Souneil Park

NewsCube: delivering multiple aspects of news to mitigate media bias

The bias in the news media is an inherent flaw of the news production process. The resulting bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as Iraq war. It is very difficult, if not impossible, for readers to have penetrating views on realities against such bias. This paper presents NewsCube, a novel Internet news service aiming at mitigating the effect of media bias. NewsCube automatically creates and promptly provides readers with multiple classified viewpoints on a news event of interest. As such, it effectively helps readers understand a fact from a plural of viewpoints and formulate their own, more balanced viewpoints. While media bias problem has been studied extensively in communications and social sciences, our work is the first to develop a news service as a solution and study its effect. We discuss the effect of the service through various user studies.

HiCon: a hierarchical context monitoring and composition framework for next-generation context-aware services

This article presents a hierarchical context monitoring and composition framework that effectively supports next-generation context-aware services. The upcoming ubiquitous space will be covered with innumerable sensors and tiny devices, which ceaselessly pump out a huge volume of data. This data gives us an opportunity for numerous proactive and intelligent services. The services require extensive understanding of rich and comprehensive contexts in real time. The framework provides three hierarchical abstractions: PocketMon (personal), HiperMon (regional), and EGI (global). The framework provides effective approaches to combining context from each level, thereby allowing us to create a rich set of applications, not possible otherwise. It deals with an extensively broad spectrum of contexts, from personal to worldwide in terms of scale, and from crude to highly processed in terms of complexity. It also facilitates efficient context monitoring and addresses the performance issues, achieving a high level of scalability. We have prototyped the proposed framework and several applications running on top of it in order to demonstrate its effectiveness.

Demo: ExerLink - enabling pervasive social exergames with heterogeneous exercise devices

We envision that diverse social exercising games, or exergames, will emerge, featuring much richer interactivity with immersive game play experiences. Further, the recent advances of mobile devices and wireless networking will make such social engagement more pervasive - people carry portable exergame devices (e.g., jump ropes) and interact with remote users anytime, anywhere. Towards this goal, we explore the potential of using heterogeneous exercise devices as game controllers for a multi-player social exergame; e.g., playing a boat paddling game with two remote exercisers (one with a jump rope, and the other with a treadmill). In this paper, we propose a novel platform called ExerLink that converts exercise intensity to game inputs and intelligently balances intensity/delay variations for fair game play experiences. We report the design considerations and guidelines obtained from the design and development processes of game controllers. We validate the efficacy of game controllers and demonstrate the feasibility of social exergames with heterogeneous exercise devices via extensive human subject studies.

Aspect-level news browsing: understanding news events from multiple viewpoints

Aspect-level news browsing provides readers with a classified view of news articles with different viewpoints. It facilitates active interactions with which readers easily discover and compare diverse existing biased views over a news event. As such, it effectively helps readers understand the event from a plural of viewpoints and formulate their own, more balanced viewpoints free from specific biased views. Realizing aspect-level browsing raises important challenges, mainly due to the lack of semantic knowledge with which to abstract and classify the intended salient aspects of articles. We first demonstrate the feasibility of aspect-level news browsing through user studies. We then deeply look into the news article production process and develop framing cycle-aware clustering. The evaluation results show that the developed method performs classification more accurately than other methods.

A Computational Framework for Media Bias Mitigation

Bias in the news media is an inherent flaw of the news production process. The bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as the Iraq war. This article presents NewsCube, a novel Internet news service which aims to mitigate the effect of media bias. NewsCube automatically creates and promptly provides readers with multiple classified views on a news event. As such, it helps readers understand the event from a plurality of views and to formulate their own, more balanced, viewpoints. The media bias problem has been studied extensively in mass communications and social science. This article reviews related mass communication and journalism studies and provides a structured view of the media bias problem and its solution. We propose media bias mitigation as a practical solution and demonstrate it through NewsCube. We evaluate and discuss the effectiveness of NewsCube through various performance studies.

Structural strengthening and damage behaviors of hybrid sprayed fiber-reinforced polymer composites containing carbon fiber cores:

Recently, composite materials of a hybrid strategy are being developed to reduce the vulnerability of partial rupture failure. Simultaneously, the demand of a modelling method particularly for simulating the behavior of these materials is also emerging. This paper presents novel hybrid sprayed fiber-reinforced polymer composites containing carbon fiber cores developed for strengthening reinforced concrete structures, and a theoretical approach to model these materials. A micromechanical formulation that accounts for the hybrid characteristic of the composites was proposed to predict the overall performance of the composites. The derived model was then implemented into the nonlinear finite element codes to predict the structural behavior of the reinforced concrete beams retrofitted with the composites subjected to loading. A series of three-point bending tests of specimens were carried out to validate the predictability of the modeling technique. The obtained test results were comparatively explored with discussions of the theoretical approaches.

Disputant Relation-Based Classification for Contrasting Opposing Views of Contentious News Issues

Contentious news issues, such as the health care reform debate, draw much interest from the public; however, it is not simple for an ordinary user to search and contrast the opposing arguments and have a comprehensive understanding of the issues. Providing a classified view of the opposing views of the issues can help readers easily understand the issue from multiple perspectives. We present a disputant relation-based method for classifying news articles on contentious issues. We observe that the disputants of a contention are an important feature for understanding the discourse. It performs unsupervised classification on news articles based on disputant relations, and helps readers intuitively view the articles through the opponent-based frame and attain balanced understanding, free from a specific biased viewpoint. The method is performed in three stages: disputant extraction, disputant partitioning, and article classification. We apply a modified version of HITS algorithm and an SVM classifier trained with pseudorelevant data for article analysis. We conduct an accuracy analysis and an upper-bound analysis for the evaluation of the method.

Design and Implementation of a Middleware for Development and Provision of Stream-Based Services

This paper proposes MISSA, a novel middleware to facilitate the development and provision of stream-based services in emerging pervasive environments. The stream-based services utilize voluminous and continuously updated data streams as their input. The characteristics of data streams bring new requirements on the development and provision of the services. To satisfy the requirements, a unique service model and a runtime system are designed in MISSA. The key concept of our service model is to separate service logic from handling data streams. This significantly mitigates the burden on service developers by allowing them to only concentrate on the service logic. Job of handling data streams is completely delegated to the runtime. In this paper, we first present the importance of stream-based services and their requirements. Also, we describe a best route finding service as an example to motivate the need for MISSA. Then, we envision overall architecture for provisioning of stream-based services and detail our design of service model and runtime. Lastly, we demonstrate the efficiency of service model and runtime through experiments.

Mitigating media bias: a computational approach

The bias in the news media is an inherent flaw of the news production process, spanning news gathering, writing, and editing stages. Producers subjective valuation, wittingly or unwittingly, takes place during the daily production process. The resulting bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as Iraq war [3]. It is very difficult, if not impossible, for readers to have penetrating views on realities against such bias. We propose NewsCube, a novel Internet news service framework aiming at mitigating the effect of media bias. NewsCube is designed to automatically create and promptly provide readers with multiple classified viewpoints on a news event of interest. It helps readers to easily discover rich facts and compare diverse biased views on the event. In this paper, we discuss the design of the NewsCube framework and introduce novel approaches which are under development.