Modeling Science Journalism Policy in Social Media Using Interpretive Structural Modeling

Science journalism on social media requires a clear framework and codified rules that can be achieved through scientific policy-making in this area. Given the deep research gap in this field, the present study attempts to model the science journalism policy on social media. This research is based on the purpose of an applied-developmental research and based on the data collection method, it is a descriptive research that has been done by cross-sectional survey method. The statistical population of the research includes theoretical experts (university professors) and experimental experts (experienced journalists). Sampling was performed by purposive method and after 20 specialized interviews, theoretical saturation was obtained. Content analysis and MaxQDA 20 software were used to identify the components of science journalism policy in social media. Structural-interpretive modeling method and MicMac software were used to determine the relationships between the components. Research findings indicate that strengthening the infrastructure and formulating the perspectives of science journalism affects the education and promotion of science journalism and leads to the empowerment of this field. Through empowerment, it is possible to produce content tailored to the general needs and increase the oversight role and demand of science journalists, and consequently, the oversight of social media. Social media can also enhance communication and trust between the scientific community and the public, and increase academics' participation and engagement with science journalists. These factors ultimately lead to the policy journalism of science on social media.