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Robust End-User-Driven Social Media Monitoring for Law Enforcement and Emergency Monitoring [chapter]

Birgit Kirsch, Sven Giesselbach, David Knodt, Stefan Rüping
2018 Community-Oriented Policing and Technological Innovations  
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]

Dat Tien Nguyen, Shafiq Joty, Muhammad Imran, Hassan Sajjad, Prasenjit Mitra
2016 arXiv   pre-print
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]

Grégoire Burel, Hassan Saif, Harith Alani
2017 Lecture Notes in Computer Science  
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

Joel Brynielsson, Fredrik Johansson, Carl Jonsson, Anders Westling
2014 Security Informatics  
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]

Reem ALRashdi, Simon O'Keefe
2019 arXiv   pre-print
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]

Sarthak Khanal, Maria Traskowsky, Doina Caragea
2021 arXiv   pre-print
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]

Firoj Alam, Ferda Ofli, Muhammad Imran, Michael Aupetit
2018 arXiv   pre-print
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

Sajjad Ahadzadeh, Mohammad Reza Malek
2021 Applied Sciences  
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]

Mehdi Ben Lazreg, Morten Goodwin, Ole-Christoffer Granmo
2016 IFIP Advances in Information and Communication Technology  
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]

Dat Tien Nguyen, Kamela Ali Al Mannai, Shafiq Joty, Hassan Sajjad, Muhammad Imran, Prasenjit Mitra
2016 arXiv   pre-print
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]

Muhammad Imran, Prasenjit Mitra, Carlos Castillo
2016 arXiv   pre-print
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]

Hamada M. Zahera, Rricha Jalota, Mohamed A. Sherif, Axel N. Ngomo
2021 arXiv   pre-print
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]

Chidubem Arachie, Manas Gaur, Sam Anzaroot, William Groves, Ke Zhang, Alejandro Jaimes
2019 arXiv   pre-print
.), 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

Chidubem Arachie, Manas Gaur, Sam Anzaroot, William Groves, Ke Zhang, Alejandro Jaimes
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
.), 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

Marco Avvenuti, Stefano Cresci, Fabio Del Vigna, Maurizio Tesconi
2016 Computer  
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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