Byungkyu Kang

Understanding Information Credibility on Twitter

Increased popularity of microblogs in recent years brings about a need for better mechanisms to extract credible or otherwise useful information from noisy and large data. While there are a great number of studies that introduce methods to find credible data, there is no accepted credibility benchmark. As a result, it is hard to compare different studies and generalize from their findings. In this paper, we argue for a methodology for making such studies more useful to the research community. First, the underlying ground truth values of credibility must be reliable. The specific constructs used to define credibility must be carefully defined. Secondly, the underlying network context must be quantified and documented. To illustrate these two points, we conduct a unique credibility study of two different data sets on the same topic, but with different network characteristics. We also conduct two different user surveys, and construct two additional indicators of credibility based on retweet behavior. Through a detailed statistical study, we first show that survey based methods can be extremely noisy and results may vary greatly from survey to survey. However, by combining such methods with retweet behavior, we can incorporate two signals that are noisy but uncorrelated, resulting in ground truth measures that can be predicted with high accuracy and are stable across different data sets and survey methods. Newsworthiness of tweets can be a useful frame for specific applications, but it is not necessary for achieving reliable credibility ground truth measurements.

Believe it or Not? Analyzing Information Credibility in Microblogs

This paper identifies and evaluates key factors that influence credibility perception in microblogs. Specifically, we report on a demographic survey (N=81) followed by a user experiment (N=102) in order to answer the following research questions: (1) What are the important cues that contribute to information being perceived as credible? and (2) To what extent is such a quantification portable across different microblogging platforms? To answer the second question, we study two popular microblogs, Reddit and Twitter. Key results include that significant effects of individual factors can be isolated, are portable, and that metadata and image type elements are, in general, the strongest influencing factors in credibility assessments.

What am I not Seeing? An Interactive Approach to Social Content Discovery in Microblogs

In this paper, we focus on the informational and user experience benefits of user-driven topic exploration in microblog communities, such as Twitter, in an inspectable, controllable and personalized manner. To this end, we introduce “HopTopics” – a novel interactive tool for exploring content that is popular just beyond a user’s typical information horizon in a microblog, as defined by the network of individuals that they are connected to. We present results of a user study (N=122) to evaluate HopTopics with varying complexity against a typical microblog feed in both personalized and non-personalized conditions. Results show that the HopTopics system, leveraging content from both the direct and extended network of a user, succeeds in giving users a better sense of control and transparency. Moreover, participants had a poor mental model for the degree of novel content discovered when presented with non-personalized data in the Inspectable interface.

The Full Story: Automatic detection of unique news content in Microblogs

In recent years a large portion of news dissemination has shifted from traditional outlets to individual users on platforms such as Twitter and Facebook. Accordingly, methods for detecting newsworthy and otherwise useful information on these platforms have received a lot of research attention. In this paper, we present a novel algorithm to automatically capture core differences in newsworthy content between microblog and traditional news media streams and discuss why it is difficult to capture such information using traditional text-based search mechanisms. We describe an experiment to tune and evaluate the algorithm using a corpus of 35 million Twitter messages and 6,112 New York Times articles on a variety of topics. Finally, we describe an online user study (N=200) to evaluate user perceptions of content recommended by our algorithm. Results show significant differences in user perception of newsworthiness and uniqueness of content from our algorithm.

Poster: Real time hand pose recognition with depth sensors for mixed reality interfaces

We present a method for predicting articulated hand poses in real-time with a single depth camera, such as the Kinect or Xtion Pro, for the purpose of interaction in a Mixed Reality environment and for studying the effects of realistic and non-realistic articulated hand models in a Mixed Reality simulator. We demonstrate that employing a randomized decision forest for hand recognition benefits real-time applications without the typical tracking pitfalls such as reinitialization. This object recognition approach to predict hand poses results in relatively low computation, high prediction accuracy and sets the groundwork needed to utilize articulated hand movements for 3D tasks in Mixed Reality workspaces.

Through the Grapevine: A Comparison of News in Microblogs and Traditional Media

In recent years the greater part of news dissemination has shifted from traditional news media to individual users on microblogs such as Twitter and Reddit. Therefore, there has been increasing research effort on how to automatically detect newsworthy and otherwise useful information on these platforms.

Mining Attribute-Specific Ratings from Reviews of Cosmetic Products

In the cosmetics domain, many online sellers support user-provided product reviews. It has been shown that reviews have a profound effect on product conversion rates. Reviews of cosmetic products carry particular importance in purchasing decisions because of their personal nature, and particularly because of the potential for irritation with unsuitable products. In this paper, we propose a method for automatic scoring of various aspects of cosmetic item review texts based on a curated dictionary of expressions from a corpus of real world online reviews. Results and discussion of a user experiment to evaluate the approach are presented. In particular, we find that a co-occurrence approach improved coverage of reviews, and that our automated approach predicted attributes in manually annotated ground truth with an accuracy of 79%.

Interactive interfaces for complex network analysis: An information credibility perspective

This paper discusses and evaluates the impact of visualization and interaction strategies for extracting quality information from data in complex networks such as microblogs. Two different approaches to interactive visual representations of data are discussed: an interactive node-link graph and a novel approach where content is separated into interactive lists based on data properties. To assess the two approaches in terms of information credibility, the TopicNets system is compared with “Fluo”, a novel system. An analysis scenario is performed through each system on a set of big data filtered from the Twitter message service. The exposure of content, trade-offs between algorithmic power and interaction complexity, methods for content filtering, and strategies for recommending new content are assessed for each system. Fluo is found to improve on TopicNets ability to efficiently find relevant content primarily by providing a more structured content view, however, TopicNets is more customizable and boasts features which are critical for an expert analyst. The paper concludes with general insights on interface design for information filtering systems to maximize perceived quality of information.