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The roles of perception and attitudes toward media reports of suicides in social learning effects (자살보도에 대한 지각과 인식: 사회학습효과의 검증)

  • Joonsung Bae ;Taekyun Hur
    • Korean Journal of Culture and Social Issue
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    • v.16 no.2
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    • pp.179-195
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    • 2010
  • Media reports of suicides has been found to increase suicide cases that were temporally and spacially proximal to the reports, but the psychological mechanisms, social learning, underlying the negative effects was not directly tested. The present study examined the cognitive processes of social learning that media reports of suicides, especially positive contents toward suicides, might change people's perception, memory, and attitudes toward suicides positively and subsequently increase subsequent suicide intentions and behaviors. Through an internet survey, 300 adults reported their perception, memory, and attitudes toward news reports of suicides, and rated whether the suicides were described positively or negatively in the reports. Finally they reported their suicide intentions and behaviors. The results revealed that people tended to remember more the contents of suicide reports suggested to increase copycat suicides. Also, people were found to have an ironic view to suicide reports of media that they acknowledged the dangers of suicides reports and approached the reports with curiosity. More importantly, the perception of the positive reward that suicides might achieved through suicides was related with positive attitudes toward suicides and behavioral intention to suicides. The present findings was discussed in the social learning understanding of copycat suicides and their implications for suicide-prevention strategies.

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How Market Reacts on the Metaverse Initiatives? An Event Study (메타버스 투자 추진이 기업 가치에 미치는 영향 분석: 이벤트 연구 방법론)

  • Mina Baek;Jeongha Kim;Dongwon Lee
    • Information Systems Review
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    • v.25 no.4
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    • pp.183-204
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    • 2023
  • Due to the COVID-19 pandemic, lots of occasions need to be held in online environment. This is the reason why "Metaverse" gets lots of attention in 2021. A number of companies made announcements on Metaverse, and this situation also boomed stock market. This paper investigates the relationship between Metaverse initiatives and business value of the firm (i.e., stock prices). We examine this relationship by using event study method with Lexis-Nexis News data from 2019 to 2021. The results indicate that Metaverse initiatives significantly impact positive influence on firm's value. In the technological perspective, technical factors affect more positive market returns, including Metaverse enablers (e.g., NFT, VR devices, digital twin) and common infrastructure (e.g., semiconductor, AI, cloud), and especially virtual environment was emphasized. Additionally, in the strategical perspective, radical innovation (e.g., pivoting, acquisition) impact more positive market return rather than incremental innovation (e.g., partnership, investment). Also, firms from non-service industries can achieve benefits from Metaverse initiatives rather than service industry in some degree.

Text Mining Analysis of Media Coverage of Maritime Sports: Perceptions of Yachting, Rowing, and Canoeing (텍스트마이닝을 활용한 해양스포츠에 대한 언론 보도기사 분석: 요트, 조정, 카누를 중심으로)

  • Ji-Hyeon Kim;Bo-Kyeong Kim
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.29 no.6
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    • pp.609-619
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    • 2023
  • This study aimed to investigate the formation of the social perception of domestic maritime sports using text mining analysis of keywords and topics from domestic media coverage over the past 10 years related to representative maritime sports, including yachting, rowing, and canoeing. The results are as follows: First, term frequency (TF) and word cloud analyses identified the top keywords: "maritime," "competition," "experience," "tourism," "world," "yachting," "canoeing," "leisure," and "participation." Second, semantic network analysis revealed that yachting was correlated with terms like "maritime," "industry," "competition," "leisure," "tourism," "boat," "facilities," and "business"; rowing with terms like "competition" and "Chungju"; and canoeing with terms like "maritime," "competition," "experience," "leisure," and "tourism." Third, topic modeling analysis indicated that yachting, rowing, and canoeing are perceived as elite sports and maritime leisure sports. However, the perception of these sports has been demonstrated to have little impact on society, public opinion, and social transformation. In summary, when considering these results comprehensively, it can be concluded that yachting and canoeing have gradually shifted from being perceived as elite sports to essential elements of the maritime leisure industry. Contrariwise, rowing remains primarily associated with elite sports, and its popularization as a maritime leisure sport appears limited at this time.

Automated Story Generation with Image Captions and Recursiva Calls (이미지 캡션 및 재귀호출을 통한 스토리 생성 방법)

  • Isle Jeon;Dongha Jo;Mikyeong Moon
    • Journal of the Institute of Convergence Signal Processing
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    • v.24 no.1
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    • pp.42-50
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    • 2023
  • The development of technology has achieved digital innovation throughout the media industry, including production techniques and editing technologies, and has brought diversity in the form of consumer viewing through the OTT service and streaming era. The convergence of big data and deep learning networks automatically generated text in format such as news articles, novels, and scripts, but there were insufficient studies that reflected the author's intention and generated story with contextually smooth. In this paper, we describe the flow of pictures in the storyboard with image caption generation techniques, and the automatic generation of story-tailored scenarios through language models. Image caption using CNN and Attention Mechanism, we generate sentences describing pictures on the storyboard, and input the generated sentences into the artificial intelligence natural language processing model KoGPT-2 in order to automatically generate scenarios that meet the planning intention. Through this paper, the author's intention and story customized scenarios are created in large quantities to alleviate the pain of content creation, and artificial intelligence participates in the overall process of digital content production to activate media intelligence.

The Effects of Field Trips on Middle School Students' Preference and Awareness of Science Museum (현장학습을 통한 중학생들의 과학관 선호도 및 인식 변화)

  • Chang, Hyun-Sook;Choi, Kyung-Hee
    • Journal of The Korean Association For Science Education
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    • v.26 no.3
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    • pp.330-341
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    • 2006
  • This study investigated whether science museum field trips over a duration of 7 months would effect student preference and awareness of science museums. 32 eighth and ninth graders were sampled from a middle school in Seoul and asked about their preference, awareness and the effect of the science museum visits both before and after a field trip. Findings showed a preference for science museums which dealt with themes and topics of student interest. After a field trip, student responded that science museums were information providers which yielded opportunity to explore forthcoming science feats, science in everyday life, developmental histories of science and technology, science-related news, social issues, and science rationale. Even though science museums in Korea leave much to be desired in terms of quantity and quality, field trips were positively received by students. This reception and its usefulness implied that expansion publizing and active use of formal/informal education relating to science museum facilities need to be top-priority business.

Optimizing Language Models through Dataset-Specific Post-Training: A Focus on Financial Sentiment Analysis (데이터 세트별 Post-Training을 통한 언어 모델 최적화 연구: 금융 감성 분석을 중심으로)

  • Hui Do Jung;Jae Heon Kim;Beakcheol Jang
    • Journal of Internet Computing and Services
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    • v.25 no.1
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    • pp.57-67
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    • 2024
  • This research investigates training methods for large language models to accurately identify sentiments and comprehend information about increasing and decreasing fluctuations in the financial domain. The main goal is to identify suitable datasets that enable these models to effectively understand expressions related to financial increases and decreases. For this purpose, we selected sentences from Wall Street Journal that included relevant financial terms and sentences generated by GPT-3.5-turbo-1106 for post-training. We assessed the impact of these datasets on language model performance using Financial PhraseBank, a benchmark dataset for financial sentiment analysis. Our findings demonstrate that post-training FinBERT, a model specialized in finance, outperformed the similarly post-trained BERT, a general domain model. Moreover, post-training with actual financial news proved to be more effective than using generated sentences, though in scenarios requiring higher generalization, models trained on generated sentences performed better. This suggests that aligning the model's domain with the domain of the area intended for improvement and choosing the right dataset are crucial for enhancing a language model's understanding and sentiment prediction accuracy. These results offer a methodology for optimizing language model performance in financial sentiment analysis tasks and suggest future research directions for more nuanced language understanding and sentiment analysis in finance. This research provides valuable insights not only for the financial sector but also for language model training across various domains.

Automatic Recognition of Pitch Accent Using Distributed Time-Delay Recursive Neural Network (분산 시간지연 회귀신경망을 이용한 피치 악센트 자동 인식)

  • Kim Sung-Suk
    • The Journal of the Acoustical Society of Korea
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    • v.25 no.6
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    • pp.277-281
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    • 2006
  • This paper presents a method for the automatic recognition of pitch accents over syllables. The method that we propose is based on the time-delay recursive neural network (TDRNN). which is a neural network classifier with two different representation of dynamic context: the delayed input nodes allow the representation of an explicit trajectory F0(t) along time. while the recursive nodes provide long-term context information that reflects the characteristics of pitch accentuation in spoken English. We apply the TDRNN to pitch accent recognition in two forms: in the normal TDRNN. all of the prosodic features (pitch. energy, duration) are used as an entire set in a single TDRNN. while in the distributed TDRNN. the network consists of several TDRNNs each taking a single prosodic feature as the input. The final output of the distributed TDRNN is weighted sum of the output of individual TDRNN. We used the Boston Radio News Corpus (BRNC) for the experiments on the speaker-independent pitch accent recognition. π 1e experimental results show that the distributed TDRNN exhibits an average recognition accuracy of 83.64% over both pitch events and non-events.

An Analysis of the Support Policy for Small Businesses in the Post-Covid-19 Era Using the LDA Topic Model (LDA 토픽 모델을 활용한 포스트 Covid-19 시대의 소상공인 지원정책 분석)

  • Kyung-Do Suh;Jung-il Choi;Pan-Am Choi;Jaerim Jung
    • Journal of Industrial Convergence
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    • v.22 no.6
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    • pp.51-59
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    • 2024
  • The purpose of the paper is to suggest government policies that are practically helpful to small business owners in pandemic situations such as COVID-19. To this end, keyword frequency analysis and word cloud analysis of text mining analysis were performed by crawling news articles centered on the keywords "COVID-19 Support for Small Businesses", "The Impact of Small Businesses by Response System to COVID-19 Infectious Diseases", and "COVID-19 Small Business Economic Policy", and major issues were identified through LDA topic modeling analysis. As a result of conducting LDA topic modeling, the support policy for small business owners formed a topic label with government cash and financial support, and the impact of small business owners according to the COVID-19 infectious disease response system formed a topic label with a government-led quarantine system and an individual-led quarantine system, and the COVID-19 economic policy formed a topic label with a policy for small business owners to acquire economic crisis and self-sustainability. Focusing on the organized topic label, it was intended to provide basic data for small business owners to understand the damage reduction policy for small business owners and the policy for enhancing market competitiveness in the future pandemic situation.

2023 Korea Digital Business Trend Study: Listening to Voices from Academia and Industry (2023 대한민국 디지털 비즈니스 트렌드 인식조사: 학계와 산업계의 다양한 목소리를 들어보다)

  • Heedong Yang;Hyunchul Ahn;Jung Lee;Hyunjeong Kang
    • Information Systems Review
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    • v.25 no.1
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    • pp.189-212
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    • 2023
  • This study uses various methods, including media analysis, expert interviews, and large-scale surveys, to derive notable digital business trends in 2023. Most trend studies have yet to deal with digital business trends in Korea. They also often have limitations in the objectivity of the results using unclear methods. On the other hand, this study emphasizes the validity of the results by collecting opinions from Korean digital business experts in various fields. First, Korean IT news articles were collected and analyzed through topic modeling analysis. Then, based on the results, interviews were conducted with 13 academic and industrial experts to derive 16 IT business trend candidates. Then, a survey was conducted on 210 experts to finalize the list of Korean IT business trends. Finally, to compare overseas and domestic views, we conducted an additional survey using the items developed by the Society for Information Management, SIM. This study is meaningful in that it drew prospects for digital business trends in consideration of the domestic business environment by scientifically converging various opinions of Korean digital business leaders. Our study contributes to developing strategies for IT technology and IT service business markets.

An Empirical Study on the Adoption of Online Direct Marketing in Agricultural Firms (농업경영체의 온라인 직거래 마케팅 수용에 관한 실증적 연구)

  • Cheolho Yoon;Changhee Park
    • Information Systems Review
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    • v.20 no.1
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    • pp.41-59
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    • 2018
  • This study analyzed the factors that affect acceptance of online direct marketing in agricultural companies. Empirical analysis was conducted using the research model based on the individual's technology acceptance model (TAM) and the information technology adoption models in organizations. These models have four dimensions: 1) technology characteristics, which include perceived usefulness and perceived ease of use of TAM 2) CEO characteristics, which including the innovativeness and IT capability of CEOs; 3) organizational readiness, which include financial, technological, and human resources capabilities and 4) environment and external pressure, which include government support and changes to the Internet environment. These concepts were empirically tested. A total of 209 valid data were collected through questionnaires and analyzed using confirmatory factor analysis and path analysis through the application of structural equation modeling. Results show that perceived usefulness, IT capability of CEOs, and changes to the Internet environment have significant effects on the adoption intention of online direct marketing. However, perceived ease of use, CEO innovativeness, government support, and the variables of organizational readiness dimension did not have significant effects on adoption intention. This study suggests practical implications for adoption of online direct marketing in agricultural companies.