Evidence map›Paper›PMID 40208870›Full record

ArticlePloS one2025

A framework for visualizing and describing city image promotion short video data based on microcube model.

Jing He, Zhuoluo Yang, Jiayi Zhu

Abstract read
In one paragraph

Article in PloS one, 2025. 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

3 authors.

Jing HeInstitute for Advanced Studies in Humanities and Social Science, Beihang University, Beijing, China.ORCID 0009-0007-8408-2435
Zhuoluo YangState Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China.
Jiayi ZhuInstitute for Advanced Studies in Humanities and Social Science, Beihang University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe rapid development of media technology and media environment provides rich resources and convenient ways to shape the image of the city. Short video has become an important help to shape the image of the city and build the city brand. How to use short video to shape the image of the city is the key link of urban construction. This study focuses on six primary variables for analyzing city image short videos: unexpected events, emotional resonance, scene transition, elemental amplification, element interaction, and screen style. These variables were selected based on their demonstrated impact on short video engagement and dissemination efficiency. HOW: In order to realize comprehensive analysis of short video content, This study collected 20,668 video screenshots from the Douyin platform as data samples. Data collection spanned July 2019 to December 2019, and analysis was conducted using the Python programming language with the Pandas and Matplotlib libraries for data processing and visualization.

objectiveTo reveal the relationship between video content features and popularity by quantitative and visual methods, and to provide reference for optimizing urban brand promotion strategies.

conclusion(1) The microcube model shows strong flexibility and applicability in short video content analysis, and can help reveal the complex relationship between short video content characteristics and communication effect. (2) Unexpected events and emotional resonance are key factors in the attractiveness and communication effect of short videos. Reasonable design of scene switching and element interaction significantly enhances the visual impact of short videos.

Indexed as

Video RecordingCitiesHumans

Identifiers

PMID40208870
PMCPMC11984742

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.