Yu-Ru Lin receives Honorable Mention for paper at IEEE VIST 2014
11/12/2014
The University of Pittsburgh’s School of Information Sciences is pleased to share that Yu-Ru Lin received an Honorable Mention for her paper #FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media at IEEE VIST 2014 in the VAST 2014: TVCG Track. Lin, an assistant professor in the Information Science and Technology program, co-authored the paper with Jian Zhao, Nan Cao, Zhen Wen, Yale Song, and Christopher Collins.
The paper presents “FluxFlow, an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media. The proposed system, FluxFlow, aims at distilling valuable social signals from the huge crowd’s messages through enhancing data analysts’ capability of discerning the anomalous information behaviors, such as the spreading of rumors or misinformation, from the rest that are more conventional patterns, such as popular topics and newsworthy events, in a timely fashion. An extensive evaluation of FluxFlow with real datasets containing the Twitter feeds captured during significant events such as Hurricane Sandy shows that the integration of machine learning algorithms for detecting anomalies and novel visualization designs well supports analysts' comprehension of the analytical results with intuitions that help discover insights in the data.”
Watch a video review of the paper to learn more.
The IEEE VAST conference is one of the premiere conferences in the visual analytics field. As explained in the call for participation, “Visual analytics is the science of analytical reasoning supported by highly interactive visual interfaces. People use visual analytics tools and techniques in different academic disciplines as well as in real world environments (e.g., media, business, industry, government, etc.) to synthesize information into knowledge; derive insight from massive, dynamic, and often conflicting data; detect the expected and discover the unexpected; provide timely, defensible, and understandable assessments; and communicate assessments effectively for action. The issues stimulating this body of research provide a grand challenge in science: turning information overload into a significant opportunity.
Visual analytics requires interdisciplinary science, going beyond traditional scientific and information visualization to include statistics, mathematics, knowledge representation, management and discovery technologies, cognitive and perceptual sciences, decision sciences, and more.”
Dr. Yu-Ru Lin’s research interests include human and social dynamics; computational approaches for mining and visualizing large-scale, time-varying, heterogeneous, multi-relational, and semi-structured data.
Learn more about Dr. Lin on her bio page or home page.
