11,527 research outputs found
Recruiting from the network: discovering Twitter users who can help combat Zika epidemics
Tropical diseases like \textit{Chikungunya} and \textit{Zika} have come to
prominence in recent years as the cause of serious, long-lasting,
population-wide health problems. In large countries like Brasil, traditional
disease prevention programs led by health authorities have not been
particularly effective. We explore the hypothesis that monitoring and analysis
of social media content streams may effectively complement such efforts.
Specifically, we aim to identify selected members of the public who are likely
to be sensitive to virus combat initiatives that are organised in local
communities. Focusing on Twitter and on the topic of Zika, our approach
involves (i) training a classifier to select topic-relevant tweets from the
Twitter feed, and (ii) discovering the top users who are actively posting
relevant content about the topic. We may then recommend these users as the
prime candidates for direct engagement within their community. In this short
paper we describe our analytical approach and prototype architecture, discuss
the challenges of dealing with noisy and sparse signal, and present encouraging
preliminary results
Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling
Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue
and Zika in Brasil and other tropical regions has long been a priority for
governments in affected areas. Streaming social media content, such as Twitter,
is increasingly being used for health vigilance applications such as flu
detection. However, previous work has not addressed the complexity of drastic
seasonal changes on Twitter content across multiple epidemic outbreaks. In
order to address this gap, this paper contrasts two complementary approaches to
detecting Twitter content that is relevant for Dengue outbreak detection,
namely supervised classification and unsupervised clustering using topic
modelling. Each approach has benefits and shortcomings. Our classifier achieves
a prediction accuracy of about 80\% based on a small training set of about
1,000 instances, but the need for manual annotation makes it hard to track
seasonal changes in the nature of the epidemics, such as the emergence of new
types of virus in certain geographical locations. In contrast, LDA-based topic
modelling scales well, generating cohesive and well-separated clusters from
larger samples. While clusters can be easily re-generated following changes in
epidemics, however, this approach makes it hard to clearly segregate relevant
tweets into well-defined clusters.Comment: Procs. SoWeMine - co-located with ICWE 2016. 2016, Lugano,
Switzerlan
Finding Eyewitness Tweets During Crises
Disaster response agencies have started to incorporate social media as a
source of fast-breaking information to understand the needs of people affected
by the many crises that occur around the world. These agencies look for tweets
from within the region affected by the crisis to get the latest updates of the
status of the affected region. However only 1% of all tweets are geotagged with
explicit location information. First responders lose valuable information
because they cannot assess the origin of many of the tweets they collect. In
this work we seek to identify non-geotagged tweets that originate from within
the crisis region. Towards this, we address three questions: (1) is there a
difference between the language of tweets originating within a crisis region
and tweets originating outside the region, (2) what are the linguistic patterns
that can be used to differentiate within-region and outside-region tweets, and
(3) for non-geotagged tweets, can we automatically identify those originating
within the crisis region in real-time
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