مقایسه گفتمان عمومی و علمی در زمینه سیستم های نوآوری  Comparing public and scientific discourse in the context of innovation systems

1. Introduction Insights in innovation systems and their dynamics and architecture are relevant for future planning due to the close link between foresight, policy planning, and the performance of innovation systems (Alkemade et al., 2007). Therefore, an in-depth analysis of current developments is crucial for capturing the state-of-the-art as a starting point to build future assumptions and strategies. For the long-term observation of thematic and technical developments, an analysis should not only address one area of the innovation system (i.e., science) but should consider further parts (i.e., society). Innovation as a systemic process is not only driven by science and technology but has diverse sources. While there are (numeric) indicators to map S&T developments such as patents, publications or standards, new indicators are required to map other areas of the innovation system. In this regard, one option is the examination of news reporting to map societal discourse. News is a recognized channel for innovation diffusion and plays an important role in informing society. Based on the current literature on innovation systems, this article proceeds with the assumption of media being a central actor, enabling a public sphere where innovation discourses can take place. Thus, apart from its role in science, policy, and the economy, media should be considered in terms of its societal functions and role in the spread of innovation. To contrast changes and developments in science and society, specifically the link between the two is addressed in this article by comparing the content of news articles and scientific publications. Publication data is a commonly used source for examining scientific progress (e.g., Leydesdorff and Milojević, 2015). This work will explore if it is possible to (automatically) recognize changes and focus in reporting of both, science and news. This potentially enables inferences on the state of a technology or its societal acceptance for the comparison of subsystems. To process the volume of textual data according to a common analytical scheme, a text mining framework has been developed. Currently, there is no methodology for the (automatic) comparison of news articles and scientific publications but theoretical discussions (e.g., Franzen et al., 2012).