Video is an increasingly important method of information-sharing on the Web. Services such as YouTube, Vimeo, and Liveleak are platforms that support uploading User-Generated Content. Users tend to seek related information during or after watching an informative video by finding and reading comments on Web services. However, existing services only support sorting by recentness (newest) or rating (LIKES score), as opposed to related information. We suggest a novel method to find informative comments by considering original content and its relevance. We conducted a qualitative study of participants watching informative videos and analyzed how users interacted with the comments, the feature preferences, and the criteria for evaluation. We developed methods to measure informativeness priority, the user-provided level of information, classify intention of information posted, and cluster duplicate themes. Analysis of 1,861 TED talk videos and 380,619 comments show suggested methods can find more informative comments compared to existing methods (LIKES).

Prior studies have addressed many negative aspects of mobile distractions in group activities. In this paper, we present Lock n' LoL. This is an application designed to help users focus on their group activities by allowing group members to limit their smartphone usage together. In particular, it provides synchronous social awareness of each other's limiting behavior. This synchronous social awareness can arouse feelings of connectedness among group members and can mitigate social vulnerability due to smartphone distraction (e.g., social exclusion) that often results in poor social experiences. After following an iterative prototyping process, we conducted a large-scale user study (n = 976) via real field deployment. The study results revealed how the participants used Lock n' LoL in their diverse contexts and how Lock n' LoL helped them to mitigate smartphone distractions.

This article aims to deepen understanding of these mass interactions in online sports viewing through studying Naver Sports, the largest online sports viewing service in Korea. We examined the diverse aspects of mass interactions, including interactive experiences, usage motives, and relationships between usage patterns and motives, through analysis of almost 6 million chats from Naver Sports and from self-reporting survey data from 1,123 users. First, we found that online sports viewing provides unique interactive experiences when compared to other settings such as offline sports viewing and social TV viewing with friends. Second, we found the key motives inspiring online sports viewing include the following: sharing feelings/thoughts, wanting to be entertained, sharing information, and wanting to feel membership in a group. Third, these motives were significantly related to specific usage patterns. Finally, we explored how the study’s key findings can offer practical design implications to enhance online sports viewing services, and to show system designers how to support particular usage patterns to better accommodate specific user motives.

We consider participatory parental mediation in which children engage with their parents in activities that encourage both parents and children to participate in co-learning of digital media use. To this end, we developed FamiLync, a mobile service that treats use-limiting as a family activity and provides the family with a virtual public space to foster social awareness and improve self-regulation. A three-week user study conducted with twelve families in Korea (17 parents and 18 teenagers) showed that FamiLync improves mutual understanding of usage behavior, thereby providing common grounds for parental mediation. Further, parents actively participated in use-limiting with their children, which significantly increased the children's desire to participate. As a consequence, parental mediation methods and parent-child interaction in relation to smartphone usage changed appreciably, and the participants smartphone usage amount significantly decreased.

The paper suggests a method for analyzing technology trends. The process, which investigates development of technologies overtime, identifies main technologies displaying the fastest growth compared to greater influence of new inventions. The method analyzes term frequency and change over time of technological terms in academic articles and patents to identify the prior technologies that lead to a new technology and to detect technologies that have the biggest impact. The analysis was performed on 4,354,054 patents from the US Patent Office dating from 1975 until today. In addition, academic articles were analyzed as a trend forecasting data set to identify patents trends 4-5 years in advance and technology trends up to 9 years in advance. The forecasting method was extensively validated using a large repository of real-world technology terms, and the results were verified against Gartner technology predictions, Web searches, news articles, and book publications. The method shows higher accuracy than existing forecasting methods do. Some correlation is displayed between technology trends and future US stock market performance.

This demonstration shows the actual application of our wireless AMI[1] system, AMR Xpider. This system consists of a sink node and multiple relay nodes. Hundreds of sensing nodes, which are connected to the relay node, transfer metering data to the MDMS (meter data management system). A total of 20,000 AMR Xpider units have been installed in apartments in South Korea for gas metering during the past three years. The AMR Xpider units are used for actual billing purposes. They operate with battery life of at least five years (generally, around seven years), with an average communication success rate of 98%.