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Despite the huge potential of social media, its functionality and impact for enhanced risk communication remain unclear. Drawing on dialogic theory by integrating both “speak from power” and “speak to power” measurements, the article aims to propose a systematic framework to address this issue.

The impact of social media on risk communication is measured by the correlation between “speak from power” and “speak to power” levels, where the former primarily spoke to two facets of the risk communication process – rapidness and attentiveness, and the latter was benchmarked against popularity and commitment. The framework was empirically validated with data relating to coronavirus disease (COVID-19) risk communication in 25,024 selected posts on 17 official provincial Weibo accounts in China.

The analysis results suggest the relationship between the “speak from power” and “speak to power” is mixed rather than causality, which confirms that neither the outcome-centric nor the process-centric method alone can render a full picture of government–public interconnectivity. Besides, the proposed interconnectivity matrix reveals that two provinces have evidenced the formation of government–public mutuality, which provides empirical evidence that dialogic relationships could exist in social media during risk communication.

The authors' study proposed a prototype framework that underlines the need that the impact of social media on risk communication should and must be assessed through a combination of process and outcome or interconnectivity. The authors further divide the impact of social media on risk communication into dialogue enabler, “speak from power” booster, “speak to power” channel and mass media alternative.

The purpose of this study is to forecast the development performance of important economies in a smart city using mixed-frequency data.

This study introduces reverse unrestricted mixed-data sampling (RUMIDAS) to support vector regression (SVR) to develop a novel RUMIDAS-SVR model. The RUMIDAS-SVR model was estimated using a quadratic programming problem. The authors then use the novel RUMIDAS-SVR model to forecast the development performance of all high-tech listed companies, an important sector of the economy reflecting the potential and dynamism of urban economic development in Shanghai using the mixed-frequency consumer price index (CPI) producer price index (PPI), and consumer confidence index (CCI) as predictors.

The empirical results show that the established RUMIDAS-SVR is superior to the competing models with regard to mean absolute error (MAE) and root-mean-squared error (RMSE) and multi-source macroeconomic predictors contribute to the development performance forecast of important economies.

Smart city policy makers should create a favourable macroeconomic environment, such as controlling inflation or stabilising prices for companies within the city, and companies within the important city economic sectors should take initiative to shoulder their responsibility to support the construction of the smart city.

This study contributes to smart city monitoring by proposing and developing a new model, RUMIDAS-SVR, to help the construction of smart cities. It also empirically provides strategic insights for smart city stakeholders.