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Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring
[chapter]
2018
Community-Oriented Policing and Technological Innovations
Preserved Fulltext
Related Work A variety of methods have been applied to social media in the past in order to classify crisis-related tweets and to obtain useful information. ...
Caragea et al. (2016) and Nguyen et al. (2016) use online deep learning to classify tweets as informative or not informative. ...
doi:10.1007/978-3-319-89294-8_4
fatcat:alb5ihq2xvhqnh4w64kqgwbggm
Applications of Online Deep Learning for Crisis Response Using Social Media Information
[article]
2016
arXiv
pre-print
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We test our models using a crisis-related real-world Twitter dataset. ...
In this paper, we propose to use Deep Neural Network (DNN) to address two types of information needs of response organizations: 1) identifying informative tweets and 2) classifying them into topical classes ...
RELATED WORK Recent studies have shown the usefulness of crisis-related data on social media for disaster response and management [1, 22, 24] . ...
arXiv:1610.01030v2
fatcat:fekss7cixfarjg7lbajt3hzwpa
Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media
[chapter]
2017
Lecture Notes in Computer Science
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In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. ...
When crises hit, many flog to social media to share or consume information related to the event. ...
In this section we describe our proposed Sem-CNN model, which is a semantically enriched deep learning model for identifying crisis-related information categories on Twitter. ...
doi:10.1007/978-3-319-68288-4_9
fatcat:ntetx4pzizgolpp6sskimipjve
Emotion classification of social media posts for estimating people's reactions to communicated alert messages during crises
2014
Security Informatics
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One of the key factors influencing how people react to and behave during a crisis is their digital or non-digital social network, and the information they receive through this network. ...
Those tweets have been utilized for building machine learning classifiers able to automatically classify new tweets. ...
We are not aware of any previous attempts to use machine learning for emotion recognition of crisis-related tweets. ...
doi:10.1186/s13388-014-0007-3
fatcat:irtqk3ftvjbuxpanvibeh3u574
Deep Learning and Word Embeddings for Tweet Classification for Crisis Response
[article]
2019
arXiv
pre-print
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Tradition tweet classification models for crisis response focus on convolutional layers and domain-specific word embeddings. ...
We evaluate four tweet classification models on CrisisNLP dataset and obtain comparable results which indicates that general-purpose word embedding such as GloVe can be used instead of domain-specific ...
Tweet classification for crisis response is a text classification task that aims at identifying if a tweet is related to a specific type of predefined informative classes. ...
arXiv:1903.11024v1
fatcat:wtymhuqpnfcbtbhuxqhkwxnzze
Identification of Fine-Grained Location Mentions in Crisis Tweets
[article]
2021
arXiv
pre-print
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Identification of fine-grained location mentions in crisis tweets is central in transforming situational awareness information extracted from social media into actionable information. ...
Most prior works have focused on identifying generic locations, without considering their specific types. ...
Many recent studies have focused on identifying informative tweets posted by individuals affected by a crisis, and classifying those tweets according to situational awareness categories useful for crisis ...
arXiv:2111.06334v1
fatcat:gfqs2b7nmncs5dl2btvuv62of4
A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria
[article]
2018
arXiv
pre-print
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Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster ...
Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. ...
ACKNOWLEDGMENTS We would like to extend our sincere thanks to Hemant Purohit from George Mason University and Kiran Zahra from University of Zurich for helping us with the data collection task. ...
arXiv:1805.05144v2
fatcat:segxwd2s7nhbnnlsuwpxtzgld4
Earthquake Damage Assessment in Three Spatial Scale Using Naive Bayes, SVM, and Deep Learning Algorithms
2021
Applied Sciences
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Multi-class classification was used to categorize messages to increase post-crisis situational awareness. ...
Based on the results of the temporal analysis, most of the damage-related messages were reported on the day of the earthquake and decreased in the following days. ...
They used the topic model to examine what type of information was distributed during the incident on social networks. ...
doi:10.3390/app11209737
fatcat:4iw5loq2gve6tp6dgu4pb3kxjy
Information Abstraction from Crises Related Tweets Using Recurrent Neural Network
[chapter]
2016
IFIP Advances in Information and Communication Technology
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The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. ...
The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text. ...
Another application using unsupervised learning identifies events related to public and safety using a spatio-temporal clustering approach [18] . ...
doi:10.1007/978-3-319-44944-9_38
fatcat:v26q7uv4ufbd5pd7imwa33ubzm
Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks
[article]
2016
arXiv
pre-print
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However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. ...
The Scarcity of labeled data, particularly in the early hours of a crisis, delays the machine learning process. ...
Crisis Embedding: Since we work on disaster related tweets, which are quite different from news, we have also trained domain-specific embeddings (vocabulary size 20 million) using the Skip-gram model of ...
arXiv:1608.03902v1
fatcat:632knvdypnf5za6xi26kiavfnm
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages
[article]
2016
arXiv
pre-print
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To demonstrate the utility of the annotations, we train machine learning classifiers. Moreover, we publish first largest word2vec word embeddings trained on 52 million crisis-related tweets. ...
During the sudden onset of a crisis situation, affected people post useful information on Twitter that can be used for situational awareness and other humanitarian disaster response efforts, if processed ...
To the best of our knowledge this is the first largest word embeddings that are trained on crisis-related tweets. ...
arXiv:1605.05894v2
fatcat:7fbntmastvebzil23ozkrev4iq
I-AID: Identifying Actionable Information from Disaster-related Tweets
[article]
2021
arXiv
pre-print
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However, identifying useful information from massive amounts of social media posts during a crisis is a challenging task. ...
between tweets' words/entities and the corresponding information types, and iii) a Relation Network as a learnable distance metric to compute the similarity between tweets and their corresponding information ...
We formulate the problem of identifying crisis information from tweets as a multi-label classification task, where a tweet t can be assigned one or more labels from Λ simultaneously. ...
arXiv:2008.13544v2
fatcat:6yjmtfq4tfec5pf6uly2cpgzoe
Unsupervised Detection of Sub-events in Large Scale Disasters
[article]
2019
arXiv
pre-print
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.), as people "on the ground" post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. ...
In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. ...
The authors extract noun-verb (NV) pairs using dependency parsing from crisis-related tweets. ...
arXiv:1912.13332v1
fatcat:6qn523flrjfubkffgwcwrfyzd4
Unsupervised Detection of Sub-Events in Large Scale Disasters
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
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.), as people "on the ground" post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. ...
In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. ...
The authors extract noun-verb (NV) pairs using dependency parsing from crisis-related tweets. ...
doi:10.1609/aaai.v34i01.5370
fatcat:lp7j3ci5mbhffatpvnfhgslgdq
Impromptu Crisis Mapping to Prioritize Emergency Response
2016
Computer
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To visualize post-emergency damage, a crisis-mapping system uses readily available semantic annotators, a machinelearning classifi er to analyze relevant tweets, and interactive maps to rank extracted ...
situational information. ...
Using the combination of damage and location information, it then creates a crisis map, coloring areas with the most damage. › damage- the Italian language that uses a multilayer perceptron as the learning ...
doi:10.1109/mc.2016.134
fatcat:nt6nwlmnqfbjneohom25r2cw6u
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