Analytical framework for evaluating digital diplomacy using network analysis and topic modeling: Comparing South Korea and Japan

Park, Sejung; Dahoon, Chung; Park, Han Woo

doi:10.1016/j.ipm.2018.10.021

Cited by 43 publications

(21 citation statements)

References 34 publications

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“…Twitter users are compiled by subgroup using the Clauset-Newman-Moore cluster algorithm and visualized using the Harel-Koren Fast Multiscale layout algorithm [32]. This method has been widely used in communication research on information measurement [33,34]. The study uses NodeXL to calculate various measures quantifying the efficiency of information diffusion and visualizing the structural topography of the network associated with the results of the four search queries as seeds.…”

Section: Social Media Network Analysis and News Channel Classificationmentioning

confidence: 99%

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

Park¹,

Park²,

Chong³

2020

J Med Internet Res

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231

200

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Background SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations. Objective Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak. Methods Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources. Results The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word “Coronavirus” communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers’ attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852). Conclusions Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

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Section: Social Media Network Analysis and News Channel Classificationmentioning

confidence: 99%

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

Park¹,

Park²,

Chong³

2020

J Med Internet Res

Self Cite

231

200

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“…Men thinking conferences are a free pass" (Topic 31, Table 1). This topic model has been used for different applications such as health (Shaw & Karami, 2017;Zhu, Kim, Banerjee, Deferio, Alexopoulos, & Pathak, 2018;Karami, Dahl, Turner--McGrievy, Kharrazi, & Shaw, 2018;Karami & Shaw, 2019), e--petition (Hagen, 2018), politics (Park, Chung, & Park, 2019;Karami, Bennett, & He, 2018;Karami & Elkouri, 2019), opinion mining (Ma, Zhang, Liu, Li, & Yuan, 2016), disaster management (Karami Shah, Vaezi, & Bansal, 2019) business (Amado, Cortez, Rita, & Moro, 2018;Karami & Pendergraft, 2018), social media analysis , automatic summarization of changes in dynamic text collections (Kar, Nunes & Ribeiro, 2015), spam detection (Karami & Zhou, 2014), and systematic literature review (Wang, Ding, Zhao, Huang, Perkins, Zou, & Chen, 2016;Altena, Moerland, Zwinderman, & Olabarriaga, 2016;Shin et al, 2019). We utilized LDA in this research to achieve a deeper semantic layer in the academic sexual harassment corpus.…”

Section: Text Miningmentioning

confidence: 99%

Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining

Karami¹,

White

Ford

et al. 2020

Information Processing & Management

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Sexual harassment in academia is often a hidden problem because victims are usually reluctant to report their experiences. Recently, a web survey was developed to provide an opportunity to share thousands of sexual harassment experiences in academia. Using an efficient approach, this study collected and investigated more than 2,000 sexual harassment experiences to better understand these unwanted advances in higher education. This paper utilized text mining to disclose hidden topics and explore their weight across three variables: harasser gender, institution type, and victim's field of study. We mapped the topics on five themes drawn from the sexual harassment literature and found that more than 50% of the topics were assigned to the unwanted sexual attention theme. Fourteen percent of the topics were in the gender harassment theme, in which insulting, sexist, or degrading comments or behavior was directed towards women. Five percent of the topics involved sexual coercion (a benefit is offered in exchange for sexual favors), 5% involved sex discrimination, and 7% of the topics discussed retaliation against the victim for reporting the harassment, or for simply not complying with the harasser. Findings highlight the power differential between faculty and students, and the toll on students when professors abuse their power. While some topics did differ based on type of institution, there were no differences between the topics based on gender of harasser or field of study. This research can be beneficial to researchers in further investigation of this paper's dataset, and to policymakers in improving existing policies to create a safe and supportive environment in academia.

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“…In this study, we consider twitter accounts of each cryptocurrency as nodes and the relationship between twitter activities such as following, replying, co-tweeting as ties (Xu & Feng, 2015). Indicators and visualizations based on an SNA are particularly useful for identifying the structural pattern of the online social activities (Otte & Rousseau, 2002;Park, Jeong, & Park, 2019;Song, Jung, Kim, & Park, 2019). Among the various structural properties of social network, we particularly focus on density, which is the total number of ties divided by the total number of possible ties (Hanneman & Riddle, 2005).…”

Section: Methods and Datamentioning

confidence: 99%

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

Park

Lee

2019

Drustvena istrazivanja

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Cryptocurrencies have embraced Twitter as a major channel of communication. Employing social network analysis and sentiment analysis, this study investigates the Twitter-mediated communication behaviors among cryptocurrencies. This study determines whether a significant association exists between cryptocurrencies' Twitter networks and their credit scores. Data were drawn from the Twitter pages of several top cryptocurrencies. The results indicate that reply-mention networks had the densest structure, that the following-follower network structure was correlated with the reply-mention structure, and that the reply-mention and co-tweet networks were positively correlated. The results also indicate that cryptocurrencies' active networking strategies affected their credit scores and more importantly, that cryptocurrencies frequently linked with fellow currencies tended to have high credit scores.

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Analytical framework for evaluating digital diplomacy using network analysis and topic modeling: Comparing South Korea and Japan

Cited by 43 publications

References 34 publications

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

Unwanted advances in higher education:Uncovering sexual harassment experiences in academia with text mining

How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis

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