related to a shift from the basic network theory to a greater focus on issues of online platforms and algorithms. It should be noted, however, that there is some thematic contamination in the dataset owing to the inclusion of biomedical literature for the COVID-19 period]]>
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