173 research outputs found
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
When Infodemic Meets Epidemic: a Systematic Literature Review
Epidemics and outbreaks present arduous challenges requiring both individual
and communal efforts. Social media offer significant amounts of data that can
be leveraged for bio-surveillance. They also provide a platform to quickly and
efficiently reach a sizeable percentage of the population, hence their
potential impact on various aspects of epidemic mitigation. The general
objective of this systematic literature review is to provide a methodical
overview of the integration of social media in different epidemic-related
contexts. Three research questions were conceptualized for this review,
resulting in over 10000 publications collected in the first PRISMA stage, 129
of which were selected for inclusion. A thematic method-oriented synthesis was
undertaken and identified 5 main themes related to social media enabled
epidemic surveillance, misinformation management, and mental health. Findings
uncover a need for more robust applications of the lessons learned from
epidemic post-mortem documentation. A vast gap exists between retrospective
analysis of epidemic management and result integration in prospective studies.
Harnessing the full potential of social media in epidemic related tasks
requires streamlining the results of epidemic forecasting, public opinion
understanding and misinformation propagation, all while keeping abreast of
potential mental health implications. Pro-active prevention has thus become
vital for epidemic curtailment and containment
Multifunctional Product Marketing Using Social Media Based on the Variable-Scale Clustering
Customers\u27 demands have become more dynamic and complicated owing to the functional diversity and lifecycle reduction of products which pushes enterprises to identify the real-time needs of distinct customers in a superior way. Meanwhile, social media turned as an emerging channel where customers often spontaneously can express their perceptions and thoughts about products promptly. This paper examines the customer satisfaction identification and improvement problem based on social media mining. First, we proposed the public opinion sensitivity index (POSI) to uncover target customers from extensive short-textual reviews. Subsequently, we presented a customer segmentation approach based on the sentiment analysis and the variable-scale clustering (VSC). The approach is able to get several customer clusters with the same satisfaction level where customers belonging to each cluster have similar interests. Finally, customer-centered marketing strategies and customer difference marketing campaigns are planned under the shadow of customer segmentation results. The experiments illustrate that our proposed method can support marketing decision marketing in practice that enriches the intention of the current customer relationship management
Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review
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