Exploring sentiment divergence on migrant workers through the lens of Sina Weibo
Article publication date: 1 September 2022
Since the opening of China (aka, reform and opening-up), a great number of rural residents have migrated to large cities in the past 40 years. Such a one-way population inflow to urban areas introduces nontrivial social conflicts between urban natives and migrant workers. This study aims to investigate the most discussed topics about migrant workers on Sina Weibo along with the corresponding sentiment divergence.
An exploratory-descriptive-explanatory research methodology is employed. The study explores the main topics on migrant workers discussed in social media via manual annotation. Subsequently, guided LDA, a semi-supervised topic modeling approach, is applied to describe the overall topical landscape. Finally, the authors verify their theoretical predictions with respect to the sentiment divergence pattern for each topic, using regression analysis.
The study identifies three most discussed topics on migrant workers, namely wage default, employment support and urban/rural development. The regression analysis reveals different diffusion patterns contingent on the nature of each topic. In particular, this study finds a positive association between urban/rural development and the sentiment divergence, while wage default exhibits an opposite relationship with sentiment divergence.
The authors combine unique characteristics of social media with well-established theories of social identity and framing, which are applied more to off-line contexts, to study a unique phenomenon of migrant workers in China. From a practical perspective, the results provide implications for the governance of urbanization-related social conflicts.
The authors thank Maoxin Cui for data provision and college students from Central University of Finance and Economics for manual annotation.
Li, Q., Zuo, Z., Zhang, Y. and Wang, X. (2022), "Exploring sentiment divergence on migrant workers through the lens of Sina Weibo", Internet Research, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/INTR-04-2021-0224
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