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From the User to the Medium: Neural Profiling Across Web Communities
Online communities provide a unique way for individuals to access information
from those in similar circumstances, which can be critical for health
conditions that require daily and personalized management. As these groups and
topics often arise organically, identifying the types of topics discussed is
necessary to understand their needs. As well, these communities and people in
them can be quite diverse, and existing community detection methods have not
been extended towards evaluating these heterogeneities. This has been limited
as community detection methodologies have not focused on community detection
based on semantic relations between textual features of the user-generated
content. Thus here we develop an approach, NeuroCom, that optimally finds dense
groups of users as communities in a latent space inferred by neural
representation of published contents of users. By embedding of words and
messages, we show that NeuroCom demonstrates improved clustering and identifies
more nuanced discussion topics in contrast to other common unsupervised
learning approaches
What's unusual in online disease outbreak news?
Background: Accurate and timely detection of public health events of
international concern is necessary to help support risk assessment and response
and save lives. Novel event-based methods that use the World Wide Web as a
signal source offer potential to extend health surveillance into areas where
traditional indicator networks are lacking. In this paper we address the issue
of systematically evaluating online health news to support automatic alerting
using daily disease-country counts text mined from real world data using
BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration
detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance
against expert moderated ProMED-mail postings. Results: We report sensitivity,
specificity, positive predictive value (PPV), negative predictive value (NPV),
mean alerts/100 days and F1, at 95% confidence interval (CI) for 287
ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period.
Results indicate that W2 had the best F1 with a slight benefit for day of week
effect over C2. In drill down analysis we indicate issues arising from the
granular choice of country-level modeling, sudden drops in reporting due to day
of week effects and reporting bias. Automatic alerting has been implemented in
BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news
alerts have the potential to enhance manual analytical methods by increasing
throughput, timeliness and detection rates. Systematic evaluation of health
news aberrations is necessary to push forward our understanding of the complex
relationship between news report volumes and case numbers and to select the
best performing features and algorithms
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