Young Bin Kim

Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies.

This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method.

Automatic player behavior analysis system using trajectory data in a massive multiplayer online game

This paper presents a new automated behavior analysis system using a trajectory clustering method for massive multiplayer online games (MMOGs). The description of a player’s behavior is useful information in MMOG development, but the monitoring and evaluation cost of player behavior is expensive. In this paper, we suggest an automated behavior analysis system using simple trajectory data with few monitoring and evaluation costs. We used hierarchical classification first, then applied an extended density based clustering algorithm for behavior analysis. We show the usefulness of our system using trajectory data from the commercial MMOG World of Warcraft (WOW). The results show that the proposed system can analyze player behavior and automatically generate insights on players’ experience from simple trajectory data.

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation

Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.

Predicting Virtual World User Population Fluctuations with Deep Learning.

This paper proposes a system for predicting increases in virtual world user actions. The virtual world user population is a very important aspect of these worlds; however, methods for predicting fluctuations in these populations have not been well documented. Therefore, we attempt to predict changes in virtual world user populations with deep learning, using easily accessible online data, including formal datasets from Google Trends, Wikipedia, and online communities, as well as informal datasets collected from online forums. We use the proposed system to analyze the user population of EVE Online, one of the largest virtual worlds.

Efficiently detecting outlying behavior in video-game players

In this paper, we propose a method for automatically detecting the times during which game players exhibit specific behavior, such as when players commonly show excitement, concentration, immersion, and surprise. The proposed method detects such outlying behavior based on the game players’ characteristics. These characteristics are captured non-invasively in a general game environment. In this paper, cameras were used to analyze observed data such as facial expressions and player movements. Moreover, multimodal data from the game players (i.e., data regarding adjustments to the volume and the use of the keyboard and mouse) was used to analyze high-dimensional game-player data. A support vector machine was used to efficiently detect outlying behaviors. We verified the effectiveness of the proposed method using games from several genres. The recall rate of the outlying behavior pre-identified by industry experts was approximately 70%. The proposed method can also be used for feedback analysis of various interactive content provided in PC environments.

Analyzing repetitive action in game based on sequence pattern matching

As games become more popular, procedures which can support the analysis and understanding of players’ behaviors are necessary for success of commercial games. This paper presents a log-based usability evaluation system to analyze user behavior in a gaming environment. We explore the potential of input log data for automated usability evaluation and visualization of player behavior in a game. We traced the keyboard input value and mouse movement of users using a sequence data mining technique in a gaming environment. And we also constructed 3D body meshes for the behavior analysis using Kinect interface. We visualized the data obtained by tracing and automatically searched repetitive patterns in the game and analyzed them. The result obtained from the analysis can be used for user interface optimization, fun evaluation, and the bot-detection field.

System for evaluating usability and user experience by analyzing repeated patterns

In this paper, a new system for evaluating interface usability through the analysis of repeated patterns is proposed. The system can be a valuable tool for verifying interfaces and in evaluating their usability by users, both of which are necessary stages in the development and operation of software. This paper concentrates on the repeated patterns that occur when users use an interface. Extracting these repeated patterns and analyzing them could enhance the development and usability of interfaces. Through experiments that applied the proposed system to several kinds of software, it was confirmed that problems with interfaces can be understood, and usability can be improved without requiring complicated analyses of user logs.

Visual simulation of rapidly freezing water based on crystallization

We propose a physics‐inspired simulation framework that expresses visual effects of flowing water frozen in glaze or directional icicles. The proposed ice model considers the direction of the water flow, which affects the growth of icicles. Water dynamics are computed using a conventional particle‐based simulation. Ice glaze and directional icicles are generated by incorporating our freezing solver. To determine whether a water particle is converted into ice or remains liquid, we compute the nucleation energy based on the humidity and water flow. The humidity is approximated as a virtual water film on object surfaces. The water flow is incorporated by introducing a growth direction vector to guide the direction of icicle growth. Ice‐generating regions can be controlled using 3‐D modeling tools such as Autodesk Maya or 3DS Max. Experiments showed that a realistic ice glaze was created on the surfaces of objects and that icicles grew in the direction of the water flow.

Predicting the Currency Market in Online Gaming via Lexicon-Based Analysis on Its Online Forum

Transactions involving virtual currencies are becoming increasingly common, including those in online games. In response, predicting the market price of a virtual currency is an important task for all involved, but it has not yet attracted much attention from researchers. This paper presents user opinions from online forums in a massive multiplayer online game (MMOG) setting widely used around the world. We propose a method for predicting the next-day rise and fall of the currency used in an MMOG environment. Based on analysis of online forum users’ opinions, we predict daily fluctuations in the price of a currency used in an MMOG setting. Focusing specifically on the World of Warcraft game, one of the most widely used MMOGs, we demonstrate the feasibility of predicting the fluctuation in value of virtual currencies used in this game community.