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Epidemiological modeling of news and rumors on Twitter
TLDR
We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors and demonstrate that our approach is accurate at capturing diffusion. Expand
'Beating the news' with EMBERS: forecasting civil unrest using open source indicators
TLDR
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Expand
Privacy Risks in Recommender Systems
TLDR
Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries. Expand
A systematic review of studies on forecasting the dynamics of influenza outbreaks
Forecasting the dynamics of influenza outbreaks could be useful for decision‐making regarding the allocation of public health resources. Reliable forecasts could also aid in the selection andExpand
The human is the loop: new directions for visual analytics
TLDR
Visual analytics is the science of marrying interactive visualizations and analytic algorithms to support exploratory knowledge discovery in large datasets. Expand
In-building wideband multipath characteristics at 2.5 and 60 GHz
TLDR
This paper contains measured data for 2.5 and 60 GHz in-building partition loss. Expand
Redescription Mining: Structure Theory and Algorithms
TLDR
We introduce a new data mining problem--redescription mining--that unifies considerations of conceptual clustering, constructive induction, and logical formula discovery. Expand
Misinformation Propagation in the Age of Twitter
TLDR
A quantitative analysis of tweets during the Ebola crisis reveals that lies, half-truths, and rumors can spread just like true news. Expand
Reasoning about sets using redescription mining
TLDR
Redescription mining is a newly introduced data mining problem that seeks to find subsets of data that afford multiple definitions. Expand
STED: semi-supervised targeted-interest event detectionin in twitter
TLDR
This paper presents STED, a semi-supervised system that helps users to automatically detect and interactively visualize events of a targeted type from twitter, such as crimes, civil unrests, and disease outbreaks. Expand
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