• Journal of Information Processing Systems
• /
• 제10권2호
• /
• pp.271-282
• /
• 2014

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -- such as tweets from Twitter -- depending on the teacher's intention.

• 경영정보학연구
• /
• 제13권3호
• /
• pp.141-163
• /
• 2011

인터넷을 중심으로 한 정보기술의 급격한 발전은 정보화 사회로의 패러다임 전환을 가져오면서 사회적 여론을 형성하는 과정에도 영향을 미치고 있다. 이에 따라 디지털 매체를 활용하여 온라인에서 정보를 생성하고 유통하며 사회적 합의에 이르는 정보처리 매커니즘의 변화에 대한 연구의 중요성이 증가하고 있다. 본 연구에서는 사회적 정보처리 과정의 고찰을 통하여 사회적 합의도출 및 제도화의 프로세스 모델을 도출하고 인터넷 매체에서의 정보처리 특성을 분석하였다. 정보처리 매커니즘의 변화를 분석하기 위하여 사회적 맥락 속에서 집단적 행동을 분석하는 질적 연구방법인 인류학적 접근법(ethnographic approach)을 적용하여 2가지 사례를 관찰 분석하였다. 사회적 컨센서스 형성과정은 사회적 의제의 제기, 여론 활동에서의 선택적 반영, 의제의 수용과 확산, 공론화와 사회적 합의, 제도화와 피드백이라는 5가지 단계로 구성되어진다. 인터넷 매체에서의 정보처리 특성은 사건에의 능동적 반응, 오피니언 리더의 역할 전이, 발의와 분석의 탄력성, 높은 확장성, 합의도출에의 적합성, 제도화와 상호작용 등 6가지로 제시하였다. 본 연구에서는 사회구성원의 정보를 공유하고 사회적 여론을 이끄는 과정을 설명하는 모텔을 제시함으로써 정보처리 구조를 사회적 네트워크 관점에서 고찰할 수 있는 이론적 기반을 마련하였다. 또한 그리고 인터넷 매체의 사회적 효용을 사회적 정보처리라는 새로운 기준으로 분석하여 정치, 커뮤니케이션 경영분야에서의 미디어 활용 및 의사결정의 합리성 제고에 기여할 것으로 보인다.

• Journal of Information Processing Systems
• /
• 제14권4호
• /
• pp.904-915
• /
• 2018

We demonstrate how social media content can be used to predict the unemployment rate, a real-world indicator. We present a novel method for predicting the unemployment rate using social media analysis based on natural language processing and statistical modeling. The system collects social media contents including news articles, blogs, and tweets written in Korean, and then extracts data for modeling using part-of-speech tagging and sentiment analysis techniques. The autoregressive integrated moving average with exogenous variables (ARIMAX) and autoregressive with exogenous variables (ARX) models for unemployment rate prediction are fit using the analyzed data. The proposed method quantifies the social moods expressed in social media contents, whereas the existing methods simply present social tendencies. Our model derived a 27.9% improvement in error reduction compared to a Google Index-based model in the mean absolute percentage error metric.

• Journal of Information Processing Systems
• /
• 제16권6호
• /
• pp.1309-1323
• /
• 2020

Crowd evacuation simulation is an important research issue for designing reasonable building layouts and planning more effective evacuation routes. The social force model (SFM) is an important pedestrian movement model, and is widely used in crowd evacuation simulations. The model can effectively simulate crowd evacuation behaviors in a simple scene, but for a multi-obstacle scene, the model could result in some undesirable problems, such as pedestrian evacuation trajectory oscillation, pedestrian stagnation and poor evacuation routing. This paper analyzes the causes of these problems and proposes an improved SFM for complex multi-obstacle scenes. The new model adds navigation points and walking shortest route principles to the SFM. Based on the proposed model, a crowd evacuation simulation system is developed, and the crowd evacuation simulation was carried out in various scenes, including some with simple obstacles, as well as those with multi-obstacles. Experiments show that the pedestrians in the proposed model can effectively bypass obstacles and plan reasonable evacuation routes.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권3호
• /
• pp.1168-1188
• /
• 2018

Social Network Aggregators are used to maintain and manage manifold accounts over multiple online social networks. Displaying the Activity feed for each social network on a common dashboard has been the status quo of social aggregators for long, however retrieving the desired data from various social networks is a major concern. A user inputs the query desiring the specific outcome from the social networks. Since the intention of the query is solely known by user, therefore the output of the query may not be as per user's expectation unless the system considers 'user-centric' factors. Moreover, the quality of solution depends on these user-centric factors, the user inclination and the nature of the network as well. Thus, there is a need for a system that understands the user's intent serving structured objects. Further, choosing the best execution and optimal ranking functions is also a high priority concern. The current work finds motivation from the above requirements and thus proposes the design of a query processing system to retrieve information from social network that extracts user's intent from various social networks. For further improvements in the research the machine learning techniques are incorporated such as Latent Dirichlet Algorithm (LDA) and Ranking Algorithm to improve the query results and fetch the information using data mining techniques.The proposed framework uniquely contributes a user-centric query retrieval model based on natural language and it is worth mentioning that the proposed framework is efficient when compared on temporal metrics. The proposed Query Processing System to Retrieve Information from Social Network (QPSSN) will increase the discoverability of the user, helps the businesses to collaboratively execute promotions, determine new networks and people. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a significant breakthrough scoring up to precision and recall respectively.

• 한국IT서비스학회지
• /
• 제19권5호
• /
• pp.65-81
• /
• 2020

This study aims to examine the effects of users' perceptions of COVID-19 risk on their seeking and processing of relevant information as COVID-19 emerges and spreads worldwide in 2019. We apply the risk information seeking and processing model (RISP Model) to verify whether users' COVID-19 related information seeking and processing behaviors have a positive effect on their preventive and information sharing behaviors. To achieve this research goal, an online survey was conducted with about 400 of social media users. The users' perceptions of risk for COVID-19 increased their perceived insufficiency of COVID-19 information. In addition, the perceived insufficiency of users' information formed a positive relationship with seeking and searching of information behaviors. The processing of COVID-19 related information has increased related preventive behaviors and sharing of information through social media. While searching for information related to COVID-19 prompted personal information sharing behaviors, it did not significantly affect preventive behaviors. Accordingly, in order to promote COVID-19 preventive behaviors as well as overall user health-related behaviors it can be inferred that additional measures are needed in addition to pursuing relevant information.

• Journal of Information Technology Applications and Management
• /
• 제17권3호
• /
• pp.191-211
• /
• 2010

Existing models of knowledge processing do not feature a systemic meaning of knowledge management and ignore the role of leadership and social energy in the knowledge processing system (KPS). This conceptual paper introduces the Leadership Invigorating Flows of Energies, (LIFE) Model as an attempt to remedy that situation and provide a more useful description of the KPS. The LIFE Model highlights the role of emergent leadership and flows of social energies as forces encouraging knowledge creation and dynamic diffusion within an organization through the Knowledge Processing Cycle in eight activities interacting with its social knowledge base in a self-organizing system.

• 한국언어정보학회지:언어와정보
• /
• 제20권1호
• /
• pp.25-49
• /
• 2016

Honorific forms express the speaker's social attitude to others and also indicate the social ranks and level of intimacy of the participants in the discourse. In a cross-linguistic perspective of grammar engineering, modelling honorification has been regarded as a key strategy for improving language processing applications. Using the HPSG and MRS formalism, this article provides a multilingual grammar model of honorification. The present study incorporates the honorific information into the Meaning Representation System (MRS) via Individual Constraints (ICONS), and then conducts an evaluation to see if the model contributes to semantics-based language processing.

• Journal of Information Processing Systems
• /
• 제17권1호
• /
• pp.163-177
• /
• 2021

Many methods of discovering social networking communities or clustering of features are based on the network structure or the content network. This paper proposes a community discovery method based on topic models using a time factor and an unsupervised clustering method. Online community discovery enables organizations and businesses to thoroughly understand the trend in users' interests in their products and services. In addition, an insight into customer experience on social networks is a tremendous competitive advantage in this era of ecommerce and Internet development. The objective of this work is to find clusters (communities) such that each cluster's nodes contain topics and individuals having similarities in the attribute space. In terms of social media analytics, the method seeks communities whose members have similar features. The method is experimented with and evaluated using a Vietnamese corpus of comments and messages collected on social networks and ecommerce sites in various sectors from 2016 to 2019. The experimental results demonstrate the effectiveness of the proposed method over other methods.

• Journal of Information Processing Systems
• /
• 제14권1호
• /
• pp.140-149
• /
• 2018

Social data such as users' comments are unstructured in nature and up-to-date technologies for analyzing such data are constrained by the available storage space and processing time when fast storing and processing is required. On the other hand, it is even difficult in using a huge amount of dynamically generated social data to analyze the user features in a high speed. To solve this problem, we design and implement a topic association analysis system based on the latent Dirichlet allocation (LDA) model. The LDA does not require the training process and thus can analyze the social users' hourly interests on different topics in an easy way. The proposed system is constructed based on the Spark framework that is located on top of Hadoop cluster. It is advantageous of high-speed processing owing to that minimized access to hard disk is required and all the intermediately generated data are processed in the main memory. In the performance evaluation, it requires about 5 hours to analyze the topics for about 1 TB test social data (SNS comments). Moreover, through analyzing the association among topics, we can track the hourly change of social users' interests on different topics.