42,363 research outputs found
Measuring Online Social Bubbles
Social media have quickly become a prevalent channel to access information,
spread ideas, and influence opinions. However, it has been suggested that
social and algorithmic filtering may cause exposure to less diverse points of
view, and even foster polarization and misinformation. Here we explore and
validate this hypothesis quantitatively for the first time, at the collective
and individual levels, by mining three massive datasets of web traffic, search
logs, and Twitter posts. Our analysis shows that collectively, people access
information from a significantly narrower spectrum of sources through social
media and email, compared to search. The significance of this finding for
individual exposure is revealed by investigating the relationship between the
diversity of information sources experienced by users at the collective and
individual level. There is a strong correlation between collective and
individual diversity, supporting the notion that when we use social media we
find ourselves inside "social bubbles". Our results could lead to a deeper
understanding of how technology biases our exposure to new information
MedlinePlus??: The National Library of Medicine?? Brings Quality Information to Health Consumers
The National Library of Medicine???s (NLM??) MedlinePlus?? is a high-quality
gateway to consumer health information from NLM, the National Institutes
of Health (NIH), and other authoritative organizations. For decades,
NLM has been a leader in indexing, organizing, and distributing health
information to health professionals. In creating MedlinePlus, NLM uses
years of accumulated expertise and technical knowledge to produce an
authoritative, reliable consumer health Web site. This article describes the
development of MedlinePlus???its quality control processes, the integration
of NLM and NIH information, NLM???s relationship to other institutions,
the technical and staffing infrastructures, the use of feedback for quality
improvement, and future plans.published or submitted for publicatio
Why People Search for Images using Web Search Engines
What are the intents or goals behind human interactions with image search
engines? Knowing why people search for images is of major concern to Web image
search engines because user satisfaction may vary as intent varies. Previous
analyses of image search behavior have mostly been query-based, focusing on
what images people search for, rather than intent-based, that is, why people
search for images. To date, there is no thorough investigation of how different
image search intents affect users' search behavior.
In this paper, we address the following questions: (1)Why do people search
for images in text-based Web image search systems? (2)How does image search
behavior change with user intent? (3)Can we predict user intent effectively
from interactions during the early stages of a search session? To this end, we
conduct both a lab-based user study and a commercial search log analysis.
We show that user intents in image search can be grouped into three classes:
Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals
different user behavior patterns under these three intents, such as first click
time, query reformulation, dwell time and mouse movement on the result page.
Based on user interaction features during the early stages of an image search
session, that is, before mouse scroll, we develop an intent classifier that is
able to achieve promising results for classifying intents into our three intent
classes. Given that all features can be obtained online and unobtrusively, the
predicted intents can provide guidance for choosing ranking methods immediately
after scrolling
Can electoral popularity be predicted using socially generated big data?
Today, our more-than-ever digital lives leave significant footprints in
cyberspace. Large scale collections of these socially generated footprints,
often known as big data, could help us to re-investigate different aspects of
our social collective behaviour in a quantitative framework. In this
contribution we discuss one such possibility: the monitoring and predicting of
popularity dynamics of candidates and parties through the analysis of socially
generated data on the web during electoral campaigns. Such data offer
considerable possibility for improving our awareness of popularity dynamics.
However they also suffer from significant drawbacks in terms of
representativeness and generalisability. In this paper we discuss potential
ways around such problems, suggesting the nature of different political systems
and contexts might lend differing levels of predictive power to certain types
of data source. We offer an initial exploratory test of these ideas, focussing
on two data streams, Wikipedia page views and Google search queries. On the
basis of this data, we present popularity dynamics from real case examples of
recent elections in three different countries.Comment: To appear in Information Technolog
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