Word Cloud Analysis Research Articles

도시 국제화와 지속가능한 미래: 부산 세계도시포럼(WUF) 추진 사례*

Internationalization of cities has been reportedly discussed to be important for sustainable urban development. World Urban Forum (WUF) has been held in every two years to discuss such contemporary urban agendas as local communities, economy, environment and culture by UN Habitat. This article aims to review the validity and necessity of Busan’s hosting plans for 13th WUF and propose desirable strategies and action plans. In doing so, it investigates agendas of the first to 11th WUF meeting and analyzes urban policy tasks, which global cities confront, from the perspective of the sustainable development goals, with methods of qualitative content analysis, focused-group interview, and word cloud analysis. From the analyses, this study demonstrates desirable and rational strategies to host and hold 13th WUF, particularly in connection with 2030 Busan World Expo, and more importantly announce internationalization strategies of cities towrad sustainable urban future.

Social Network Analysis of Coffee Shop Visitors Using Twitter Data

The concept of the present and the sense of comfort offered by the coffee shop bring in various visitors in terms of age, children, teenagers, adults, and even the elderly. Visitors in terms of work, both from junior high school, high school/vocational school students, university students, private employees, civil servants, and from various other types of work are happy to visit. The purpose of this study was to conduct a social network analysis of coffee shop visitors in Indonesia. The postmodern concept of consumption is related to the realization that consumption appears more significant at sign value or symbolic qualities than use value. The research method used is qualitative netnography. The data is obtained from uploads on the Twitter application with the help of Atlas. ti application. Because it uses network analysis, tweets that are not related to other tweets will be excluded from the study. The results showed that Twitter users went to a coffee shop to do work while drinking coffee; conduct watching events together, either football, moto GP, or other similar matches; perform social activities with the closest people, and want to eat their favorite food. The results of the word cloud analysis obtained several words that appear and are often used by coffee shop visitors on Twitter applications such as warkop, nongkrong, ngopi, kopi, ngobrol, kerja, makan, teh, lagu, nongki, and many other words. The novelty of this study lies in the approach used, namely netnography so that the data obtained reaches a wider range of data.

An Interactive Live and Online Cooking Program for Children in Vulnerable Families-An Exploratory Study.

The COVID-19 pandemic has highlighted the importance of technology for communication and social interactions. Especially for children in low-income families-a vulnerable population suffering from health and digital disparities-the situation worsened during the pandemic. Earlier studies in times of COVID-19 suggested that the children in Korea who usually do homework and dine at community childcare centers (CCCCs, free after-school care places) need to learn more about how to eat healthily and how to interact with others using digital technology. Therefore, to reduce these children's health and digital inequalities, an interactive live and online cooking program was developed and provided to 313 children and 95 staff members at the 29 CCCCs located in the southern provinces in South Korea. The aim of the current study was to explore the experiences of children and staff with the program. After surveying their experiences, a high degree of satisfaction was found (children: 3.60 ± 0.10; staff: 3.63 ± 0.08 points out of 4.00). Aspects that needed improvement in the program were related to (in)experience in online technology, the frequency and timing of the cooking classes, and the communication between the centers and (online) chefs. In addition, in a word cloud analysis, terms such as 'fun', 'delicious', and 'want' were highlighted for children, and terms such as 'participating' and 'preparation' dominantly appeared for the staff. In the analysis of negative experiences, terms related to environmental factors such as 'sound', 'hear', and 'voice' were highlighted. This novel but preliminary approach for children from low-income families, by integrating cooking with digital technology, indicates that with enough digital support, the CCCCs are a promising platform to promote healthy eating and digital literacy. Optimizing and disseminating these strategies during this pandemic period, and future pandemics, could be beneficial to keep children in their communities healthy, and ultimately reduce socioeconomic health disparities.

The CHST11 gene is linked to lung cancer and pulmonary fibrosis.

The abnormal modification of chondroitin sulfate is one of the leading causes of disease, including cancer progression. During chondroitin sulfate biosynthesis, the CHST11 enzyme plays a vital role in its modification, but its role in cancer is not fully understood. Therefore, understanding the relationship between CHST11 and pulmonary-related diseases through clinically relevant information may be useful for diagnosis or treatment. A variety of pulmonary fibrosis clinical gene expression omnibus (GEO) datasets were used to assess the association between CHST11-related manifestations and fibrosis. Multiple lung cancer-related databases, including The Cancer Genome Atlas, GEO datasets, UCSC Xena, GEPIA2, Cbioportal and ingenuity pathway analysis were used to evaluate the clinical correlation between CHST11 and lung cancer and potential molecular mechanisms. For drug repurposing prediction, the molecules that correlated with CHST11 were subjected to the LINCS L1000 algorithm. A variety of in vitro assays were performed to evaluate the in-silico models, including RNA and protein expression, proliferation, migration and invasion. Clinical analyses indicate that the levels of CHST11 are significantly elevated in cases of pulmonary-related diseases, including fibrosis and lung cancer. According to multiple lung cancer cohorts, CHST11 is the only member of the carbohydrate sulfotransferase family associated with overall survival for lung adenocarcinomas, and it is highly related to smoking-induced lung cancer patients. Based on the results of in vitro experiments, CHST11 expression contributes to tumor malignancy and promotes multiple fibrotic activators. Correlation-based ingenuity pathway analysis indicated that CHST11-related molecules contributed to pulmonary fibrosis or lung adenocarcinomas via similar upstream stimulators. Based on known molecular regulatory relationships, CHST11 has been associated with the regulation of TGF-β and INFγ as important molecules contributing to fibrosis and cancer progression. Interestingly, WordCloud analysis revealed that CHST11-related molecules are involved in regulation primarily by integrin signaling, and these relationships were consistently reflected in the analysis of cell lines and the clinical correlation. A CHST11 signature-based drug repurposing analysis demonstrated that the CHST11/integrin axis could be targeted by AG-1478 (Tyrphostin AG 1478), brefeldin A, geldanamycin and importazole. This study provides the first demonstration that CHST11 may be used as a biomarker for pulmonary fibrosis or lung cancer, and the levels of CHST11 were increased by TGF-β and INFγ. The molecular simulation analyses demonstrate that the CHST11/integrin axis is a potential therapeutic target for treating lung cancer.

초등교원 양성대학의 학습자 중심 교육과정 설계 방향 탐색 연구

Objectives The purposes of this study were to analyze the educational purpose, goal, vision, and current status of the curriculum of the elementary school teacher training college, based on this, to explore the learner-centered curriculum design direction of the elementary school teacher training college. Methods In order to achieve this objective, the researcher analyzed the educational purpose, goal, vision and curriculum status of 10 elementary school teacher training colleges in Korea, literature review, and then extracted keywords using the word cloud analysis method. Based on the extracted keywords and the results of literature review, the learner-centered curriculum design direction of the elementary school teacher training college was derived. Results The learner-centered curriculum design direction of the elementary school teacher training college explored through the research process is as follows. ① re-establishment of curriculum document system for elementary school teachers training college, ② elementary school teacher training college curriculum design based on core competency, ③ learner participation as the main body of curriculum development for elementary school teachers training college, ④ the intrinsic value and specificity of elementary school teacher training college curriculum balance design Conclusions Through these research results, it was concluded that the learner-centered curriculum design direction of the elementary school teacher training college should be a holistic direction that can simultaneously include the changes of the times and the essential value of the elementary school teacher training college. In addition, it is meaningful in that it provided a direction for the problem situation faced by elementary school teachers, and provided a theoretical basis for the development direction and design of the curriculum of the elementary school teacher training college.

마리나에 대한 사회적 이슈 분석: 토픽모델링을 활용한 국내 언론 보도기사 분석을 중심으로

본 연구에서는 마리나 산업의 과거와 현재를 조망하고 이에 따른 마리나 산업의 발전방안을 제시하기 위해 텍스트 마이닝을 활용하였다. "마리나"를 키워드로 언론 보도기사를 추출했으며, 이를 대상으로 텍스트 마이닝 기법을 활용해 TF분석, 워드 클라우드 분석, TF-IDF 분석, 토픽 모델링 분석을 실시하였다. 이를 통해 도출된 연구결과는 다음과 같다. 첫째, TF 분석을 통해 ‘사업’, ‘마리나’, ‘해양’ 등 상위 60개의 키워드를 제시하였고, 상위 키워드 500개를 기준으로 워드 클라우드 분석을 통해 시각화하였다. 둘째, TF-IDF 분석을 통해 시기별 마리나의 사회적인 트렌드를 알아보았다. 셋째, 토픽모델링 분석을 통해 ‘마리나 정책’, ‘관광’, ‘부동산 개발’, ‘마리나 투자유치’, ‘해양레저산업’, ‘복합 마리나 개발’, ‘호텔 및 위락시설’, ‘해외기업의 국내투자’로 명명한 8개의 토픽을 도출하였다. 넷째, 시기별 토픽모델링 분석을 통해 토픽들의 사회적 이슈와 변화를 분석하여 마리나 산업의 과거와 현재를 조망하였다. 이상의 결과를 기반으로 향후 마리나 산업의 발전을 위한 제언을 할 수 있었다. The purpose of this study was to examine the past and present of the marina industry to suggest a development plan using text mining. Press articles were extracted with the keyword "marina", and TF analysis, word cloud analysis, TF-IDF analysis, and topic modeling analysis were performed using text mining techniques. The results of this study are as follows. First, the top 60 keywords such as "business", "marina", and "marine" were presented through TF analysis and it was visualized through word cloud analysis based on the top 500 keywords. Second, TF-IDF analysis confirmed the social trend of marina by period. Third, by topic modeling analysis, 8 topics were derived, "marina policy", "tourism", "real estate development", "marina investment attraction", "marine leisure industry", "complex marina development", "hotels and entertainment facilities", "domestic investment by foreign companies". Fourth, the past and present of the marina industry were examined by analyzing the social issues and changes of topics through topic modeling analysis by period. Based on the results, it was possible to make suggestions for the development of the marina industry in the future.

빅데이터를 이용한 '경호'와 '경비'의 용어 비교 분석

Gaurd and Security have Since there are many problems that do not match the rapidly changing trend of the times, the overall perception of society was compared and analyzed. In order to provide basic data and provide meaningful implications, data were collected through TEXTOM, and word cloud analysis, LDA topic modeling analysis, emotional analysis, and semantic network analysis were conducted. First, in 'Gaurd', 'Security' has many associations, and 'Gaurd' was suitable to include the meaning of 'Security’. Second, ‘Gaurd’ is a term that allows people to think of related tasks more without being confused than "Security", and it was a term suitable to include the meaning of related tasks comprehensively. Third, it was confirmed that 'Security' clearly had more negative perceptions than 'Gaurd', and 'Gaurd' was found to be a relatively positive term. Fourth, among the terms with strong connection centrality in 'Gaurd', there was 'security' as a term that could replace 'Gaurd' and 'Security', but in 'Security', no term that could imply the meaning of the two terms did not appear. Security, a term that is already widely used in academia and industry and can be replaced without confusion, is expected to be the most suitable replacement for "Gaurd" and "Security" in a large framework and include both meanings.

The positive aspects of caregiving in dementia: A scoping review and bibliometric analysis.

PurposeThe increasing incidence of dementia and home-based care exposes family caregivers to a variety of challenges as they endure strong stressors underlying the caregiver role. Despite growing publications on positive aspects of caregiving in dementia, few studies have identified the extent, nature, and gaps in the existing literature based on a holistic view. The aim was to identify key issues and a holistic view of literature regarding positive aspects of caregiving in dementia.MethodsA scoping review was conducted underlying a five-stage framework by Arksey and O'Malley. Five databases, including PubMed, CINAHL, PsycINFO, Embase, and Web of Science, were searched, and references were listed accordingly. Data were extracted by two researchers, comprising article characteristics, forms of positive aspects of caregiving and measurements, theories, forms of dementia and family caregiver, and keywords. Descriptive statistics and narrative synthesis were performed to analyze data. Network analysis of keywords and authors was conducted using VOSviewer software. Word cloud analysis of titles was examined by NVivo.ResultsThe review included 230 articles, most of which have been published in the last decade (62.61%). Most articles (40.00%) were contributed by the United States. Cross-sectional studies (41.30%) ranked first, followed by qualitative studies (13.48%). Over a quarter of the literature (28.26%) focused on Alzheimer's care, and nearly 90% included all forms of family caregivers. The Positive Aspects of Caregiving Scale and stress coping theory were most frequently cited. Four clusters dominated by Casey D, Quinn C, Joling KJ, and Teahan A were identified in the network of co-authorship. Six themes were identified: current situations of caregiver experiences, antecedents, consequences, measurement development, effects of interventions, and the concept of positive aspects of caregiving. These were in line with network analysis of keywords and word cloud analysis of titles.ConclusionsPositive aspects of caregiving in dementia have been widely concerned, but most of them are based on the theory of the negative stress process and are limited to current situations and influencing factors. Building theories focus on the positive aspects of caregiving, subsequently developing a comprehensive measurement and effective interventions, should be further studied.

Analysis of the Development Trend of Sports Research in China and Taiwan Using Natural Language Processing

Background: A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3283 abstracts). Methods: Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. Results: The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. Conclusions: It is suggested that all disciplines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.

Gene Expression Landscape of Chronic Myeloid Leukemia K562 Cells Overexpressing the Tumor Suppressor Gene PTPRG.

This study concerns the analysis of the modulation of Chronic Myeloid Leukemia (CML) cell model K562 transcriptome following transfection with the tumor suppressor gene encoding for Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and treatment with the tyrosine kinase inhibitor (TKI) Imatinib. Specifically, we aimed at identifying genes whose level of expression is altered by PTPRG modulation and Imatinib concentration. Statistical tests as differential expression analysis (DEA) supported by gene set enrichment analysis (GSEA) and modern methods of ontological term analysis are presented along with some results of current interest for forthcoming experimental research in the field of the transcriptomic landscape of CML. In particular, we present two methods that differ in the order of the analysis steps. After a gene selection based on fold-change value thresholding, we applied statistical tests to select differentially expressed genes. Therefore, we applied two different methods on the set of differentially expressed genes. With the first method (Method 1), we implemented GSEA, followed by the identification of transcription factors. With the second method (Method 2), we first selected the transcription factors from the set of differentially expressed genes and implemented GSEA on this set. Method 1 is a standard method commonly used in this type of analysis, while Method 2 is unconventional and is motivated by the intention to identify transcription factors more specifically involved in biological processes relevant to the CML condition. Both methods have been equipped in ontological knowledge mining and word cloud analysis, as elements of novelty in our analytical procedure. Data analysis identified RARG and CD36 as a potential PTPRG up-regulated genes, suggesting a possible induction of cell differentiation toward an erithromyeloid phenotype. The prediction was confirmed at the mRNA and protein level, further validating the approach and identifying a new molecular mechanism of tumor suppression governed by PTPRG in a CML context.

중국 전통 패치워크의 디자인적 요소를 활용한 복식 디자인 개발

Background/Objectives: This study uses data mining methods for practical research on product design application to identify the importance of the online market in China and design products that ordinary consumers would prefer and purchase. Methods: Data were collected through literature review and field investigation for theoretical research; photographic data were collected in parallel through online museums and onsite photography. For practical research, Python Program was used, and data mining analysis was conducted through term frequency analysis, word cloud analysis, and social network analysis, using consumer evaluation data of patchwork products currently sold in the Chinese online market. Results: First, two factors were identified in the birth of traditional Chinese patchwork: economic factors, which include a shortage of supplies, and social background factors, which denote the spread of the spirit of thrift. Second, fashion products are the most representative online shopping items in China. Comprehensive data analysis performed via data mining through platforms such as Baidu Index and Sina Weibo confirmed that patchwork products include decorative items, tops, and aprons. The keywords for the patchwork product style were "boxy, casual, and modern brands," and the target consumers were young people in their 20s and 30s. Third, according to the results of data mining, doubles were selected as sweatshirts and shirts popular with young people. As for the design of tops was applied with design elements analyzed through research on traditional Chinese patchwork. Conclusion/Implications: The design developed using traditional patchwork was created with a total of five items, three sweatshirt designs and two shirt designs, and the possibility of commercialization of traditional patchwork in the modern fashion market was confirmed through 3D image work after development.

로봇과 물류에 관한 텍스트 마이닝 분석

This research aims to highlight issues related to logistics and robot applying a text mining technique to Naver news article big data at the time of paying much attention to AI and big data technology as well as agenda on fourth industrial revolution. In ordr to analyze the role of robots in the logistics industry, this study employed 609 NAVER news articles from January 21, 2020 to December 31, 2020 for the analysis, excluding redundant and irrelevant articles. News article frequency analysis, news article keyworkd analysis, cosine similarity and co-occurence frequency analysis were conducted for the visualization of the research findings employing R program in order to invegstigate into the main issues related to news articles.. Cosine similarity and co-occurrence frequency as well as wordcloud analysis was visualized. It was found that the the news articles dealing with robots and logistics addressed the subjects related to corporations, robot, technology, and logistics together, presenting these keywords in the ranking of keyword frequency from the various perspectives. It is suggested that specific projects on delivery of products/services in an efficent manner needs to be initiated in order to apply the technology of robotics to not only the logistics industry, but also service industry (e.g., hotel and tourism-related businesses). This study has contributed to the presentation of the issue on robot among the fourth industrial revolution technologies.

Keystone Malaria Symposium 2022: a vibrant discussion of progress made and challenges ahead from drug discovery to treatment

Keystone Malaria Symposium 2022: a vibrant discussion of progress made and challenges ahead from drug discovery to treatment

사회적 트라우마티즘 회복을 위한 맥락적 관광경험 연구

This study aims to understand the contextual tourism experience to overcome social traumatism. Judith Hermann"s trauma recovery stage was applied to tourism. The first stage is a trip to a tourist destination, and the second stage is a happy memory of the tourism experience. And the third stage is the quality of life after tourism. For analysis, an interview and online survey were conducted, and the questionnaire consisted of 307 copies. The analysis result was Wordcloud analysis. The analysis result is as follows. In the first stage, moving to a tourist destination, the most keywords used are excitement, stability, and expectation. In the second stage, tourism experience, the most keywords used are comfort, tourism, and experience. And in the third stage, the quality of life after tourism, the most keywords used are comfort, forgiveness, and change. Therefore, Trauma can be recovered through tourism experience. The state should make tourism a welfare policy to recover from social traumatism.

Detecting latent topics and trends in blended learning using LDA topic modeling

With the rapid application of blended learning around the world, a large amount of literature has been accumulated. The analysis of the main research topics and development trends based on a large amount of literature is of great significance. To address this issue, this paper collected abstracts from 3772 eligible papers published between 2003 and 2021 from the Web of Science core collection. Through LDA topic modeling, abstract text content was analyzed, then 7 well-defined research topics were obtained. According to the topic development trends analysis results, the emphasis of topic research shifted from the initial courses about health, medicine, nursing, chemistry and mathematics to learning key elements such as learning outcomes, teacher factors, and presences. Among 7 research topics, the popularity of presences increased significantly, while formative assessment was a rare topic requiring careful intervention. The other five topics had no significant increase or decrease trends, but still accounted for a considerable proportion. Through word cloud analysis technology, the keyword characteristics of each stage and research focus changes of research were obtained. This study provides useful insights and implications for blended learning related research.

Ramadan Fasting as a Religious Obligation: A Qualitative Study on Opinions and Experiences of Muslim Pregnant Women about Fasting in Turkey.

The aim of this study was to examine opinions and experiences of Muslim pregnant women about Ramadan fasting in Turkey. The study had a descriptive, phenomenological design and included 17 Muslim pregnant women living in Turkey. Content analysis and word cloud analysis were performed. The content analysis revealed two themes. The most frequent words in the word cloud analysis were fasting, pregnancy, Ramadan, fast, religious duty, health, observing a missed fast, relief, concern, baby, thirsty and hungry. The findings of the study showed that religious beliefs, experiences, family and society pressured the pregnant women to keep Ramadan fasts. However, due to feeling worried about their own physical and psychological health and their infants, some of the women did not go on a fast. It can be recommended that pregnant women wanting to keep their fasts should be offered information about possible risks of fasting.

Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set.

BackgroundPublic engagement is a key element for mitigating pandemics, and a good understanding of public opinion could help to encourage the successful adoption of public health measures by the population. In past years, deep learning has been increasingly applied to the analysis of text from social networks. However, most of the developed approaches can only capture topics or sentiments alone but not both together.ObjectiveHere, we aimed to develop a new approach, based on deep neural networks, for simultaneously capturing public topics and sentiments and applied it to tweets sent just after the announcement of the COVID-19 pandemic by the World Health Organization (WHO).MethodsA total of 1,386,496 tweets were collected, preprocessed, and split with a ratio of 80:20 into training and validation sets, respectively. We combined lexicons and convolutional neural networks to improve sentiment prediction. The trained model achieved an overall accuracy of 81% and a precision of 82% and was able to capture simultaneously the weighted words associated with a predicted sentiment intensity score. These outputs were then visualized via an interactive and customizable web interface based on a word cloud representation. Using word cloud analysis, we captured the main topics for extreme positive and negative sentiment intensity scores.ResultsIn reaction to the announcement of the pandemic by the WHO, 6 negative and 5 positive topics were discussed on Twitter. Twitter users seemed to be worried about the international situation, economic consequences, and medical situation. Conversely, they seemed to be satisfied with the commitment of medical and social workers and with the collaboration between people.ConclusionsWe propose a new method based on deep neural networks for simultaneously extracting public topics and sentiments from tweets. This method could be helpful for monitoring public opinion during crises such as pandemics.

Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape.

Simple SummaryThe combination of digital pathology (DP) with artificial intelligence (AI) offers faster, more accurate, and more comprehensive diagnoses, resulting in more precise individualized treatment. As this technology is constantly evolving, it is critical to understand the current state of AI applications in DP. Thus, it is necessary to analyze AI patent applications, assignees, and leaders in the field. In this study, five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, were searched using key phrases, such as DP, AI, machine learning, and deep learning, and 523 patents were shortlisted based on the inclusion criteria. Our data demonstrated that the key areas of the patents were whole-slide imaging, segmentation, classification, and detection. In the past five years, an increasing trend in patent filing has been observed, mainly in a few prominent countries, with a focus on the digitization of pathological images and AI technologies that support the critical role of pathologists.The integration of digital pathology (DP) with artificial intelligence (AI) enables faster, more accurate, and thorough diagnoses, leading to more precise personalized treatment. As technology is advancing rapidly, it is critical to understand the current state of AI applications in DP. Therefore, a patent analysis of AI in DP is required to assess the application and publication trends, major assignees, and leaders in the field. We searched five major patent databases, namely, those of the USPTO, EPO, KIPO, JPO, and CNIPA, from 1974 to 2021, using keywords such as DP, AI, machine learning, and deep learning. We discovered 6284 patents, 523 of which were used for trend analyses on time series, international distribution, top assignees; word cloud analysis; and subject category analyses. Patent filing and publication have increased exponentially over the past five years. The United States has published the most patents, followed by China and South Korea (248, 117, and 48, respectively). The top assignees were Paige.AI, Inc. (New York City, NY, USA) and Siemens, Inc. (Munich, Germany) The primary areas were whole-slide imaging, segmentation, classification, and detection. Based on these findings, we expect a surge in DP and AI patent applications focusing on the digitalization of pathological images and AI technologies that support the vital role of pathologists.

COVID-19 DAN KEBEBASAN BEREKSPRESI DI INTERNET: MELIHAT REPRESI DIGITAL DALAM PEMBERITAAN TEMPO.CO

The presence of the Covid-19 pandemic and high internet penetration in Indonesia have also encouraged community activities, one of which is digital activism. The purpose of this article is to describe digital activism carried out in the midst of a pandemic that has received a lot of repressive measures in cyberspace. The approach used is media cloud content analysis of Tempo.co news related to digital repression in Indonesia from March to December 2020. Researchers processed data using Nvivo 12 Plus software, the data generated included crosstab and wordcloud analysis. The results showed that hacking and doxing attacks were the most discussed cases of digital repression on Tempo.co. In addition, law enforcement and law enforcement against democracy in danger are indicators of the highest issue in Tempo.co's reporting. While the wordcloud analysis shows quotes from the police and government, many words imply some form of performance from the Police and Government.

383 Balancing science policy and patient advocacy in medical education: the case of differences of sex development.

OBJECTIVES/GOALS: The clinical management of differences of sex development (DSDs) aims to guarantee best practices in medical care while addressing concerns related to non-reversible surgeries. Rhetorical analysis was conducted to study the balance between science policy and patient advocacy related to DSD surgeries as depicted in medical education materials METHODS/STUDY POPULATION: Unrestricted transcripts of two educational videos and text from all chapters of a handbook addressed to medical learners and faculty by the Association of American Medical Colleges (AAMC) were submitted to automated word cloud analysis (NVivo, QSR International®). Words with a weighted percentage > 0.19% from total words of a given source were defined as words of frequent use and were selected for further analysis after exclusion of words as conjunctions, prepositions, pronouns, or conversational fillers. Words sharing noun, adjective and adverb forms were coded and weighed as a single word following the Oxford dictionary. Discrepancies on word selection, exclusion or coding were resolved between four raters. The rhetorical context of most frequent words was identified. RESULTS/ANTICIPATED RESULTS: The word cloud analysis of the video resource intended for medical learners (n=104 words of frequent use) and the video intended for medical faculty (n= 94 words of frequent use) depicts a patient-centered approach (word people’) that is based on expert opinion (word [I] think’). The handbook (n= 998 words of frequent use) makes reiterated reference to patients’; lgbt’; gender’; health’; and caring’ while underscoring health concerns that are unrelated to genital variance (health’; caring’ and medical’). The noun surgery’ did not figure among the most frequent words in spoken language nor in written text even when summing its adjective and adverb forms. DISCUSSION/SIGNIFICANCE: Educational materials by the AAMC on DSDs accentuate patient-centered care within a medical humanism framework. However, the lack of discussion of DSD surgeries is an educational gap that should be addressed by key science policy and patient advocacy stakeholders.