2022, Vol.56, No.3

Journal of the Korean Society for Library and Information Science 2023 KCI Impact Factor : 0.84

pISSN : 1225-598X / eISSN : 2982-6292
https://journal.kci.go.kr/kslis

The purpose of this study is to analyze high school students’ understanding and use of the recommended books lists. The survey distributed to high school students in seven high schools located in Seoul, and 311 students responded. Using SPSS 24, the data was analyzed by frequency, binary logistic model, and one-way ANOVA. Results show the followings. First, the meaningful factors affecting students’ use of recommended books lists are gender, grade levels, and the degree to which students think recommended books lists include the books that are suitable and interesting. Particularly, the degree to which students think recommended books lists include the suitable books for them is the strong factor affecting students’ use of the recommended books lists. Second, male students are less likely to use recommended books lists than female students. Male students consistently are less likely to use the recommended books lists made by school librarians, subject teachers, and reading experts and/or organizations. Third, teacher-librarians believed that the recommended books lists would help students who do not enjoy reading and have difficulties in reading. However, the study finds that students who enjoy reading and read well are more willing to use the recommended books lists made by school librarians, subjects teachers, and reading experts and/or organizations than those who do not. Fourth, students are most willing to use the recommended books lists for college preparation. The findings suggest the further research topics in designing the recommended books lists suitable for high school students and in scaffolding the high school students’ use of book information reflected in recommended books lists.

Today, public libraries are contributing deeply to our society by strengthening their ability and services to identify and respond to users’ needs through various programs. However, it is difficult to find a study that analyzed the research status of public library programs or changes over time. Therefore, for the purpose of systematically grasping research trends in the field of overseas public library programs, an intellectual structure analysis based on keyword profiling was performed. Specifically, subject terms analysis, network analysis and cluster analysis, and analysis by period/year were performed based on the controlled keywords (subject terms) of journal articles papers searched in the LISTA database. As a result, first, it was found that 9 subjects corresponding to all global/hot/local topics are leading the research in the field of overseas public library programs. Second, five research areas in the field of overseas public library programs(cultural programs, outreach programs, activity programs, public services, community) could be visualized and clearly identified. Third, research in the field of overseas public library programs began in earnest in the late 1990s and was active from the mid-2000s to the early 2010s, and after that, it was found to be somewhat stagnant until recently. This study is the result of specifically identifying research trends on programs that recently emerged as a major task of public libraries, and can be used as basic data and prior knowledge to explore the development direction of public library programs in the future.

This study aims to understand the trends of subjects related to real estate policies and public’s emotional opinion on the policies. Two keywords related to real estate policies such as “real estate policy” and “real estate measure” were used to collect tweets created from February 25, 2008 to August 31, 2021. A total of 91,740 tweets were collected and we applied sentiment analysis and dynamic topic modeling to the final preprocessed and categorized data of 18,925 tweets. Sentiment analysis and dynamic topic model analysis were conducted for a total of 18,925 posts after preprocessing data and categorizing them into supply, real estate tax, interest rate, and population variance. Keywords of each category are as follows: the supply categories (rental housing, greenbelt, newlyweds, homeless, supply, reconstruction, sale), real estate tax categories (comprehensive real estate tax, acquisition tax, holding tax, multiple homeowners, speculation), interest rate categories (interest rate), and population variance categories (Sejong, new city). The results of the sentiment analysis showed that one person posted on average one or two positive tweets whereas in the case of negative and neutral tweets, one person posted two or three. In addition, we found that part of people have both positive as well as negative and neutral opinions towards real estate policies. As the results of dynamic topic modeling analysis, negative reactions to real estate speculative forces and unearned income were identified as major negative topics and as for positive topics, expectation on increasing supply of housing and benefits for homeless people who purchase houses were identified. Unlike previous studies, which focused on changes and evaluations of specific real estate policies, this study has academic significance in that it collected posts from Twitter, one of the social media platforms, used emotional analysis, dynamic topic modeling analysis, and identified potential topics and trends of real estate policy over time. The results of the study can help create new policies that take public opinion on real estate policies into consideration.