Social impact in social media: A new method to evaluate the social impact of research

Abstract

The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.

A New Application of Social Impact in Social Media for Overcoming Fake News in Health

“…If we analyzed the total amount of data collected, we obtained the following SICOR for each social media channel selected, as shown in Table 5. The SICOR calculation [2] is a ratio that calculates the percentage of ESISM found in the full sample selected. In this case, the SICOR is the percentage of tweets with evidence of social impact in relation to all the tweets collected; the same applied to Facebook posts and comments and subreddit comments.…”

“…The method used and the sample selection are described in detail in the following sections. The method used is social impact in social media (SISM) methodology [2], which combines quantitative and qualitative content analysis of the sample selected considering the contributions of the social impact of the research [45]. According to Elo et al [46], there are trustworthiness issues in the preparation phases of the data collection method, the sampling strategy and the selection of a suitable unit of analysis according to the way in which the research goals are defined.…”

“…The main aim of this work is to contribute to advancing methods of overcoming misinformation or fake news in health through social media data analysis. In this sense, we have applied the methodology of social impact in social media (SISM) [2] to identify which type of interactions spread misinformation and which type of interactions contribute to overcoming fake news or misinformation related to health. The main research questions are as follows: RQ1) How are social media messages focused on fake health information or misinformation?…”

“…Researchers apply a classification of messages according to the codebook (see Table 3) and interactions received. The social impact coverage ratio (SICOR) will be applied for each source of social media selected, which identifies "the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found" [2]. The elaborated codebook has four categories defined a priori as a result of the literature review performed.…”

“…The advancements in the methodologies related to social media analysis provide new insights to unveil how citizens share health information and the ways in which fake news influences public health. Social impact in social media (SISM) constitutes a novel methodology in both social media analytics and the evaluation of the social impact of research [2]. This article applies the SISM methodology to the specific case of fake news in health to identify the type of interactions related to the information shared in social media.…”