833 research outputs found
On analyzing geotagged tweets for location-based patterns
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
Understanding Citizen Reactions and Ebola-Related Information Propagation on Social Media
In severe outbreaks such as Ebola, bird flu and SARS, people share news, and
their thoughts and responses regarding the outbreaks on social media.
Understanding how people perceive the severe outbreaks, what their responses
are, and what factors affect these responses become important. In this paper,
we conduct a comprehensive study of understanding and mining the spread of
Ebola-related information on social media. In particular, we (i) conduct a
large-scale data-driven analysis of geotagged social media messages to
understand citizen reactions regarding Ebola; (ii) build information
propagation models which measure locality of information; and (iii) analyze
spatial, temporal and social properties of Ebola-related information. Our work
provides new insights into Ebola outbreak by understanding citizen reactions
and topic-based information propagation, as well as providing a foundation for
analysis and response of future public health crises.Comment: 2016 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining (ASONAM 2016
On the Accuracy of Hyper-local Geotagging of Social Media Content
Social media users share billions of items per year, only a small fraction of
which is geotagged. We present a data- driven approach for identifying
non-geotagged content items that can be associated with a hyper-local
geographic area by modeling the location distributions of hyper-local n-grams
that appear in the text. We explore the trade-off between accuracy, precision
and coverage of this method. Further, we explore differences across content
received from multiple platforms and devices, and show, for example, that
content shared via different sources and applications produces significantly
different geographic distributions, and that it is best to model and predict
location for items according to their source. Our findings show the potential
and the bounds of a data-driven approach to geotag short social media texts,
and offer implications for all applications that use data-driven approaches to
locate content.Comment: 10 page
Exploring Social Media for Event Attendance
Large popular events are nowadays well reflected in social media fora (e.g. Twitter), where people discuss their interest in participating in the events. In this paper we propose to exploit the content of non-geotagged posts in social media to build machine-learned classifiers able to infer users' attendance of large events in three temporal periods: before, during and after an event. The categories of features used to train the classifier reflect four different dimensions of social media: textual, temporal, social, and multimedia content. We detail the approach followed to design the feature space and report on experiments conducted on two large music festivals in the UK, namely the VFestival and Creamfields events. Our attendance classifier attains very high accuracy with the highest result observed for the Creamfields dataset ~87% accuracy to classify users that will participate in the event
Scaling of city attractiveness for foreign visitors through big data of human economical and social media activity
Scientific studies investigating laws and regularities of human behavior are
nowadays increasingly relying on the wealth of widely available digital
information produced by human social activity. In this paper we leverage big
data created by three different aspects of human activity (i.e., bank card
transactions, geotagged photographs and tweets) in Spain for quantifying city
attractiveness for the foreign visitors. An important finding of this papers is
a strong superlinear scaling of city attractiveness with its population size.
The observed scaling exponent stays nearly the same for different ways of
defining cities and for different data sources, emphasizing the robustness of
our finding. Temporal variation of the scaling exponent is also considered in
order to reveal seasonal patterns in the attractivenessComment: 8 pages, 3 figures, 1 tabl
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