• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.674-683
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• 2021

Purpose: The purpose of this study is to examine the changes in the pattern of crimes caused by the spread and slowdown of coronavirus infections and to devise preventive and countermeasures against various crimes in the future. Method: In order to find out the characteristics of each crime in the non-face-to-face and face-to-face environment, the results of previous prior research and data officially released by the National Police Agency and the prosecution office were compared and analyzed. Result: In the early epidemic of infectious diseases, overall crime has decreased, and civil life-related crimes and crimes targeting the socially disadvantaged are increasing. In the second half of the infectious disease, unlike the first half, the prolonged corona caused the economic recession and unemployment, deepening the damage from illegal private finance and significantly increasing illegal gambling game crimes. Conclusion: According to the time of the outbreak of the COVID-19 pandemic, the quarterly crime characteristics showed that there was a difference in crime type and crime increase and decrease rate, and that crime response measures should be changed accordingly.

• 한국사회복지학회:학술대회논문집
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• 2003.05a
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• pp.282-309
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• 2003

This exploratory qualitative study investigates the effects of experiencing domestic violence on male adolescent offenders' masculinities. Empirical and theoretical literature suggests that negative male role models in violent families result in male adolescents' experiencing conflict in constructing gender identities, especially masculinities. Moreover. criminologists argue that masculinities are often connected with crimes as a way to prove masculine competence. This study compares male adolescent offenders who have experienced domestic violence with those who have not experienced domestic violence and explores how domestic violence experiences influence the construction of gender identities among male adolescent offenders. The study used a secondary qualitative data analysis method. The data consisted of ethnographic in-depth interview transcripts, observational field notes, and formal facility records collected at a juvenile correctional facility in Minnesota. The process of data analysis was a "constant comparative method" that sought to understand differences and similarities in the expressed gender narratives and identity patterns between the two groups of offenders. This process also examined differences within each group. The qualitative data analysis revealed that domestic violence experiences in childhood may be related to the construction of gender identities during adolescence. The findings of this study showed that male adolescent offenders who had experienced domestic violence tended to attach themselves to oppressed mothers more readily than those who had not experienced domestic violence. Next, their attachment to mothers related to the construction of more relational gender identities although most participants, regardless of domestic violence experiences, had much in common regarding gender expression. Finally, despite these relational gender identities, male adolescent offenders who had experienced domestic violence tended to depend upon violence and crimes to show masculine competence, as did male adolescent offenders who had not experienced domestic violence. The study findings suggest a need for research to understand the construction of gender identities in the context of particular experiences and the importance of building theories that advance a comprehensive understanding of the construction of masculinities and youth crime. This study also discusses the development of social work programs that protect young men from adherence to exaggerated masculinity, which is often associated with crimes.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.