Jonghwan Oh

Ph.D. Student / Graduate School of Convergence Science and Technology

Reading Behaviour

  • Design supporting tools for skim reading environment

The amount of accessible information online is rapidly expanding. This increases the demand for a support tool that can help people acquire information in a short period of time. Highlights can help with reading, especially by enhancing the effectiveness of skim reading. This paper suggests a novel method of collecting reading-pattern data from a group of subjects through the use of mobile devices so that this data can be utilized to automatically generate highlighted text. Through experimentation, we can gather data while participants read on their mobile devices. The information obtained includes the location of sentences on the screen, the position of scrolling fingers, and the exposure time of each sentence on the screen. From suggested these data, we will develop methods for collecting and filtering the reading patterns of the test subjects, we expect to develop the idea and uses this information for automatically generating the highlights.

– JongHwan Oh, SungJin Nam, Joonhwan Lee (2014). “Generating highlights automatically from text-reading behaviors on mobile devices,” in Proceedings of the 2014 ACM annual conference extended abstracts on Human Factors in Computing Systems Extended Abstracts. pp. 2317-2322


Natural User Interface

  • Designing new interfaces for disabled people using motion recognition

Now we display information everywhere, but ceiling is the last place we haven’t used extensively. A ceiling at home, an overhead surface ordinarily used for structural and aesthetic purpose and less used compared to other interior surfaces, is expected to be a perfect screen when people lay on the bed. In this paper, we present Hands-Up system, our novel way to utilize ceiling to display information and to interact with by using Microsoft Kinect, which could give commands to the computer through a minimum hands motion. The Hands-Up system has been created by a combination of the specific situation on the bed and a smart device, Kinect, which can read human movements. We made a prototype and designed User Interface (UI) suitable for the system.

– JongHwan Oh, Yerhyun Jung, Yongseok Cho, Chaewoon Hahm, Hyeyoung Sin, and Joonhwan Lee (2012). “Hands-up: motion recognition using kinect and a ceiling to improve the convenience of human life,” in Proceedings of the 2012 ACM annual conference extended abstracts on Human Factors in Computing Systems Extended Abstracts. pp. 1655-1660

Social Computing

  • A Case Study on Twitted Haiyan Photographs, Reality Construction Narrative, and the Epistemological Issues

This study investigates whether a Social Network Service (SNS), i.e. Twitter, has a potential to function as a medium that represents the reality in a consistent way. On Twitter, each user generates information individually and each tweet contains different comprehension on the reality. Yet, the analysis informs that through the use of Twitter the reality can be perceived as a combined whole, which is constructed by the narrative of the thread, not as a sum of individual piece. We collected tweets uploaded for a certain period that include a hashtag #Haiyan-the Typhoon Haiyan hit the Philippines in early-November 2013-and a photograph. Initially, all collected tweets were classified into 7 categories according to the visual theme in the photograph of each tweet. Reading the fluctuation of the photographed discourses, we analysed how discourses on the typhoon are organised to finally construct a coherent story, a conventional disaster narrative. As a real time experience, Twitter users should have seen Hayen images in a random way, the data set extracted for this study being an unstable entity mediated by the twitter algorithm and the twitter users’ continuous reactions. The possibility of the reality construction by the time depending data set of Twitter acclaims changes in the theories of reality construction of the media. The discussion continued under the perspectives of postmodern narrative theory, ambient journalism, and collective intelligence. Finally, we attempt to provide explanation on the phenomenon under the light of Latour’s monadology, which inspires the sociological meaning of the collected data set through SNS, compared to previous SNS studies that extract patterns from the “big data.”

– 홍석경, 오종환 (2015). SNS를 통한 현실인식 가능성에 대한 고찰. 『커뮤니케이션 이론』, 11(1), 46-93.

  • Usage of Korean Consonants and Vowels for Delivering Emotion

On Korean websites, people often use several Korean vowels or consonants solely, which is the way to express emotion. This way of expression has scarcely been examined seriously, though it is one of the most popular online slangs. This is an exploratory study that reveals how and how much these expressions are used on social media. Data crawling technique was used as a way to examine the usage of vowels and consonants in ordinary situations. 6 ‘Facebook pages’ popular among Korean users are selected, and about 2.5 million comments are collected from posts in those pages. 10 expressions, including ‘ᄏ(K)’, ‘ᄒ(H)’, ‘ᅮ(U)’, are counted and analyzed from the collected texts of comments. As a result, several patterns of using vowels and consonants are found. Especially, ‘ᄏ(K)’ is used much more frequently than other vowels or consonants, and used more continually. It is also found that the patterns of using vowels or consonants in writing comments changes as the mood of posts or the characteristics of Facebook pages.

– 오종환, 장수연, 이준환 (2014). 한글 자음 및 모음 사용을 통해 드러나는 온라인에서의 정서 표현에 대한 탐색적 연구. 『멀티미디어학회논문지』, 17(7), 866-878

  • A Study on Patterns of Social Relationships between Twitter Users

Following activity in Twitter is the most fundamental action that Twitter users need to perform to receive information. People choose followee for various reasons including information sharing and social interaction, and we are interested in characterizing the features associated with the following activities. In this paper, we investigate the following behaviors among Twitter users and build quantitative models that can explain the dynamics of the Twitter following. We first characterize the following behavior of Twitter users by conducting online survey. We then collect more than three million tweets from the survey participants to extract quantitative features, with which we build a series of Generalize Linear Models (GLM) on Twitter following. We analyze the models and the results show that Twitter users exhibit different interaction behavior based on the type of their followee. The result informs that classification algorithms can be utilized to build intelligent Twitter applications such as personalization, filtering, summarization, and recommendation systems.

– 서봉원, 이준환, 오종환 (2012). 트위터 사용자 간 관계형성 패턴에 대한 연구. 『한국언론학보』, 56(5), 88-113.