Target oriented tweets monitoring system during natural disasters

Win, Si Si Mar; Aung, Than Nwe

doi:10.1109/icis.2017.7959984

Cited by 16 publications

(19 citation statements)

References 6 publications

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“…Current content classification models mostly rely on features derived from text only (Arthur et al 2018;Verma et al 2011;David, Ong, and Legara 2016;Vieweg et al 2010;Cho, Jung, and Park 2013;Qu, Wu, and Wang 2009;Qu et al 2010;Chowdhury et al 2013;Kongthon et al 2012;Vieweg 2011;Olteanu, Vieweg, and Castillo 2015;Imran, Mitra, and Srivastava 2016;Acar and Muraki 2011;Smith 2010;Wang, Ye, and Tsou 2016;Bruns et al 2012;Shaw et al 2013;Gurman and Ellenberger 2015;Temnikova and Castillo 2015;Herfort et al 2013;Landwehr et al 2016;Spence et al 2015;Sutton et al 2014;Takahashi, Tandoc, and Carmichael 2015;Valenzuela, Puente, and Flores 2017;Wang and Zhuang 2017;Win and Aung 2017;Yu et al 2019). These models are often limited in accuracy due to the variability, uncertainty and succinct characteristics of social media messages.…”

Section: Content Classificationmentioning

confidence: 99%

Situational awareness extraction: a comprehensive review of social media data classification during natural hazards

Vongkusolkit

Huang

2020

Annals of GIS

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Social media (e.g., Twitter and Facebook) can be regarded as vital sources of information during disasters to improve situational awareness (SA) and disaster management since they play a significant role in the rapid spread of information in the event of a disaster. Due to the volume of data is far beyond the capabilities of manual examination, existing works utilize natural language processing methods based on keywords, or classification models relying on features derived from text and other metadata (e.g., user profiles) to extract social media data contributing to SA and automatically categorize them into the relevant classes (e.g., damage and donation). However, the design of the classification schema and the associated information extraction methods are far less than straightforward and highly depend on: (1) the event type, (2) the study or analysis purpose, and (3) the social media platform used. To this end, this paper reviews the literature for extracting social media data and provides an overview of classification schemas that have been used to assess SA in events involving natural hazards from five different analytical perspectives (content, temporal, user, sentiment, and spatiotemporal) by discussing the prevalent topic categories, disaster event types, purpose of studies, and platforms utilized from each schema. Finally, this paper summarizes classification methods, and platforms that are most commonly used for each disaster event type, and outlines a research agenda with recommendations for future innovations.

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Section: Content Classificationmentioning

confidence: 99%

Situational awareness extraction: a comprehensive review of social media data classification during natural hazards

Vongkusolkit

Huang

2020

Annals of GIS

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“…Win et al [5] proposed a system which successfully annotated the Myanmar Earthquake data at 75% accuracy on average. The method combines feature extraction using NLP and machine learning approach to obtain the annotated datasets to improve disaster response efforts.…”

Section: Related Workmentioning

confidence: 99%

“…During some natural disasters like events, the tweets are of diverse category based on its context; which triggers to the need of tweets classification for extraction of useful and relevant information [5]. It is also challenging to verify the credibility of the information extracted from microblogs from other reliable sources [6].…”

Section: Introductionmentioning

confidence: 99%

Extracting Information from Microblogs Posted During Natural Disasters

Vishwakarma*,

Chandel,

Hora

2020

IJITEE

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Social Networking websites plays an important role in our life. The usage of the above websites is in every domain of our lives and they have increasingly infused itself into daily life. In recent years, the social networking websites such as Twitter, Facebook, are used in natural disasters. Many features have been included in Twitter for fast responses in such kind of unexpected events. This paper is based on the experiments performed on the 2017 Microblog Track provided by Forum of Information Retrieval & Evaluation. The Classification schemes are used with two predefined labels as need and availability. The various pre-processing and natural language processing techniques are applied before the training of the model. The experiments showed that the classification accuracy is increased when the term weight is modified by using the information gain method and using the SVM classifier. This system automatically annotated the FIRE-2015 dataset of microblog track with 97% accuracy.

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“…The socio-economic sphere needs reengineering (Anping, 2012). It is necessary to review, evaluate, analyze and take measures to eliminate its disadvantages (Win & Aung, 2017). We analyzed social problems of the Chechen Republic, because this topic is important for our region.…”

Section: Introductionmentioning

confidence: 99%