What rather than how: A DMR topic modeling analysis of news coverage on the British Museum

Minwoo Kim; Haein Lee; Seon Hong Lee; Jang Hyun Kim

doi:10.1109/BigComp57234.2023.00036

What rather than how: A DMR topic modeling analysis of news coverage on the British Museum

Minwoo Kim
, Haein Lee
, Seon Hong Lee
, Jang Hyun Kim

School of Interdisciplinary Studies

Sungkyunkwan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The British Museum founded in 1753 is one of the largest museums filled with looted artifacts. Countries of origin of cultural properties are demanding the return of the artifacts held by the British Museum, but the museum is refusing their requests. The issues were studied including legal, and historical perspectives. However, there was a lack of research in the field of media or communication. This study introduced the topic distribution of each country's coverage of the British Museum and its cultural assets. Dirichlet Multinomial Regression (DMR) topic modeling method was used to analyze news topics. The country where each article was reported was set as metadata. With the DMR model, 25,744 major news data collected from LexisNexis were investigated. News reports in the United Kingdom (UK) identified a relatively less tendency to deal with the topics of looted artifacts and repatriation. This research confirmed how the country has reported on specific issues by using the DMR method. Additionally, by applying agenda-setting theory, mass media's role in the repatriation issue of the British Museum was discussed.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
Editors	Hyeran Byun, Beng Chin Ooi, Katsumi Tanaka, Sang-Won Lee, Zhixu Li, Akiyo Nadamoto, Giltae Song, Young-guk Ha, Kazutoshi Sumiya, Wu Yuncheng, Hyuk-Yoon Kwon, Takehiro Yamamoto
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	167-173
Number of pages	7
ISBN (Electronic)	9781665475785
DOIs	https://doi.org/10.1109/BigComp57234.2023.00036
State	Published - 2023
Event	2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 - Jeju, Korea, Republic of Duration: 13 Feb 2023 → 16 Feb 2023

Publication series

Name	Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

Conference

Conference	2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023
Country/Territory	Korea, Republic of
City	Jeju
Period	13/02/23 → 16/02/23

Keywords

Agenda-setting theory
Big Data Analysis
British Museum
DMR
Journalism
Topic modeling

Access to Document

10.1109/BigComp57234.2023.00036

Cite this

Kim, M., Lee, H., Lee, S. H., & Kim, J. H. (2023). What rather than how: A DMR topic modeling analysis of news coverage on the British Museum. In H. Byun, B. C. Ooi, K. Tanaka, S.-W. Lee, Z. Li, A. Nadamoto, G. Song, Y. Ha, K. Sumiya, W. Yuncheng, H.-Y. Kwon, & T. Yamamoto (Eds.), Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 (pp. 167-173). (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigComp57234.2023.00036

Kim, Minwoo ; Lee, Haein ; Lee, Seon Hong et al. / What rather than how : A DMR topic modeling analysis of news coverage on the British Museum. Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. editor / Hyeran Byun ; Beng Chin Ooi ; Katsumi Tanaka ; Sang-Won Lee ; Zhixu Li ; Akiyo Nadamoto ; Giltae Song ; Young-guk Ha ; Kazutoshi Sumiya ; Wu Yuncheng ; Hyuk-Yoon Kwon ; Takehiro Yamamoto. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 167-173 (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023).

@inproceedings{3895c85cfd8d424ea4c89af26b2953e2,

title = "What rather than how: A DMR topic modeling analysis of news coverage on the British Museum",

abstract = "The British Museum founded in 1753 is one of the largest museums filled with looted artifacts. Countries of origin of cultural properties are demanding the return of the artifacts held by the British Museum, but the museum is refusing their requests. The issues were studied including legal, and historical perspectives. However, there was a lack of research in the field of media or communication. This study introduced the topic distribution of each country's coverage of the British Museum and its cultural assets. Dirichlet Multinomial Regression (DMR) topic modeling method was used to analyze news topics. The country where each article was reported was set as metadata. With the DMR model, 25,744 major news data collected from LexisNexis were investigated. News reports in the United Kingdom (UK) identified a relatively less tendency to deal with the topics of looted artifacts and repatriation. This research confirmed how the country has reported on specific issues by using the DMR method. Additionally, by applying agenda-setting theory, mass media's role in the repatriation issue of the British Museum was discussed.",

keywords = "Agenda-setting theory, Big Data Analysis, British Museum, DMR, Journalism, Topic modeling",

author = "Minwoo Kim and Haein Lee and Lee, \{Seon Hong\} and Kim, \{Jang Hyun\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 ; Conference date: 13-02-2023 Through 16-02-2023",

year = "2023",

doi = "10.1109/BigComp57234.2023.00036",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "167--173",

editor = "Hyeran Byun and Ooi, \{Beng Chin\} and Katsumi Tanaka and Sang-Won Lee and Zhixu Li and Akiyo Nadamoto and Giltae Song and Young-guk Ha and Kazutoshi Sumiya and Wu Yuncheng and Hyuk-Yoon Kwon and Takehiro Yamamoto",

booktitle = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",

address = "United States",

}

Kim, M, Lee, H, Lee, SH & Kim, JH 2023, What rather than how: A DMR topic modeling analysis of news coverage on the British Museum. in H Byun, BC Ooi, K Tanaka, S-W Lee, Z Li, A Nadamoto, G Song, Y Ha, K Sumiya, W Yuncheng, H-Y Kwon & T Yamamoto (eds), Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023, Institute of Electrical and Electronics Engineers Inc., pp. 167-173, 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023, Jeju, Korea, Republic of, 13/02/23. https://doi.org/10.1109/BigComp57234.2023.00036

What rather than how: A DMR topic modeling analysis of news coverage on the British Museum. / Kim, Minwoo; Lee, Haein; Lee, Seon Hong et al.
Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. ed. / Hyeran Byun; Beng Chin Ooi; Katsumi Tanaka; Sang-Won Lee; Zhixu Li; Akiyo Nadamoto; Giltae Song; Young-guk Ha; Kazutoshi Sumiya; Wu Yuncheng; Hyuk-Yoon Kwon; Takehiro Yamamoto. Institute of Electrical and Electronics Engineers Inc., 2023. p. 167-173 (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - What rather than how

T2 - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

AU - Kim, Minwoo

AU - Lee, Haein

AU - Lee, Seon Hong

AU - Kim, Jang Hyun

PY - 2023

Y1 - 2023

N2 - The British Museum founded in 1753 is one of the largest museums filled with looted artifacts. Countries of origin of cultural properties are demanding the return of the artifacts held by the British Museum, but the museum is refusing their requests. The issues were studied including legal, and historical perspectives. However, there was a lack of research in the field of media or communication. This study introduced the topic distribution of each country's coverage of the British Museum and its cultural assets. Dirichlet Multinomial Regression (DMR) topic modeling method was used to analyze news topics. The country where each article was reported was set as metadata. With the DMR model, 25,744 major news data collected from LexisNexis were investigated. News reports in the United Kingdom (UK) identified a relatively less tendency to deal with the topics of looted artifacts and repatriation. This research confirmed how the country has reported on specific issues by using the DMR method. Additionally, by applying agenda-setting theory, mass media's role in the repatriation issue of the British Museum was discussed.

AB - The British Museum founded in 1753 is one of the largest museums filled with looted artifacts. Countries of origin of cultural properties are demanding the return of the artifacts held by the British Museum, but the museum is refusing their requests. The issues were studied including legal, and historical perspectives. However, there was a lack of research in the field of media or communication. This study introduced the topic distribution of each country's coverage of the British Museum and its cultural assets. Dirichlet Multinomial Regression (DMR) topic modeling method was used to analyze news topics. The country where each article was reported was set as metadata. With the DMR model, 25,744 major news data collected from LexisNexis were investigated. News reports in the United Kingdom (UK) identified a relatively less tendency to deal with the topics of looted artifacts and repatriation. This research confirmed how the country has reported on specific issues by using the DMR method. Additionally, by applying agenda-setting theory, mass media's role in the repatriation issue of the British Museum was discussed.

KW - Agenda-setting theory

KW - Big Data Analysis

KW - British Museum

KW - DMR

KW - Journalism

KW - Topic modeling

UR - https://www.scopus.com/pages/publications/85151543459

U2 - 10.1109/BigComp57234.2023.00036

DO - 10.1109/BigComp57234.2023.00036

M3 - Conference contribution

AN - SCOPUS:85151543459

T3 - Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

SP - 167

EP - 173

BT - Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023

A2 - Byun, Hyeran

A2 - Ooi, Beng Chin

A2 - Tanaka, Katsumi

A2 - Lee, Sang-Won

A2 - Li, Zhixu

A2 - Nadamoto, Akiyo

A2 - Song, Giltae

A2 - Ha, Young-guk

A2 - Sumiya, Kazutoshi

A2 - Yuncheng, Wu

A2 - Kwon, Hyuk-Yoon

A2 - Yamamoto, Takehiro

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 13 February 2023 through 16 February 2023

ER -

Kim M, Lee H, Lee SH, Kim JH. What rather than how: A DMR topic modeling analysis of news coverage on the British Museum. In Byun H, Ooi BC, Tanaka K, Lee SW, Li Z, Nadamoto A, Song G, Ha Y, Sumiya K, Yuncheng W, Kwon HY, Yamamoto T, editors, Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 167-173. (Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023). doi: 10.1109/BigComp57234.2023.00036

What rather than how: A DMR topic modeling analysis of news coverage on the British Museum

Abstract

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Keywords

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