6,693 research outputs found
The 'who' and 'what' of #diabetes on Twitter
Social media are being increasingly used for health promotion, yet the
landscape of users, messages and interactions in such fora is poorly
understood. Studies of social media and diabetes have focused mostly on
patients, or public agencies addressing it, but have not looked broadly at all
the participants or the diversity of content they contribute. We study Twitter
conversations about diabetes through the systematic analysis of 2.5 million
tweets collected over 8 months and the interactions between their authors. We
address three questions: (1) what themes arise in these tweets?, (2) who are
the most influential users?, (3) which type of users contribute to which
themes? We answer these questions using a mixed-methods approach, integrating
techniques from anthropology, network science and information retrieval such as
thematic coding, temporal network analysis, and community and topic detection.
Diabetes-related tweets fall within broad thematic groups: health information,
news, social interaction, and commercial. At the same time, humorous messages
and references to popular culture appear consistently, more than any other type
of tweet. We classify authors according to their temporal 'hub' and 'authority'
scores. Whereas the hub landscape is diffuse and fluid over time, top
authorities are highly persistent across time and comprise bloggers, advocacy
groups and NGOs related to diabetes, as well as for-profit entities without
specific diabetes expertise. Top authorities fall into seven interest
communities as derived from their Twitter follower network. Our findings have
implications for public health professionals and policy makers who seek to use
social media as an engagement tool and to inform policy design.Comment: 25 pages, 11 figures, 7 tables. Supplemental spreadsheet available
from http://journals.sagepub.com/doi/suppl/10.1177/2055207616688841, Digital
Health, Vol 3, 201
What Stops Social Epidemics?
Theoretical progress in understanding the dynamics of spreading processes on
graphs suggests the existence of an epidemic threshold below which no epidemics
form and above which epidemics spread to a significant fraction of the graph.
We have observed information cascades on the social media site Digg that spread
fast enough for one initial spreader to infect hundreds of people, yet end up
affecting only 0.1% of the entire network. We find that two effects, previously
studied in isolation, combine cooperatively to drastically limit the final size
of cascades on Digg. First, because of the highly clustered structure of the
Digg network, most people who are aware of a story have been exposed to it via
multiple friends. This structure lowers the epidemic threshold while moderately
slowing the overall growth of cascades. In addition, we find that the mechanism
for social contagion on Digg points to a fundamental difference between
information spread and other contagion processes: despite multiple
opportunities for infection within a social group, people are less likely to
become spreaders of information with repeated exposure. The consequences of
this mechanism become more pronounced for more clustered graphs. Ultimately,
this effect severely curtails the size of social epidemics on Digg.Comment: 8 pages, 10 figures, accepted in ICWSM1
A New Basis for Sparse PCA
The statistical and computational performance of sparse principal component
analysis (PCA) can be dramatically improved when the principal components are
allowed to be sparse in a rotated eigenbasis. For this, we propose a new method
for sparse PCA. In the simplest version of the algorithm, the component scores
and loadings are initialized with a low-rank singular value decomposition.
Then, the singular vectors are rotated with orthogonal rotations to make them
approximately sparse. Finally, soft-thresholding is applied to the rotated
singular vectors. This approach differs from prior approaches because it uses
an orthogonal rotation to approximate a sparse basis. Our sparse PCA framework
is versatile; for example, it extends naturally to the two-way analysis of a
data matrix for simultaneous dimensionality reduction of rows and columns. We
identify the close relationship between sparse PCA and independent component
analysis for separating sparse signals. We provide empirical evidence showing
that for the same level of sparsity, the proposed sparse PCA method is more
stable and can explain more variance compared to alternative methods. Through
three applications---sparse coding of images, analysis of transcriptome
sequencing data, and large-scale clustering of Twitter accounts, we demonstrate
the usefulness of sparse PCA in exploring modern multivariate data.Comment: 33 pages, 8 figure
Recent Advances in Social Data and Artificial Intelligence 2019
The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace
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