Using Word2Vec-LDA-Cosine Similarity for Discovering News Dissemination Pattern to Support Government–Citizen Engagement

Evidence of ineffective government–citizen engagement was observed when the Malaysian government decided to make face masks mandatory in public spaces. It is especially critical during a COVID-19 pandemic, where public compliance depends on the speed and clarity at which regulations are announced. Hundreds of arrested cases due to violation were met with confusion and demanded greater clarification. This evidence signifies the need to identify if government-disseminated information is communicated effectively to the citizens through news coverage. Despite this need, current literature has limitations in effectively analysing huge numbers of articles as they mainly employ manual intervention for data news content analysis. Furthermore, there has been no usage of systematic text analytics approaches in government–citizen engagement studies through newspapers. As such, we researched and implemented a modelling framework for discovering how news coverage pattern aligns with government-disseminated information through a case study of COVID-19 in Malaysia during the pandemic. A Word2Vec-LDA-cosine similarity technique was employed in our framework to determine topic similarities as the indication of alignment between news content and government-disseminated information. Our results show that this framework succeeds in capturing the semantics of the corpus to describe news coverage at the same time identified the challenges in general topic comparison tasks.