SOLUTION JOURNALISM AND THE DEVELOPMENT OF MEDIA QUALITY ASSESSMENT TOOLS: A CASE STUDY OF HOUSEHOLD ECONOMIC

Abstract

The COVID-19 pandemic has had a severe economic impact, particularly at the household level in Thailand. Thai PBS, as a public media organization, prioritized "household economy" as its main news agenda in 2021 to address public concerns and propose solutions for affected communities. This research aims to analyze Thai PBS's news reporting strategies on household economic issues and develop a quality media monitoring tool to evaluate organizational performance.

A content analysis was conducted on 3,996 news items across three platforms—television, website, and social media—using the Inform–Explain–Solution framework. Findings revealed that 68.9% of the content focused on solutions, 19.3% on explanation, and 6.5% on information delivery. The solution-oriented reports frequently employed actionable case studies, enabling audiences to better understand problems, visualize problem-solving processes, and adapt in terms of livelihood and income. The results also reflect the principles of explanatory journalism by presenting information from multiple sources and providing in-depth context, thereby fostering rational understanding and enabling the public to respond effectively to crises.

Furthermore, this study developed a Machine Learning (ML) system in collaboration with NECTEC using the “Inform–Explain–Solution” framework to label 6,121 news items for automated classification, achieving an accuracy rate of 79%. This work represents a pioneering integration of qualitative and quantitative content analysis with ML technology to enhance editorial decision-making, create standardized datasets, and provide a systematic approach for evaluating public service news quality. The findings underscore the role of Thai PBS in promoting public understanding and supporting effective social problem-solving in the post-pandemic era.