Significant efforts have been made to analyze the political stance or bias in news articles, especially as political polarization intensifies over the years. Recent advancements in machine learning have enabled researchers to develop various bias prediction models, which typically learn features not only from the text of the news articles but also from external knowledge. However, when training these models, the political bias label assigned to a news article is often based solely on the news source which published it. This approach can be problematic, as a news outlet with a particular political stance might publish an article that reflects a different political perspective. To address this issue, we first identify distinct text patterns associated with specific news sources or publishers, that are minimally relevant to predicting the political bias of a news article. We then conduct comprehensive experiments to investigate (i) whether existing models trained to predict political bias can also accurately predict the source, and (ii) whether these models change their predictions when a distinct pattern from a source with a different political stance is incorporated into a news article. Our experimental results reveal that all existing models tend to predict the source, even when trained solely to predict bias. Based on these findings, we propose a new deep learning model for political bias prediction that avoids learning source-indicative patterns specific to a given news source.

Over the years, the Internet has become a field in which a small number of large Internet companies dominate most of the Internet services. As users get used to using their services, the users’ generated content and the data about their online behaviors are concentrated in such companies. This phenomenon, called “data consolidation”, has become a serious problem, which makes the Internet society seek to decentralize the current Internet. The decentralized Internet aims to (i) prevent the concentration of user data in a few giant companies like Google and Facebook, and (ii) give users full ownership and control of their data. Various technical solutions that address the data consolidation problem have been proposed; however, those solutions focus on somewhat different scopes of the problem often from their limited viewpoints. The main contributions in this paper are the following. First, we survey the solutions relevant to Internet decentralization based on the following criteria: data consolidation, data ownership, and the privacy of user data. Second, we suggest a holistic reference framework from a functional viewpoint, while the prior proposals in the literature handle a limited set of requirements. Last, we seek to identify remaining research issues, considering additional requirements that have not been addressed in the existing solutions.

Understanding socio-economic systems in MMORPGs can provide an important implication on how people participate in the economy and how people interact with each other. In this paper, we model the socio-economic system of an Aion, a popular MMORPG, as a multi-layer graph. Using the dataset consisting of 94,870 users and their activity records spanning three months, we examine how economic activities are associated with social interactions, and find that social interactions like participating in a party or exchanging messages are highly correlated with the trade activities. We also find that virtual economy in Aion is heavily inclined to a small number of upper-class userswho play a crucial role in virtual economy. Our analysis on the upper-class users reveals that a significant portion of them reach at the max-level and tend to either (i) have many social interactions with others or (ii) play extremely much time with no social activity. We also reveal that there are some low-level upper-class users who gain much money but hardly socialize with others. Lastly, we show how upper-class users who are at low-levels, play the game extremely much more than others, or rarely interact with other users, are associated with the Real Money Trade (RMT), which may be an illegal behavior that gathers in-game money for exchanging into real-world money. We reveal that more than half of total money exchanged through the trade are associated with the upper-class users who involve in the RMT.