Analysis of the Cultural Cognition of Manchurian Regime Architectural Heritage via Online Ethnography Data

As tangible relics of modern colonial history, Manchurian regime (Manchukuo) architecture of Changchun possesses both historical commemorative value and tourism and cultural functions. Public perception and sentiment regarding this heritage in the contemporary social media context are key dimensions for evaluating the effectiveness of cultural regeneration. Existing research on Manchurian regime architecture has focused primarily on historical research and architectural form analysis, with limited research examining the diverse public interpretations of its cultural value through multi-platform social media data. This study aims to systematically explore the public’s cognitive characteristics, sentimental attitudes, and themes of interest regarding Changchun’s Manchurian regime architecture using online ethnographic data, providing empirical support for optimizing cultural regeneration pathways for Manchurian regime architectural heritage. The study collected data from 1 January 2020 to 20 September 2025, using the keyword “Changchun Manchurian regime architecture”. Using Python crawlers, the study extracted 334 original videos and 18,156 related comments from Douyin, Ctrip, and Dianping. The analysis was conducted using word frequency statistics, SnowNLP sentiment analysis, LDA topic modeling, and multidimensional visualization. The study found that (1) word frequency statistics show that the public has multiple concerns about the historical symbols, geographical positioning, cultural and tourism functions, and national emotions of Manchurian regime architecture; (2) SnowNLP analysis shows that positive comments account for 71%, neutral comments account for 11%, and negative comments account for 18%; (3) the optimal number of topics was determined to be five through perplexity and consistency indicators, namely “historical narrative and imperial power symbols”, “emotional experience and historical reflection”, “visit experience and service facilities”, “site distribution and regional space”, and “explanation and tour evaluation”; (4) the corpus can be divided into five time period stages, namely S1 (2020)–S5 (2024–2025), reflecting the shift in public attention from “space-facilities” to in-depth reflection on “emotion-history”.

Authors

Institutions

Publication Details

Journal
Buildings
Published
2025-10-29
DOI
https://doi.org/10.3390/buildings15213912
Primary Topic
Cultural Heritage Management and Preservation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of the Cultural Cognition of Manchurian Regime Architectural Heritage via Online Ethnography Data

Junxin Song, Bailang Jing, Liang Zheng, Junkai Chen et al.
Buildings
Cultural Heritage Management and Preservation
article

Analysis of the Cultural Cognition of Manchurian Regime Architectural Heritage via Online Ethnography Data

Junxin Song, Bailang Jing, Liang Zheng, Junkai Chen, Yile Chen, Shanshan Zhang, Liwei Zhang
article en

Abstract

As tangible relics of modern colonial history, Manchurian regime (Manchukuo) architecture of Changchun possesses both historical commemorative value and tourism and cultural functions. Public perception and sentiment regarding this heritage in the contemporary social media context are key dimensions for evaluating the effectiveness of cultural regeneration. Existing research on Manchurian regime architecture has focused primarily on historical research and architectural form analysis, with limited research examining the diverse public interpretations of its cultural value through multi-platform social media data. This study aims to systematically explore the public’s cognitive characteristics, sentimental attitudes, and themes of interest regarding Changchun’s Manchurian regime architecture using online ethnographic data, providing empirical support for optimizing cultural regeneration pathways for Manchurian regime architectural heritage. The study collected data from 1 January 2020 to 20 September 2025, using the keyword “Changchun Manchurian regime architecture”. Using Python crawlers, the study extracted 334 original videos and 18,156 related comments from Douyin, Ctrip, and Dianping. The analysis was conducted using word frequency statistics, SnowNLP sentiment analysis, LDA topic modeling, and multidimensional visualization. The study found that (1) word frequency statistics show that the public has multiple concerns about the historical symbols, geographical positioning, cultural and tourism functions, and national emotions of Manchurian regime architecture; (2) SnowNLP analysis shows that positive comments account for 71%, neutral comments account for 11%, and negative comments account for 18%; (3) the optimal number of topics was determined to be five through perplexity and consistency indicators, namely “historical narrative and imperial power symbols”, “emotional experience and historical reflection”, “visit experience and service facilities”, “site distribution and regional space”, and “explanation and tour evaluation”; (4) the corpus can be divided into five time period stages, namely S1 (2020)–S5 (2024–2025), reflecting the shift in public attention from “space-facilities” to in-depth reflection on “emotion-history”.

BuildingsVol. 15(21)
Macau University of Science and Technology (MO), Jilin Province Science and Technology Department (CN), East Asia Research (SG), PLA Air Force Aviation University (CN), Changchun University (CN), City University of Macau (MO)
Openalex Percentile: Top 68%
Cultural Heritage Management and Preservation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.