Stage distinctive communication networks of the online breast cancer community.

In this study, we reveal the distinctive communication network structures and contents of online breast cancer community posts in accordance with different cancer stages. Using data collected from community.breastcancer.org, a major online breast cancer community (28,139 original posts and 663,748 replies), we traced the communication network structures and contents of replies associated with its severity. By combining network and quantitative content analyses, we deciphered the functions and utilities of health-related online communication. We found an inverse relationship between offline epidemiological prevalence and online communication activation. Despite the relatively small percentage of breast cancer patients, it was found that the more severe the condition of breast cancer, the more active online communication was. We further found that as pathological severity advances, communication networks move from informational exchange to emotional support. The capture of online social networks based on the cancer stage can help unpack the distinctive communication patterns found across different cancer severities. Our results provide insights into a possible online communication intervention design tailored to symptom severity.

Tracking emotions from song lyrics: Analyzing 30 years of K-pop hits.

Emotions that are shared by a large number of people could broadly impact affective experiences at the individual level. Here, we used text mining on popular song lyrics-a cultural product that has been suggested to mirror emotions that many members of a society value and prefer-to track the changes in emotions over time. Morpheme frequency analysis and structural topic modeling on 2,962 hit K-pop songs from 1990 to 2019 showed converging evidence for increased positive emotional content and decreased negative emotional content embedded within the lyrics. This pattern of temporal shift in emotions aligned with rapid changes in South Korea in the past 30 years, notably a rise in individualism and ego orientation in a traditionally collectivistic culture, as well as economic growth. More generally, this study illustrates a strategy for tracking emotions that people value and prefer from large natural language data, supplementing existing methods such as self-reported surveys and laboratory experiments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Online information analysis on pancreatic cancer in Korea using structural topic model.

Inappropriate information on a deadly and rare disease can make people vulnerable to problematic decisions, leading to irreversible bad outcomes. This study explored online information exchanges on pancreatic cancer. We collected 35,596 questions and 83,888 answers related to pancreatic cancer from January 1, 2003 to May 31, 2020, from Naver, the most popular Korean web portal. We also collected 8495 news articles related to pancreatic cancer during the same period. The study methods employed were structural topic modeling, keyword frequency analysis, and qualitative coding of medical professionals. The number of questions and news articles increased over time. In Naver's questions, topics on symptoms and diagnostic tests regarding pancreatic cancer increased in proportion. The news topics on new technologies related to pancreatic cancer from various companies increased as well. The use of words related to back pain-which is not an important early symptom in pancreatic cancer-and biomarker tests using blood increased over time in Naver's questions. Based on 100 question samples related to symptoms and diagnostic tests and an analysis of the threaded answers' appropriateness, there was considerable misinformation and commercialized information in both categories.

A social network analysis of the spread of COVID-19 in South Korea and policy implications.

This study estimates the COVID-19 infection network from actual data and draws on implications for policy and research. Using contact tracing information of 3283 confirmed patients in Seoul metropolitan areas from January 20, 2020 to July 19, 2020, this study created an infection network and analyzed its structural characteristics. The main results are as follows: (i) out-degrees follow an extremely positively skewed distribution; (ii) removing the top nodes on the out-degree significantly decreases the size of the infection network, and (iii) the indicators that express the infectious power of the network change according to governmental measures. Efforts to collect network data and analyze network structures are urgently required for the efficiency of governmental responses to COVID-19. Implications for better use of a metric such as R0 to estimate infection spread are also discussed.

Political Consequences of COVID-19 and Media Framing in South Korea.

This study explored the Korean media's framing of COVID-19 and its impact on people's support for the government. A disaster such as a public health crisis has political consequences. COVID-19 is no exception. However, the direction of the effect is not easily determined. To properly understand this phenomenon, it is necessary to analyze how the media frames the crisis. Using Structural Topic Model, this study examines the Korean media's framing of COVID-19 and especially pays attention to international comparative framing. Based on our analysis results, we argue that expanded framing, which compared the quarantine performance of Korea and other countries, induced a positive change in people's attitudes toward the government, leading to a major political victory for the ruling party in the legislative election. Our research not only identifies the impact of international comparative framing on government support but also contributes to the development of methods for measuring media framing utilizing topic modeling methods.

Funding sources and breast cancer research frame.

This study aims to analyze the formation of the frame of breast cancer research. To test our hypothesis that the research frame depends on the funding sources, we collected the abstracts of 48,448 breast cancer research papers from PubMed and applied structural topic modeling, word network analysis, and LASSO logistic regression to the data. In particular, we analyzed the relationship between funding sources and the molecularization of breast cancer knowledge. The results show that government-funded research is likely to have molecular objects or population as the unit of interest, whereas the research not funded by the government is likely to have individual patients as the unit of interest in relation to specific treatments. This phenomenon is attributed to the different interests of government institutions and the private sector. This study improves our understanding of molecularization and medical knowledge production.

Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis.

BACKGROUND: In case of a population-wide infectious disease outbreak, such as the novel coronavirus disease (COVID-19), people's online activities could significantly affect public concerns and health behaviors due to difficulty in accessing credible information from reliable sources, which in turn causes people to seek necessary information on the web. Therefore, measuring and analyzing online health communication and public sentiment is essential for establishing effective and efficient disease control policies, especially in the early stage of an outbreak. OBJECTIVE: This study aimed to investigate the trends of online health communication, analyze the focus of people's anxiety in the early stages of COVID-19, and evaluate the appropriateness of online information. METHODS: We collected 13,148 questions and 29,040 answers related to COVID-19 from Naver, the most popular Korean web portal (January 20, 2020, to March 2, 2020). Three main methods were used in this study: (1) the structural topic model was used to examine the topics in the online questions; (2) word network analysis was conducted to analyze the focus of people's anxiety and worry in the questions; and (3) two medical doctors assessed the appropriateness of the answers to the questions, which were primarily related to people's anxiety. RESULTS: A total of 50 topics and 6 cohesive topic communities were identified from the questions. Among them, topic community 4 (suspecting COVID-19 infection after developing a particular symptom) accounted for the largest portion of the questions. As the number of confirmed patients increased, the proportion of topics belonging to topic community 4 also increased. Additionally, the prolonged situation led to a slight increase in the proportion of topics related to job issues. People's anxieties and worries were closely related with physical symptoms and self-protection methods. Although relatively appropriate to suspect physical symptoms, a high proportion of answers related to self-protection methods were assessed as misinformation or advertisements. CONCLUSIONS: Search activity for online information regarding the COVID-19 outbreak has been active. Many of the online questions were related to people's anxieties and worries. A considerable portion of corresponding answers had false information or were advertisements. The study results could contribute reference information to various countries that need to monitor public anxiety and provide appropriate information in the early stage of an infectious disease outbreak, including COVID-19. Our research also contributes to developing methods for measuring public opinion and sentiment in an epidemic situation based on natural language data on the internet.

News media's framing of health policy and its implications for government communication: A text mining analysis of news coverage on a policy to expand health insurance coverage in South Korea.

On August 9, 2017, South Korea announced a new measure to expand National Health Insurance (NHI) coverage, which was nicknamed "Mooncare." At the early stage of its implementation, the interpretation of a policy by social actors influences its success and the formation of social conflicts around it. This study sought to identify the strategies for interpreting Mooncare in newspapers and government documents and examine the conflicts between them. Therefore, this study used text mining methods that are well-suited to processing large amounts of natural language data. Findings revealed that, while the conservative newspaper The Chosun Ilbo tended to highlight the financial feasibility of Mooncare, the liberal newspaper The Hankyoreh emphasized the change in rationality of government from the previous administration implied by Mooncare. Additionally, medical newspapers tended to adopt the perspective of healthcare providers and to focus on the changes in the medical system that may threaten them. In contrast, general newspapers tended to adopt the perspective of Mooncare's beneficiaries. Finally, government documents were found to focus on simply introducing the benefits of Mooncare, not responding to the framings of various media. This study identified how various social actors interpreted Mooncare. The results suggest that the government should assume a more active role in the meaning making of the policy.