Evidence map›Paper›PMID 35005717›Full record

ArticleComputational urban science2022

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China.

Xin Xiao, Chaoyang Fang, Hui Lin, Li Liu, Ya Tian, Qinghua He

Abstract read
In one paragraph

Article in Computational urban science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xin XiaoSchool of Geography and Environment, Jiangxi Normal University, Nanchang, 330022 China.ORCID 0000-0001-8859-7240
Chaoyang FangSchool of Geography and Environment, Jiangxi Normal University, Nanchang, 330022 China.
Hui LinSchool of Geography and Environment, Jiangxi Normal University, Nanchang, 330022 China.
Li LiuSchool of Geography and Environment, Jiangxi Normal University, Nanchang, 330022 China.
Ya TianSchool of Geography and Environment, Jiangxi Normal University, Nanchang, 330022 China.
Qinghua HeSchool of Geography and Environment, Jiangxi Normal University, Nanchang, 330022 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo's overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

Indexed as

ESTDASocial mediaUrban emotionsUser-generated contentWord shift graph

Identifiers

PMID35005717
PMCPMC8724235

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.