Resumen
Evidence-based policy seeks to use evidence in public policy in a systematic way in a bid to improve decision-making quality. Evidence-based policy cannot work properly and achieve the expected results without accurate, appropriate, and sufficient evidence. Given the prevalence of social media and intense user engagement, the question to ask is whether the data on social media can be used as evidence in the policy-making process. The question gives rise to the debate on what characteristics of data should be considered as evidence. Despite the numerous research studies carried out on social media analysis or policy-making, this domain has not been dealt with through an ?evidence detection? lens. Thus, this study addresses the gap in the literature on how to analyze the big text data produced by social media and how to use it for policy-making based on evidence detection. The present paper seeks to fill the gap by developing and offering a model that can help policy-makers to distinguish ?evidence? from ?non-evidence?. To do so, in the first phase of the study, the researchers elicited the characteristics of the ?evidence? by conducting a thematic analysis of semi-structured interviews with experts and policy-makers. In the second phase, the developed model was tested against 6-month data elicited from Twitter accounts. The experimental results show that the evidence detection model performed better with decision tree (DT) than the other algorithms. Decision tree (DT) outperformed the other algorithms by an 85.9% accuracy score. This study shows how the model managed to fulfill the aim of the present study, which was detecting Twitter posts that can be used as evidence. This study contributes to the body of knowledge by exploring novel models of text processing and offering an efficient method for analyzing big text data. The practical implication of the study also lies in its efficiency and ease of use, which offers the required evidence for policy-makers.