Abstract:
Post-marketing drug surveillance is a critical component of drug safety. Drug regulatory agencies such as the U.S. Food and Drug Administration (FDA) rely on voluntary reports from health professionals and consumers contributed to its FDA Adverse Event Reporting System (FAERS) to identify adverse drug events (ADEs). However, it is widely known that FAERS underestimates the prevalence of certain adverse events. Popular patient social media sites such as DailyStrength and PatientsLikeMe provide new information sources from which patient-reported ADEs may be extracted. In this study, we propose an analytical framework for extracting patient-reported adverse drug events from online patient forums. We develop a novel approach - the AZDrugMiner system - based on statistical learning to extract ad-verse drug events in patient discussions and identify reports from patient experiences. We evaluate our system using a set of manually annotated forum posts which show promising performance. We also examine correlations and differences between patient ADE reports extracted by our system and reports from FAERS. We conclude that patient social media ADE reports can be extracted effectively using our proposed framework. Those patient reports can reflect unique perspectives in treatment and be used to improve patient care and drug safety. © 2013 Springer-Verlag.
Abstract:
Improvised explosive device web pages represent a significant source of knowledge for security organizations. In this paper, we present significant improvements to our approach to the discovery and classification of IED related web pages in the Dark Web. We present a statistical feature ranking approach to the expansion of the keyword lexicon used to discover IED related web pages, which identified new relevant terms for inclusion. Additionally, we present an improved web page feature representation designed to better capture the structural and stylistic cues revealing of genres of communication, and a series of experiments comparing the classification performance of the new representation with our existing approach. ©2009 IEEE.
Abstract:
Information overload is a critical problem in World Wide Web. Category map developed based on Kohonen's self-organizing map (SOM) has been proven to be a promising browsing tool for the Web. The SOM algorithm automatically categorizes a large Internet information space into manageable sub-spaces. It compresses and transforms a complex information space into a two-dimensional graphical representation. Such graphical representation provides a user-friendly interface for users to explore the automatically generated mental model. However, as the amount of information increases, it is expected to increase the size of the category map accordingly in order to accommodate the important concepts in the information space. It results in increasing of visual load of the category map. Large pool of information is packed closely together on a limited size of displaying window, where local details are difficult to be clearly seen. In this paper, we propose the fisheye views and fractal views to support the visualization of category map. Fisheye views are developed based on the distortion approach while fractal views are developed based on the information reduction approach. The purpose of fisheye views are to enlarge the regions of interest and diminish the regions that are further away while maintaining the global structure. On the other hand, fractal views are an approximation mechanism to abstract complex objects and control the amount of information to be displayed. We have developed a prototype system and conducted a user evaluation to investigate the performance of fisheye views and fractal views. The results show that both fisheye views and fractal views significantly increase the effectiveness of visualizing category map. In addition, fractal views are significantly better than fisheye views but the combination of fractal views and fisheye views do not increase the performance compared to each individual technique. © 2002 Elsevier Science B.V. All rights reserved.
Abstract:
Social media is being increasingly used as a political communication channel. The web makes it easy to spread extreme opinions or ideologies that were once restricted to small groups. Terrorists and extremists use the web to deliver their extreme ideology to people and encourage them to get involved in fanatic behaviors. In this research, we aim to understand the mechanisms and properties of the exposure process to extreme opinions through these new publication methods, especially web forums. We propose the topic diffusion model for web forums, based on the SIR (Susceptible, Infective, and Recovered) model frequently used in previous research to analyze disease outbreaks and knowledge diffusion. The logistic growth of possible authors, the interaction between possible authors and current authors, and the influence decay of past authors are incorporated in a novel topic-based SIR model. From the proposed model we can estimate the maximum number of authors on a topic, the degree of infectiousness of a topic, and the rate describing how fast past authors lose influence over others. We apply the proposed model to a major international Jihadi forum where extreme ideology is expounded and evaluate the model on the diffusion of major violent topics. The fitting results show that it is plausible to describe the mechanism of violent topic diffusion in web forums with the SIR epidemic model. © 2011 IEEE.
