• Journal of Information Processing Systems
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• v.9 no.4
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• pp.538-547
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• 2013

In this paper, we propose a bias detection method that is based on personal and social opinions that express contrasting views on competing topics on Twitter. We used unsupervised polarity classification is conducted for learning social opinions on targets. The $tf{\cdot}idf$ algorithm is applied to extract targets to reflect sentiments and features of tweets. Our method addresses there being a lack of a sentiment lexicon when learning social opinions. To evaluate the effectiveness of our method, experiments were conducted on four issues using Twitter test collection. The proposed method achieved significant improvements over the baselines.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.73-79
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• 2017

In this paper, we propose a design and implementation of a DB system for providing comprehensive information retrieval service about social economic information. We classify social economic organizations into 6 major categories and 35 small categories. The DB contains 25,938 social economic organizations in total. The DB system provides simple and intuitive interface for searching information such as keyword search, category search and initial search. Fully loaded time of webpage of implemented DB system achieved 2.3s by Vancouver, Canada Server.

• Journal of information and communication convergence engineering
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• v.18 no.4
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• pp.230-238
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• 2020

This study aims to determine whether student participation on Twitter affects academic performance. The key goals of the training course were to acquire social networking knowledge and skills and to learn how to share information, be productive in discussions, and create an interest-based community. The initial sample comprised 286 students from Jordan universities, 68.4% of whom agreed to participate in the study. Undergraduate students accounted for 73.9%, and graduate students accounted for 26.1%. Only 14.3% of the students chose the Twitter-based learning model. This is a mixed-methods study that integrates quantitative and qualitative approaches. The undergraduate students were found to tweet more and have more likes, while graduate students had more followers and were following more accounts. Moreover, 21% of the participants were the most active. Spearman's correlation analysis revealed a connection between participation in social media and student performance. Therefore, the results of this study may help educational professionals and education managers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.1-17
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• 2022

The purpose of this study is to suggest a plan to improve the level of acceptance of related technologies and the transition to smart factories of small and medium-sized manufacturing enterprises by using 'technology readiness' and 'integrated technology acceptance model'. To this end, the research hypothesis was verified by collecting questionnaire data from 130 small and medium-sized manufacturing companies in Korea and conducting path analysis. First, optimism affects performance expectations, social influence, and facilitation conditions, innovation affects performance expectations, effort expectations, and social influence, discomfort affects performance expectations, social influence, and facilitation conditions, and anxiety affects effort expectations, social influence and facilitation conditions. has been proven to affect Finally, performance expectations, effort expectations, social influence, and facilitation conditions were verified to have a significant positive effect on the intention to accept technology.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.704-710
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• 2007

This paper examines the operations of private housing developers (PHDs) based on Social Network Theory. PHDs need to choose the best consultants, contractors and suppliers (CCS) to make sure the project run and complete successfully. PHDs gather the scarce resources from the external environment through personal network. This research used the social network method which relies on alliances based on network, social, tie and trust. The more people/firm PHDs network with, the better chances of finding the right CCS.

• International Journal of Knowledge Content Development & Technology
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• v.14 no.1
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• pp.7-17
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• 2024

A survey was conducted to determine the level of awareness of library professionals working in engineering and technological institution libraries in Karnataka regarding web technology and social media sites in particular. The researcher designed a structured questionnaire using Google Forms and sent it to 204 engineering colleges affiliated with Visvesvaraya Technological University, Belagavi. There is a reasonable amount of awareness and acquaintance with social media sites among working library professionals. Analysis and interpretation are performed with the standard statistical tool (SPSS). The result of this study could provide library professionals with useful insights on how to use social media sites to promote library services.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.566-575
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• 2019

This study examines university students' awareness and preparedness for anticipated social problems of the Fourth Industrial Revolution and deteremines if there exist differences between gender, major fields of study and school years of students. Based on surveys with 122 university students in Seoul, students majoring in technology-engineering showed more positive outlook than students majoring in humanity-social science. Female students and humanity-social science students showed higher levels of concerns with social problems than male and technology-engineering students. More than three out of five students anticipated that they will experience the influence of the Fourth Industrial Revolution within the next five years. Two out of five students assessed their preparedness to be inadequate. Given the overarching social influence, it is necessary to develop convergent education of technology and social science that raises students' understanding and preparedness without differences in major fields of study and gender for the Fourth Industrial Revolution.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.928-931
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• 2014

The detection of human behavior from social media revolutionized health, business, criminal and political prediction. Significance of it, in incentive transformation of public opinion had already proven for developed countries in improving democratic process of elections. In $3^{rd}$ World countries, voters poll votes for personal interests being unaware of party manifesto or national interest. These issues can be addressed by social media, resulting as ongoing process of improvement for presently adopted electoral procedures. On the optimistic side, people of such countries applied social media to garner support and campaign for political parties in General Elections. Political leaders, parties, and people empowered themselves with social media, in disseminating party's agenda and advocacy of party's ideology on social media without much campaigning cost. To study effectiveness of social media inferred from individual's political behavior, large scale analysis, sentiment detection & tweet classification was done in order to classify, predict and forecast election results. The experimental results depicts that social media content can be used as an effective indicator for capturing political behaviors of different parties positive, negative and neutral behavior of the party followers as well as party campaign impact can be predicted from the analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.1-12
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• 2020

In this study, the effect of the social distance maintenance and pedestrian route system was analyzed for Seoul Station, one of the multi use facilities according to the COVID-19 pandemic. For analysis, the Seoul Station pedestrian network was established through the survey of the number of passengers and CAD floor plan. A pedestrian that maintaining Social Distance was implemented using the Social Force Model. Based on this, scenario analysis was proceed. As a result, when the walking line system was installed the average walking speed decreased compared to the current situation. but the average density was analyzed that maintain the walking level of service (LOS)'C', this mean walking line system is effective, and the effect of the walking line system was proved. It can be used as a pedestrian simulation model.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.712-720
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• 2022

The use of social media to disseminate news and interact with project stakeholders is increasing over time in the construction industry. Such social media data can be analyzed to get useful insights of the industry such as demands of new housing construction and satisfaction of construction workers. However, there has been a limited attempts to analyze social media data related to the construction industry. The objective of this study is to collect and analyze construction related tweets to understand the overall sentiments of individuals and organizations about the construction industry. The study collected 87,244 tweets from April 6, 2020, to April 13, 2020, which had hashtags relevant to the construction industry. The tweets were then analyzed to evaluate its sentiments polarity (positive or negative) and sentiment intensity or scores (-1 to +1). Descriptive statistics were produced for the tweets and the sentiment scores were visualized in a scatterplot to show the trend of the sentiment scores over time. The results shows that the overall sentiment score of all the tweets was slightly positive (0.0365). Negative tweets were retweeted and marked as favorite by more users on average than the positive ones. More specifically, the tweets with negative sentiments were retweeted by 2,802 users on average compared to the tweets with positive sentiments (247 average retweet count). This study can potentially be expanded in the future to produce a real time indicator of the construction market industry such as the increased availability of construction jobs, improved wage rates, and recession.