Descriptive and visual summaries of disaster events using artificial intelligence techniques: case studies of Hurricanes Harvey, Irma, and Maria
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TLDR
This work shows that textual and imagery content on social media provide complementary information useful to improve situational awareness and proposes a methodological approach that combines several computational techniques effectively in a unified framework to help humanitarian organisations in their relief efforts.Abstract:
People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several ...read more
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Using AI and Social Media Multimodal Content for Disaster Response and Management: Opportunities, Challenges, and Future Directions
TL;DR: Various applications and opportunities of SM multimodal data, latest advancements, current challenges, and future directions for the crisis informatics and other related research fields are highlighted.
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Rapid relevance classification of social media posts in disasters and emergencies: A system and evaluation featuring active, incremental and online learning
TL;DR: A well-performing classifier based on the European floods dataset is achieved by only requiring a quarter of labeled data compared to the traditional batch learning approach, and a substantial improvement could be determined on the BASF SE incident dataset.
Journal ArticleDOI
Quality management in humanitarian operations and disaster relief management: a review and future research directions.
TL;DR: An extensive literature review in the field of quality management in humanitarian operations and disaster relief management, comprising 61 articles published from 2009 to 2018, leads to the identification of enablers, challenges, and theory development approaches that must be addressed.
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Examine the effects of neighborhood equity on disaster situational awareness: Harness machine learning and geotagged Twitter data
TL;DR: By incorporating with aggregated sociodemographic data, geotagged Twitter data can also be used to understand disaster SA from the perspective of social equity, and it is found that the sentiment of a tweet in a black neighborhood could be less likely to be negative.
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VOST: A case study in voluntary digital participation for collaborative emergency management
TL;DR: An analysis of the structural, procedural and technical requirements of VOSTs for the collaborative deployment with emergency management agencies during the Grand Depart of the Tour de France 2017 in Dusseldorf is contained.
References
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Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Book
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TL;DR: The eagerly anticipated fourth edition of the title that pioneered the comparison of qualitative, quantitative, and mixed methods research design, John W, Creswell as discussed by the authors, includes a preliminary consideration of philosophical assumptions, a review of the literature, an assessment of the use of theory in research approaches, and reflections about the importance writing and ethics in scholarly inquiry.
Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Journal ArticleDOI
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
TL;DR: A new graphical display is proposed for partitioning techniques, where each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation, and provides an evaluation of clustering validity.