8,149 research outputs found
TwitterMancer: Predicting Interactions on Twitter Accurately
This paper investigates the interplay between different types of user
interactions on Twitter, with respect to predicting missing or unseen
interactions. For example, given a set of retweet interactions between Twitter
users, how accurately can we predict reply interactions? Is it more difficult
to predict retweet or quote interactions between a pair of accounts? Also, how
important is time locality, and which features of interaction patterns are most
important to enable accurate prediction of specific Twitter interactions? Our
empirical study of Twitter interactions contributes initial answers to these
questions.
We have crawled an extensive dataset of Greek-speaking Twitter accounts and
their follow, quote, retweet, reply interactions over a period of a month.
We find we can accurately predict many interactions of Twitter users.
Interestingly, the most predictive features vary with the user profiles, and
are not the same across all users.
For example, for a pair of users that interact with a large number of other
Twitter users, we find that certain "higher-dimensional" triads, i.e., triads
that involve multiple types of interactions, are very informative, whereas for
less active Twitter users, certain in-degrees and out-degrees play a major
role. Finally, we provide various other insights on Twitter user behavior.
Our code and data are available at https://github.com/twittermancer/.
Keywords: Graph mining, machine learning, social media, social network
#mytweet via Instagram: Exploring User Behaviour across Multiple Social Networks
We study how users of multiple online social networks (OSNs) employ and share
information by studying a common user pool that use six OSNs - Flickr, Google+,
Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical
signature of users' sharing behaviour, showing how they exhibit distinct
behaviorial patterns on different networks. We also examine cross-sharing
(i.e., the act of user broadcasting their activity to multiple OSNs
near-simultaneously), a previously-unstudied behaviour and demonstrate how
certain OSNs play the roles of originating source and destination sinks.Comment: IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, 2015. This is the pre-peer reviewed version and the
final version is available at
http://wing.comp.nus.edu.sg/publications/2015/lim-et-al-15.pd
Using Social Media to Promote STEM Education: Matching College Students with Role Models
STEM (Science, Technology, Engineering, and Mathematics) fields have become
increasingly central to U.S. economic competitiveness and growth. The shortage
in the STEM workforce has brought promoting STEM education upfront. The rapid
growth of social media usage provides a unique opportunity to predict users'
real-life identities and interests from online texts and photos. In this paper,
we propose an innovative approach by leveraging social media to promote STEM
education: matching Twitter college student users with diverse LinkedIn STEM
professionals using a ranking algorithm based on the similarities of their
demographics and interests. We share the belief that increasing STEM presence
in the form of introducing career role models who share similar interests and
demographics will inspire students to develop interests in STEM related fields
and emulate their models. Our evaluation on 2,000 real college students
demonstrated the accuracy of our ranking algorithm. We also design a novel
implementation that recommends matched role models to the students.Comment: 16 pages, 8 figures, accepted by ECML/PKDD 2016, Industrial Trac
Leveraging Program Analysis to Reduce User-Perceived Latency in Mobile Applications
Reducing network latency in mobile applications is an effective way of
improving the mobile user experience and has tangible economic benefits. This
paper presents PALOMA, a novel client-centric technique for reducing the
network latency by prefetching HTTP requests in Android apps. Our work
leverages string analysis and callback control-flow analysis to automatically
instrument apps using PALOMA's rigorous formulation of scenarios that address
"what" and "when" to prefetch. PALOMA has been shown to incur significant
runtime savings (several hundred milliseconds per prefetchable HTTP request),
both when applied on a reusable evaluation benchmark we have developed and on
real applicationsComment: ICSE 201
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Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.
Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics
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