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Opinion polarisation in social media has recently become a significant issue. The existing literature mainly attributes polarisation to online friends' informational social influence, that is, users are more likely to interact with others with similar opinions, which leads to the echo chamber effect. However, the impact of social interaction on individual polarisation may also result from normative social influence, which varies with social settings on the platform. In this paper, we leverage a quasi‐experiment to investigate the normative social influence of online friends on focal users' review polarity. We use fixed effects and difference‐in‐differences approaches, along with propensity score matching, to address the potential endogeneity in users' friend function adoption decisions. Our results indicate that adopting the friend function leads users to post less extreme ratings. We further separate the reviews into positive and negative, finding that the reduction in the review polarity for positive reviews is more prominent than for negative ones. Regarding user heterogeneity, our causal forest analysis uncovers that users with a higher engagement level on the platform are less affected by adopting the friend function than those with less engagement. Our study has clear implications for managers and platform designers, highlighting the importance of social function design in reducing social media induced polarisation.

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Inclusive green growth efficiency (IGGE) analysis is an effective tool for improving coordinated economic, social, and environmental development. This study incorporated the game cross-efficiency DEA to measure the IGGE of 30 provinces in China. Then, the modified spatial gravity model and social network analysis model were applied to construct and analyze the spatial correlation network structure of the IGGE. The quadratic assignment procedure was used to mine the influencing factors that affect the formation and evolution of the spatial correlation network of the IGGE. The results are as follows. (1) During the study period, there were significant differences in the IGGE among the 31 provinces, among which the eastern provinces were higher than the central and western provinces. (2) The spatial correlation of the IGGE presented a complex and multi-threaded network structure, indicating that the IGGE has a noticeable cross-regional spillover effect. Beijing, Tianjin, Zhejiang, Shanghai, Jiangsu, and Guangdong played the role of the “net spillover” block. Qinghai, Guizhou, Guangxi, and the surrounding provinces played the role of the “primary beneficial”. The Yangtze delta and Pearl River Delta economic zone (primarily including Shanghai and Guangdong) acted as a “bridge” to the Yunnan–Guizhou region and the surrounding provinces. (3) The spatial adjacency, degree of openness, economic development, and environmental governance were the prominent factors influencing the formation and evolution of the IGGE spatial correlation network. This work provides an example of constructing an IGGE correlation network while considering various factors, such as the economy, population, and distance. It also could help policymakers clarify the IGGE spatial correlation pattern and the provinces’ roles and potential for IGGE synergic improvement.