Predicting Virtual World User Population Fluctuations with Deep Learning

<div><p>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.</p></div

EVE Online Forum Opinion Analysis Example

<p>EVE Online Forum Opinion Analysis Example</p

Pearson Correlation Coefficient Result

<p>Pearson Correlation Coefficient Result</p

Z-scores of fluctuations in population and results of opinion analysis.

<p>Some opinions show a trend similar to that of fluctuations in the population.</p

Example of a deep learning dataset.

<p>The z-score for data from the previous 20 days was used as the values A–J, which indicate the value of the sum of forum opinion on a given date. V–Z denote formal data values (number of topics, sum of replies, sum of views, Google Trends value, and Wikipedia page views) on a given date.</p

Experimental results of predicted fluctuations in the EVE Online user population

<p>Experimental results of predicted fluctuations in the EVE Online user population</p

System overview.

<p>System overview.</p

Summary of crawled data

<p>Summary of crawled data</p

Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies

<div><p>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.</p></div

Experimental result of predicted Ripple price fluctuation.

<p>Experimental result of predicted Ripple price fluctuation.</p