Ryong Lee

Discovery of unusual regional social activities using geo-tagged microblogs

The advent of microblogging services represented by Twitter evidently stirred a popular trend of personal update sharing from all over the world. Furthermore, the recent mobile device and wireless network technologies are greatly expanding the connectivity between people over the social networking sites. Regarding the shared buzzes over the sites as a crowd-sourced database reflecting a various kind of real-world events, we are able to conduct a variety of social analytics using the crowd power in much easier ways. In this paper, we propose a geo-social event detection method by finding out unusually crowded places based on the conception of social networking sites as a social event detector. In order to detect unusual statuses of a region, we previously construct geographical regularities deduced from geo-tagged microblogs. Especially, we utilize a large number of geo-tagged Twitter messages which are collected by means of our own tweets acquisition method in terms of geographic relevancy. By comparing to those regularities, we decide if there are any unusual events happening in monitoring geographical areas. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities which are computed from a large number of real geo-tagged tweet dataset around Japan.

Discovery of user behavior patterns from geo-tagged micro-blogs

The explosive growth of micro-blogging sites such as Twitter has enabled folks to share their personal up-to-dates. Compared to conventional blog sites, through the short length of messages, micro-blogging sites help users easily express their experiences, thoughts and feelings and share them instantly and globally. In addition, mobile devices based micro-blogging applications are ensuring the usefulness in a variety of our daily activities without spatial or temporal restriction. Especially, the most significant characteristics chiefly possible in such mobile micro-blogging is on the fact that the cutting-edge smartphones can utilize location sensing information that make it clear to analyze where the published messages are made almost in real time. In the respect of the diversity and the quantity of crowds writing the micro-blogs, we are sure that the micro-blogging sites can be a very important social media platform where a lot of valuable knowledge such as geographic social phenomena can be extracted. In this paper, we endeavor to find geographic social patterns from user movement histories made by mass mobile micro-bloggers. We particularly propose fundamental models based on aggregation and dispersion about movements of micro-bloggers in geographic regions. We also performed experiments to discover geographic characteristics from the micro-blog data actually gathered from Twitter.

Urban area characterization based on semantics of crowd activities in Twitter

It is essential to characterize geographic regions in order to make various geographic decisions. These regions can be characterized from various perspectives such as the physical appearance of a town. In this paper, as a novel approach to characterize geographic regions, we focus on the daily lifestyle patterns of crowds via location-based social networking sites in urban areas. For this purpose, we propose a novel method to characterize urban areas using Twitter, the most representative microblogging site. In order to grasp images of a city by social network based crowds, we define the geographic regularity of the region using daily crowd activity patterns; for instance, the number of tweets, through the number of users, and the movement of the crowds. We also analyze the changing patterns of geographic regularity with time and categorize clustered urban types by tracking common patterns among the regions. Finally, we present examples of several urban types through the observation of experimentally extracted patterns of crowd behavior in actual urban areas.

Exploring urban characteristics using movement history of mass mobile microbloggers

The explosive growth of the smartphones market and social network sites such as the Twitter has enabled to share numerous short messages reflecting our daily lives and social events from outside their homes or offices. Compared to conventional blogging sites, micro-blogging sites through mobile network enable us to easily write and share their daily logs without any spatial or temporal restrictions. Such mass geo-tagged and time-stamped micro-blogs can inform us about social patterns, regardless of their scale, time, or significance. We investigate characteristic patterns in urban areas such as populated areas from the movement histories of mass mobile micro-bloggers. In particular, some interesting movement patterns can be frequently observed in urban areas using our two measures such as aggregation and dispersion. We also present experimental results in determining urban characteristics from actual micro-blog dataset from the Twitter.

Interoperable augmented web browsing for exploring virtual media in real space

Although location-based information systems can now be accessed from mobile devices, searching for information from an outdoor environment requires a significant amount of effort given the time-consuming manipulations that need to be made. Augmented-Reality (AR) based systems can help resolve these issues by reducing the physical effort required to input keywords onto a small keyboard, and the mental effort necessary to review the search results. But most AR systems do not consider media sharing environments where individual systems are not interoperable in respect of their media and browsing abilities. In this paper, we focus on a media environment for interoperable augmented-reality systems, making the assumption that such devices will become more wide spread and that many people will share individual media in a real-world space. Furthermore, we describe such emerging scenarios and new research issues. We also present a model to manage the futuristic data, making it similar to existing physical media, and show a practical example using the system we have developed.

Detection of Unusually Crowded Places through Micro-Blogging Sites

Recently, micro-blogging sites such as Twitter have garnered a great deal of interests as an advanced form of blogging, where individuals can share their experiences, thoughts, feelings, etc, in real time. Additionally, mobile device based micro-blogging applications are now enabling the incorporation of extremely precise location information in the form of GPS-based coordinates. With the enormous number of micro-blogs being published all over the world, the resulting social media presents a novel dataset that can be used to survey our society on a global or local scale. In this paper, we propose an effective method for the detection of unusual crowding in physical locations. This method achieves the extraction of useful and interesting movement patterns reflecting the occurrence of critical events in a geographic region. In order to accomplish this, we analyze common patterns of occurrence in each region over a specified time period employing K-means based micro-blog clustering. Furthermore, we contrast unusual occurrence patterns with the movement patterns of micro-bloggers. Finally, we present an experimental evaluation of the proposed method using a real dataset collected from Twitter.

Layer-Based Media Integration for Mobile Mixed-Reality Applications

Rapidly evolving and widely-used smart phones provide many novel applications and services, making it possible to gather information from any location. Recent advances in technology have introduced many useful functions to assist users of outdoor mobile devices by sensing nearby conditions, such as the user’s current location and even the user’s slightest motions. Among these growing capabilities and their potential applications, the development of new mobile mixed-reality applications will need to consider various integration forms to be beneficial in both mobile device applications and services. In this paper, a layer-based media integration model for mobile mixed-reality applications is proposed to help developers gather diverse resources in a unified form, i.e., media, sensing, and even internal processing controls. In particular, to make it possible to identify and look for relevant information about real-world geospatial objects of interests in a very intuitive and direct manner by simply pointing towards an object, an object-identification layer is introduced. For each identified object, various types of services can be naturally integrated with the layer-based model; to take a snapshot or to jump to a related web page, or to collaborate with the popular touch control. The authors address an integration model that will significantly simplify mixed-reality application development work to establish a relationship between a geospatial object, its graphic image on a project screen, and user interaction. The improved simplicity, advantages, and new capabilities of the proposed model are also validated with two implemented applications, “Automatic Tagging Camera” and “Touch-based Mixed-Reality Web Search” based on the layer model.

Automatic Generation of Learning Channels by Using Semantic Relations among Lecture Slides and Recorded Videos for Self-Learning Systems

We present a method of automatically generating learning channels by using the semantic relations that implicitly exist in slides of a lecture that has accompanying recorded video. These days, many lecture videos with presentation files are shared over the Web from many universities through their own public sites. Although these materials are useful and valuable to many potential students, their use of sequential static media for self-learning purposes means there is still a lack of support for self-learners seeking learning channels suitable for various levels of understanding. Our newly generated learning channels let users easily focus on either highly detailed slides or introductory slides without needing to examine all of the data. We describe a prototype system supported by this learning-channel construction method.

Systematic measurement of human map-reading ability with street-view based navigation systems

Recently, the various navigation systems spread in many aspects of our daily life. It becomes necessary for the user to choose the one which is suitable for oneself from among these systems. However, it is difficult that users understand all these systems, because ability which understands map and guidance disagree by each user. Besides, there will be the user who cannot understand it even if a system more easily informed guidance for a user. We think that a uniform guidance is a problem for some people, because the ability to understand a map is different by a user. Therefore, it is necessary for us to investigate ability to understand guidance, that is, human map-reading ability. Moreover, we consider that it is important that a system analyzes the ability of user and shows weak points of user. In this paper, we developed a system to simulate whether we seem to do way-finding in real space, and first of all we performed outdoor experiments about pedestrian way-finding for measuring by the system. Next, we proposed three major indicators from pedestrian behaviors, and finally measured human map-reading ability in our system. Consequently, we think that will open up a new vista of the future for personal guidance services by our approach.

Web-based evidence excavation to explore the authenticity of local events

With the increasing employment of the Web in our daily lives, we often use it to find evidence of real-world events. The difference with normal web searches is that users want to reveal whether an event is true or false based on firm facts. In this paper, we propose a model to excavate real-world events from the Web, to manage the vestige in spatio-temporal space and to offer users reliable evidence of an event. We also describe a similarity measure between events to perform searching and clustering. A credibility estimation method, based on the trustworthiness of events and the authority of web sites, using a primary experiment is also presented.