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Why do people comment on government social media - an empirical analysis on China's local governments in Sina Weibo

Xiaodong Feng & Guoyin Jiang

International Journal of Internet and Enterprise Management2019https://doi.org/10.1504/ijiem.2019.103226article
ABDC C
Weight
0.40

What the paper says

Government microblog is a new way to provide public service and communicate with public. The engagement is an important guarantee to realise the effects of government microblog platform. This research builds an interest-driven behaviour model to explain the micro-level public engagement behaviour on specific posts published on government microblogs. Specifically, we focus on how the individual's characteristic, features of posts and historical personal interest recorded on the web influence him or her selecting specific post to interact with, and it is verified by the data from Sina Weibo. Results show that the interest match has significant positive influence on personal engagement, and the interest match of posts by higher administrative level-government officials is lower than by lower administrative level-officials. Also, posts with rich information will be more likely to receive more engagement. These findings would help government officials to promote more engagement with public.

4 citations

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https://doi.org/https://doi.org/10.1504/ijiem.2019.103226

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@article{xiaodong2019,
  title        = {{Why do people comment on government social media - an empirical analysis on China's local governments in Sina Weibo}},
  author       = {Xiaodong Feng & Guoyin Jiang},
  journal      = {International Journal of Internet and Enterprise Management},
  year         = {2019},
  doi          = {https://doi.org/https://doi.org/10.1504/ijiem.2019.103226},
}

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Why do people comment on government social media - an empirical analysis on China's local governments in Sina Weibo

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Evidence weight

0.40

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.13 × 0.4 = 0.05
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.