ISCRAM Digital Library -- Query Results

	ISCRAM Digital Library Home \| Show All \| Simple Search \| Advanced Search	Login Quick Search: Field: contains: ...
	201–225 of 260 records found matching your query (RSS):

Search & Display Options

Select All Deselect All

<< 1 2 3 4 5 6 7 8 9 10 >> [11–11]

List View

Citations

Details

	Records
	Author	Jens Kersten; Jan Bongard; Friederike Klan
	Title	Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	744-754
	Keywords	Information Overload Reduction, Semantic Clustering, Crisis Informatics, Twitter Stream
	Abstract	Twitter is an immediate and almost ubiquitous platform and therefore can be a valuable source of information during disasters. Current methods for identifying and classifying crisis-related content are often based on single tweets, i.e., already known information from the past is neglected. In this paper, the combination of tweet-wise pre-trained neural networks and unsupervised semantic clustering is proposed and investigated. The intention is to (1) enhance the generalization capability of pre-trained models, (2) to be able to handle massive amounts of stream data, (3) to reduce information overload by identifying potentially crisis-related content, and (4) to obtain a semantically aggregated data representation that allows for further automated, manual and visual analyses. Latent representations of each tweet based on pre-trained sentence embedding models are used for both, clustering and tweet classification. For a fast, robust and time-continuous processing, subsequent time periods are clustered individually according to a Chinese restaurant process. Clusters without any tweet classified as crisis-related are pruned. Data aggregation over time is ensured by merging semantically similar clusters. A comparison of our hybrid method to a similar clustering approach, as well as first quantitative and qualitative results from experiments with two different labeled data sets demonstrate the great potential for crisis-related Twitter stream analyses.
	Address	German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Departmen
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	jens.kersten@dlr.de			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2369
Share this record to Facebook



	Author	Yan Wang; Qi Wang; John Taylor
	Title	Loss of Resilience in Human Mobility across Severe Tropical Cyclones of Different Magnitudes			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	755-765
	Keywords	Disaster Resilience, Geo-social networking, Human mobility, Tropical Cyclones
	Abstract	Severe tropical cyclones impose threats on highly populated coastal urban areas, thereby, understanding and predicting human movements plays a critical role in evaluating disaster resilience of human society. However, limited research has focused on tropical cyclones and their influence on human mobility resilience. This preliminary study examined the strength and duration of human mobility perturbation across five significant tropical storms and their affected eight urban areas using Twitter data. The results suggest that tropical cyclones can significantly perturb human movements by changing travel frequencies and displacement probability distributions. While the power-law still best described the pattern of human movements, the changes in the radii of gyration were significant and resulted in perturbation and loss of resilience in human mobility. The findings deepen the understanding about human-environment interactions under extreme events, improve our ability to predict human movements using social media data, and help policymakers improve disaster evacuation and response.
	Address	University of Florida; Northeastern University; Georgia Institute of Technology
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	yanw@ufl.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2370
Share this record to Facebook



	Author	Valentin Barriere; Guillaume Jacquet
	Title	How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	766-771
	Keywords	Transformers, Contextual keywords, Humanitarian Computing, Tweets analysis
	Abstract	In a classification task, dealing with text snippets and metadata usually requires to deal with multimodal approaches. When those metadata are textual, it is tempting to use them intrinsically with a pre-trained transformer, in order to leverage the semantic information encoded inside the model. This paper describes how to improve a humanitarian classification task by adding the crisis event type to each tweet to be classified. Based on additional experiments of the model weights and behavior, it identifies how the proposed neural network approach is partially over-fitting the particularities of the Crisis Benchmark, to better highlight how the model is still undoubtedly learning to use and take advantage of the metadata's textual semantics.
	Address	European Commission's Joint Research Center; European Commission's Joint Research Center
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	valbarrierepro@gmail.com			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2371
Share this record to Facebook



	Author	Valerio Lorini; Carlos Castillo; Steve Peterson; Paola Rufolo; Hemant Purohit; Diego Pajarito; João Porto de Albuquerque; Cody Buntain
	Title	Social Media for Emergency Management: Opportunities and Challenges at the Intersection of Research and Practice			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	772-777
	Keywords	Crisis Informatics, Social Media, Workshop Report, Disaster Management
	Abstract	This paper summarizes key opportunities and challenges identified during the workshop “Social Media for Disaster Risk Management: Researchers Meet Practitioners” which took place online in November 2020. It constitutes a work-in-progress towards identifying new directions for research and development of systems that can better serve the information needs of emergency managers. Practitioners widely recognize the potential of accessing timely information from social media. Nevertheless, the discussion outlined some critical challenges for improving its adoption during crises. In particular, validating such information and integrating it with authoritative information and into more traditional information systems for emergency managers requires further work, and the negative impacts of misinformation and disinformation need to be prevented.
	Address	European Commission, Joint Research Centre (JRC), Ispra, Italy; Universitat Pompeu Fabra, Barcelona, Spain; Community Emergency Response Team, Montgomery County, Maryland, USA; European Commission, Joint Research Centre, Ispra, Italy; George Mason Univers
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	valerio.lorini@ec.europa.eu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2372
Share this record to Facebook



	Author	Therese Habig; Richard Lüke; Simon Gehlhar; Torben Sauerland; Daniel Tappe
	Title	A Consolidated Understanding of Disaster Community Technologies			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	778-791
	Keywords	Disaster Community Technologies, social media and crowdsourcing, categorization and classification schema, knowledge base
	Abstract	Since the beginning of this millennium, there has been an increasing use of social media and crowdsourcing (SMCS) technologies in disaster situations (Reuter & Kaufhold, 2018). Disaster management organizations and corresponding research are increasingly working on ways of integrating SMCS into the processes of crisis management. In a changing technological landscape to address disasters, and with increasing diversity of stakeholders in disasters, the purpose of this research is to provide an overview of technologies for SMCS within disasters to improve community resilience. The identified and analyzed technologies are summarized under the term “Disaster Community Technologies” (DCT). The paper presents a classification schema (the “DCT-schema”) for those technologies. The goal is to generate an overview of DCT in a rapidly evolving environment and to provide the practical benefit for different stakeholders to identify the right one from the overview.
	Address	safety innovation center; safety innovation center; safety innovation center; safety innovation center; safety innovation center
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	habig@safetyinnovation.center			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2373
Share this record to Facebook



	Author	Antone Evans Jr.; Yingyuan Yang; Sunshin Lee
	Title	Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	792-807
	Keywords	Big Data Analysis, Machine Learning, COVID-19, Twitter
	Abstract	During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases.
	Address	University of Illinois Springfield; University of Illinois Springfield; University of Illinois Springfield
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	aevan7@uis.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2374
Share this record to Facebook



	Author	Shivam Sharma; Cody Buntain
	Title	An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts			Type	Conference Article
	Year	2021	Publication	ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2021
	Volume		Issue		Pages	808-815
	Keywords	covid19, twitter, trecis, cross-validation, machine learning, transfer learning
	Abstract	In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data.
	Address	New Jersey Institute of Technology; New Jersey Institute of Technology
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	cbuntain@njit.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2375
Share this record to Facebook



	Author	Dario Salza; Edoardo Arnaudo; Giacomo Blanco; Claudio Rossi
	Title	A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	570-583
	Keywords	Emergency; Event Detection; Social Media; Twitter; Incremental Clustering
	Abstract	Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a ‘glocal’ approach, i.e., offering a global coverage while detecting events at local (municipality level) scale.
	Address	LINKS Foundation; LINKS Foundation; LINKS Foundation; LINKS Foundation
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2440
Share this record to Facebook



	Author	Cody Buntain; Richard Mccreadie; Ian Soboroff
	Title	Incident Streams 2021 Off the Deep End: Deeper Annotations and Evaluations in Twitter			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	584-604
	Keywords	Emergency Management; Crisis Informatics; Twitter; Categorization; Priorization; Multi-Modal; Public Safety; PSCR; TREC
	Abstract	This paper summarizes the final year of the four-year Text REtrieval Conference Incident Streams track (TREC-IS), which has produced a large dataset comprising 136,263 annotated tweets, spanning 98 crisis events. Goals of this final year were twofold: 1) to add new categories for assessing messages, with a focus on characterizing the audience, author, and images associated with these messages, and 2) to enlarge the TREC-IS dataset with new events, with an emphasis of deeper pools for sampling. Beyond these two goals, TREC-IS has nearly doubled the number of annotated messages per event for the 26 crises introduced in 2021 and has released a new parallel dataset of 312,546 images associated with crisis content – with 7,297 tweets having annotations about their embedded images. Our analyses of this new crisis data yields new insights about the context of a tweet; e.g., messages intended for a local audience and those that contain images of weather forecasts and infographics have higher than average assessments of priority but are relatively rare. Tweets containing images, however, have higher perceived priorities than tweets without images. Moving to deeper pools, while tending to lower classification performance, also does not generally impact performance rankings or alter distributions of information-types. We end this paper with a discussion of these datasets, analyses, their implications, and how they contribute both new data and insights to the broader crisis informatics community.
	Address	University of Maryland, College Park (UMD); University of Glasgow; National Institute of Standards and Technology (NIST)
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2441
Share this record to Facebook



	Author	Pooneh Mousavi; Cody Buntain
	Title	“Please Donate for the Affected”: Supporting Emergency Managers in Finding Volunteers and Donations in Twitter Across Disasters			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	605-622
	Keywords	social media; crisis in formatics; volunteers; donations; emergency support functions
	Abstract	Despite the outpouring of social support posted to social media channels in the aftermath of disaster, finding and managing content that can translate into community relief, donations, volunteering, or other recovery support is difficult due to the lack of sufficient annotated data around volunteerism. This paper outlines three experiments to alleviate these difficulties. First, we estimate to what degree volunteerism content from one crisis is transferable to another by evaluating the consistency of language in volunteer-and donation-related social media content across 78 disasters. Second it introduces methods for providing computational support in this emergency support function and developing semi-automated models for classifying volunteer-and donation-related social media content in new disaster events. Results show volunteer-and donation-related social media content is sufficiently similar across disasters and disaster types to warrant transferring models across disasters, and we evaluate simple resampling techniques for tuning these models. We then introduce and evaluate a weak-supervision approach to integrate domain knowledge from emergency response officers with machine learningmodelstoimproveclassification accuracy andacceleratethisemergencysupportinnewevents. This method helps to overcome the scarcity in data that we observe related to volunteer-and donation-related social media content.
	Address	University of Maryland, College Park; University of Maryland, College Park
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2442
Share this record to Facebook



	Author	Thomas Papadimos; Nick Pantelidis; Stelios Andreadis; Aristeidis Bozas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris
	Title	Real-time Alert Framework for Fire Incidents Using Multimodal Event Detection on Social Media Streams			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	623-635
	Keywords	Alert framework; social media; event detection; kernel density estimation; community detection
	Abstract	The frequency of wildfires is growing day by day due to vastly climate changes. Forest fires can have a severe impact on human lives and the environment, which can be minimised if the population has early and accurate warning mechanisms. To date, social media are able to contribute to early warning with the additional, crowd-sourced information they can provide to the emergency response workers during a crisis event. Nevertheless, the detection of real-world fire incidents using social media data, while filtering out the unavoidable noise, remains a challenging task. In this paper, we present an alert framework for the real-time detection of fire events and we propose a novel multimodal event detection model, which fuses both probabilistic and graph methodologies and is evaluated on the largest fires in Spain during 2019.
	Address	Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologie
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2443
Share this record to Facebook



	Author	Kiran Zahra; Rahul Deb Das; Frank O. Ostermann; Ross S. Purves
	Title	Towards an Automated Information Extraction Model from Twitter Threads during Disasters			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	637-653
	Keywords	Social media threads; Text summarization; Disasters; Lexicons; Information extraction models; Word embeddings
	Abstract	Social media plays a vital role as a communication source during large-scale disasters. The unstructured and informal nature of such short individual posts makes it difficult to extract useful information, often due to a lack of additional context. The potential of social media threads– sequences of posts– has not been explored as a source of adding context and more information to the initiating post. In this research, we explored Twitter threads as an information source and developed an information extraction model capable of extracting relevant information from threads posted during disasters. We used a crowdsourcing platform to determine whether a thread adds more information to the initial tweet and defined disaster-related information present in these threads into six themes– event reporting, location, time, intensity, casualty and damage reports, and help calls. For these themes, we created the respective thematic lexicons from WordNet. Moreover, we developed and compared four information extraction models trained on GloVe, word2vec, bag-of-words, and thematic bag-of-words to extract and summarize the most critical information from the threads. Our results reveal that 70 percent of all threads add information to the initiating post for various disaster-related themes. Furthermore, the thematic bag-of-words information extraction model outperforms the other algorithms and models for preserving the highest number of disaster-related themes.
	Address	University of Zurich; University of Zurich, IBM; University of Twente; University of Zurich
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2444
Share this record to Facebook



	Author	Gaëtan Caillaut; Cécile Gracianne; Nathalie Abadie; Guillaume Touya; Samuel Auclair
	Title	Automated Construction of a French Entity Linking Dataset to Geolocate Social Network Posts in the Context of Natural Disasters			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	654-663
	Keywords	Automated geotagging; French Entity Linking; Wikipedia; Twitter; Crisis Management; Natural Disaster
	Abstract	During natural disasters, automatic information extraction from Twitter posts is a valuable way to get a better overview of the field situation. This information has to be geolocated to support effective actions, but for the vast majority of tweets, spatial information has to be extracted from texts content. Despite the remarkable advances of the Natural Language Processing field, this task is still challenging for current state-of-the-art models because they are not necessarily trained on Twitter data and because high quality annotated data are still lacking for low resources languages. This research in progress address this gap describing an analytic pipeline able to automatically extract geolocatable entities from texts and to annotate them by aligning them with the entities present in Wikipedia/Wikidata resources. We present a new dataset for Entity Linking on French texts as preliminary results, and discuss research perspectives for enhancements over current state-of-the-art modeling for this task.
	Address	BRGM; BRGM; LASTIG, Univ Gustave Eiffel, IGN-ENSG; LASTIG, Univ Gustave Eiffel, IGN-ENSG; BRGM
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2445
Share this record to Facebook



	Author	Jens Kersten; Jan Bongard; Friederike Klan
	Title	Gaussian Processes for One-class and Binary Classification of Crisis-related Tweets			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	664-673
	Keywords	Gaussian Process; One-class Classification; Twitter; Overload Reduction; Crisis Informatics
	Abstract	Overload reduction is essential to exploit Twitter text data for crisis management. Often used pre-trained machine learning models require training data for both, crisis-related and off-topic content. However, this task can also be formulated as a one-class classification problem in which labeled off-topic samples are not required. Gaussian processes (GPs) have great potential in both, binary and one-class settings and are therefore investigated in this work. Deep kernel learning combines the representative power of text embeddings with the Bayesian formalism of GPs. Motivated by this, we investigate the potential of deep kernel models for the task of classifying crisis-related tweet texts with special emphasis on cross-event applications. Compared to standard binary neural networks, first experiments with one-class GP models reveal a great potential for realistic scenarios, offering a fast and flexible approach for interactive model training without requiring off-topic training samples and comprehensive expert knowledge (only two model parameters involved).
	Address	German Aerospace Center– Jena, Germany; German Aerospace Center– Jena, Germany; German Aerospace Center– Jena, Germany
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2446
Share this record to Facebook



	Author	Carlo Alberto Bono; Barbara Pernici; Jose Luis Fernandez-Marquez; Amudha Ravi Shankar; Mehmet Oguz Mülâyim; Edoardo Nemni
	Title	TriggerCit: Early Flood Alerting using Twitter and Geolocation – A Comparison with Alternative Sources			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	674-686
	Keywords	Social Media; Disaster management; Early Alerting
	Abstract	Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. The paper focuses on assessing the reliability of the approach as a triggering system, comparing it with alternative sources for alerts, and evaluating the quality and amount of complementary information gathered. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021. The system respectively returned a large scale and a local scale alert, both in a timely manner and accompanied by a valid geographical description, while providing information complementary to existing disaster alert mechanisms.
	Address	Politecnico di Milano- DEIB;Politecnico di Milano- DEIB;University of Geneva;University of Geneva;Artificial Intelligence Research Institute (IIIA-CSIC); United Nations Satellite Centre (UNOSAT), United Nations Institute for Training and Research (UNITAR)
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2447
Share this record to Facebook



	Author	Ahmed Alnuhayt; Suvodeep Mazumdar; Vitaveska Lanfranchi; Frank Hopfgartner
	Title	Understanding Reactions to Misinformation – A Covid-19 Perspective			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	687-700
	Keywords	Misinformation; social reactions; twitter; people; COVID-19
	Abstract	The increasing use of social media as an information source brings further challenges – social media platforms can be an excellent medium for disseminating public awareness and critical information, that can be shared across large populations. However, misinformation in social media can have immense implications on public health, risking the effectiveness of health interventions as well as lives. This has been particularly true in the case of COVID-19 pandemic, with a range of misinformation, conspiracy theories and propaganda being spread across social channels. In our study, through a questionnaire survey, we set out to understand how members of the public interact with different sources when looking for information on COVID-19. We explored how participants react when they encounter information they believe to be misinformation. Through a set of three behaviour tasks, synthetic misinformation posts were provided to the participants who chose how they would react to them. In this work in progress study, we present initial findings and insights into our analysis of the data collected. We highlight what are the most common reactions to misinformation and also how these reactions are different based on the type of misinformation.
	Address	Information School University of Sheffield; Information School University of Sheffield; Computer Science University of Sheffield; Information School University of Sheffield
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2448
Share this record to Facebook



	Author	Nils Bourgon; Benamara Farah; Alda Mari; Véronique Moriceau; Gaetan Chevalier; Laurent Leygue; Yasmine Djadda
	Title	Are Sudden Crises Making me Collapse? Measuring Transfer Learning Performances on Urgency Detection			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	701-709
	Keywords	Sudden crises; Transfer learning; Few-shot learning; Zero-shot learning; Social media content
	Abstract	This paper aims at measuring transfer learning performances across different types of crises related to sudden or unexpected events (like earthquakes, terror attacks, explosions, technological incidents) that cannot be foreseen by emergency services and on the occurrence of which they have virtually no control. Although sudden crises are present in most existing crisis datasets, as far as we are aware, no one studied their impact on classifiers performances when evaluated in an out-of-type scenario in which models are tested on a particular type of crisis unseen during training. Our contribution is threefold: (1) A new dataset of about 3,800 French tweets related to four sudden events that occurred in France annotated for both relatedness (i.e., useful vs. not useful for emergency responders) and urgency (i.e., not useful vs. urgent vs. not urgent), (2) A set of monotask and multitask zero-shot learning experiments to transfer knowledge across events and types, and finally, (3) Experiments involving few-shot learning to measure the amount of sudden events instances needed during training to guarantee good performances. When compared to a cross-event setting, our preliminary results are encouraging and show that transfer from predictable ecological crisis to sudden events is feasible and constitutes a first step towards real-time crisis management systems from social media content.
	Address	IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3; IJN, CNRS/ENS/EHESS PSL University; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3; DGSCGC SDAIRS; DGSCGC SDAIRS
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2449
Share this record to Facebook



	Author	Hafiz Budi Firmansyah; Jesus Cerquides; Jose Luis Fernandez-Marquez
	Title	Ensemble Learning for the Classification of Social Media Data in Disaster Response			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	710-718
	Keywords	Ensemble learning; image classification; social media; disaster response
	Abstract	Social media generates large amounts of almost real-time data which has proven valuable in disaster response. Specially for providing information within the first 48 hours after a disaster occurs. However, this potential is poorly exploited in operational environments due to the challenges of curating social media data. This work builds on top of the latest research on automatic classification of social media content, proposing the use of ensemble learning to help in the classification of social media images for disaster response. Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Experimental results show that ensemble learning is a valuable technology for the analysis of social media images for disaster response,and could potentially ease the integration of social media data within an operational environment.
	Address	Citizen Cyberlab, CUI, University of Geneva, Switzerland; Citizen Cyberlab, CUI, University of Geneva, Switzerland; IIIA-CSIC, Barcelona, Spain
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2450
Share this record to Facebook



	Author	Shivam Sharma; Cody Buntain
	Title	Bang for your Buck: Performance Impact Across Choices in Learning Architectures for Crisis Informatics			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	719-736
	Keywords	Incident Streams; TREC; TRECIS; crisis informatics
	Abstract	Over the years, with the increase in social media engagement, there has been an in increase in various pipelines to analyze, classify and prioritize crisis-related data on various social media platforms. These pipelines utilize various data augmentation methods to counter imbalanced crisis data, sophisticated and off-the-shelf models for training. However, there is a lack of comprehensive study which compares these methods for the various sections of a pipeline. In this study, we split a general crisis-related pipeline into 3 major sections, namely, data augmentation, model selection, and training methodology. We compare various methods for each of these sections and then present a comprehensive evaluation of which section to prioritize based on the results from various pipelines. We compare our results against two separate tasks, information classification and priority scoring for crisis-related tweets. Our results suggest that data augmentation, in general,improves the performance. However, sophisticated, state-of-the-art language models like DeBERTa only show performance gain in information classification tasks, and models like RoBERTa tend to show a consistent performance increase over our presented baseline consisting of BERT. We also show that, though training two separate task-specific BERT models does show better performance than one BERT model with multi-task learning methodology over an imbalanced dataset, multi-task learning does improve performance for more sophisticated model like DeBERTa with a much more balanced dataset after augmentation.
	Address	New Jersey Institute of Technology; New Jersey Institute of Technology
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2451
Share this record to Facebook



	Author	Zijun Long; Richard McCreadie
	Title	Is Multi-Modal Data Key for Crisis Content Categorization on Social Media?			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	1068-1080
	Keywords	Social Media Classification; Multi-modal Learning; Crisis Management; Deep Learning, BERT; Supervised Learning
	Abstract	The user-base of social media platforms, like Twitter, has grown dramatically around the world over the last decade. As people post everything they experience on social media, large volumes of valuable multimedia content are being recorded online, which can be analysed to help for a range of tasks. Here we specifically focus on crisis response. The majority of prior works in this space focus on using machine learning to categorize single-modality content (e.g. text of the posts, or images shared), with few works jointly utilizing multiple modalities. Hence, in this paper, we examine to what extent integrating multiple modalities is important for crisis content categorization. In particular, we design a pipeline for multi-modal learning that fuses textual and visual inputs, leverages both, and then classifies that content based on the specified task. Through evaluation using the CrisisMMD dataset, we demonstrate that effective automatic labelling for this task is possible, with an average of 88.31% F1 performance across two significant tasks (relevance and humanitarian category classification). while also analysing cases that unimodal models and multi-modal models success and fail.
	Address	University of Glasgow; University of Glasgow
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2472
Share this record to Facebook



	Author	Ly Dinh; Sumeet Kulkarni; Pingjing Yang; Jana Diesner
	Title	Reliability of Methods for Extracting Collaboration Networks from Crisis-related Situational Reports and Tweets			Type	Conference Article
	Year	2023	Publication	Proceedings of the ISCRAM Asia Pacific Conference 2022	Abbreviated Journal	Proc. ISCRAM AP 2022
	Volume		Issue		Pages	181-195
	Keywords	Collaboration Networks; Natural Language Processing; Interorganizational Collaboration; Situational Awareness
	Abstract	Assessing the effectiveness of crisis response is key to improving preparedness and adapting policies. One method for response evaluation is reviewing actual response activities and interactions. Response reports are often available in the form of natural language text data. Analyzing a large number of such reports requires automated or semi automated solutions. To improve the trustworthiness of methods for this purpose, we empirically validate the reliability of three relation extraction methods that we used to construct interorganizational collaboration networks by comparing them against human-annotated ground truth (crisis-specific situational reports and tweets). For entity extraction, we find that using a combination of two off-the-shelf methods (FlairNLP and SpaCy) is optimal for situational reports data and one method (SpaCy) for tweets data. For relation extraction, we find that a heuristics-based model that we built by leveraging word co-occurrence and deep and shallow syntax as features and training it on domain-specific text data outperforms two state-of-the-art relation extraction models (Stanford OpenIE and OneIE) that were pre-trained on general domain data. We also find that situational reports, on average, contain less entities and relations than tweets, but the extracted networks are more closely related to collaboration activities mentioned in the ground truth. As it is widely known that general domain tools might need adjustment to perform accurately in specific domains, we did not expect the tested off-the-shelf tools to perform highly accurately. Our point is to rather identify what accuracy one could reasonably expect when leveraging available resources as-is for domain specific work (in this case, crisis informatics), what errors (in terms of false positives and false negatives) to expect, and how to account for that.
	Address	University of South Florida; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign
	Corporate Author				Thesis
	Publisher	Massey Unversity	Place of Publication	Palmerston North, New Zealand	Editor	Thomas J. Huggins, V.L.
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-0-473-66845-7	Medium
	Track	Social Media for Disaster Response	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2492
Share this record to Facebook



	Author	Xiao Li; Julia Kotlarsky; Michael D. Myers
	Title	Crowdsourcing and the COVID-19 Response in China: An Actor-Network Perspective			Type	Conference Article
	Year	2023	Publication	Proceedings of the ISCRAM Asia Pacific Conference 2022	Abbreviated Journal	Proc. ISCRAM AP 2022
	Volume		Issue		Pages	240-246
	Keywords	Disaster; Crowdsourcing; Actor-Network; Social Media
	Abstract	Crowdsourcing, serving as a distributed problem-solving and production model, can help in the response to a disaster. The current literature focuses on the flow of crowdsourced information, but the question of how crowdsourcing contributes to physical disaster workflows remains to be addressed. Based on a case study of China’s response to COVID-19, this research aims to explore the role of crowdsourcing stakeholders and how they acted to respond to the outbreak. Actor network theory is applied as the lens to elucidate the roles of different heterogeneous actors. The preliminary results indicate that socio-technical actors activated, absorbed, associated, and aligned with each other to combat the pandemic. We suggest ways to augment the actor network to address potential future outbreaks.
	Address	University of Auckland; University of Auckland; University of Auckland
	Corporate Author				Thesis
	Publisher	Massey Unversity	Place of Publication	Palmerston North, New Zealand	Editor	Thomas J. Huggins, V.L.
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-0-473-66845-7	Medium
	Track	Social Media for Disaster Response	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2497
Share this record to Facebook



	Author	Robert Power; Bella Robinson; Mark Cameron
	Title	Insights from a Decade of Twitter Monitoring for Emergency Management			Type	Conference Article
	Year	2023	Publication	Proceedings of the ISCRAM Asia Pacific Conference 2022	Abbreviated Journal	Proc. ISCRAM AP 2022
	Volume		Issue		Pages	247-257
	Keywords	Crisis Coordination; Disaster Management; Situation Awareness; Social Media; System Architecture; Twitter
	Abstract	The Emergency Situation Awareness (ESA) tool began as a research study into automated web text mining to support emergency management use cases. It started in late 2009 by investigating how people respond on Twitter to specific emergency events and we quickly realized that every emergency situation is different and preemptively defining keywords to search for content on Twitter beforehand would likely miss important information. So, in late September 2011 we established location-based searches with the aim of collecting all the tweets published in Australia and New Zealand. This was the beginning of over a decade of collecting and processing tweets to help emergency response agencies and crisis coordination centres use social media content as a new channel of information to support their work practices and to engage with the community impacted by emergency events. This journey has seen numerous challenges overcome to continuously maintain a tweet stream for an operational system. This experience allows us to derive insights into the changing use of Twitter over this time. In this paper we present some of the lessons we’ve learned from maintaining a Twitter monitoring system for emergency management use cases and we provide some insights into the changing nature of Twitter usage by users over this period.
	Address	CSIRO Data61; CSIRO Data61; CSIRO Data61
	Corporate Author				Thesis
	Publisher	Massey Unversity	Place of Publication	Palmerston North, New Zealand	Editor	Thomas J. Huggins, V.L.
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-0-473-66845-7	Medium
	Track	Social Media for Disaster Response	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2498
Share this record to Facebook



	Author	Dilini Rajapaksha; Kacper Sokol; Jeffrey Chan; Flora Salim; Mukesh Prasad; Mahendra Samarawickrama
	Title	Analysing Donors’ Behaviour in Non-profit Organisations for Disaster Resilience			Type	Conference Article
	Year	2023	Publication	Proceedings of the ISCRAM Asia Pacific Conference 2022	Abbreviated Journal	Proc. ISCRAM AP 2022
	Volume		Issue		Pages	258-267
	Keywords	Disaster Response; Social Media; Donors’ Behaviour; Australian Bushfires
	Abstract	With the advancement and proliferation of technology, non-profit organisations have embraced social media platforms to improve their operational capabilities through brand advocacy, among many other strategies. The effect of such social media campaigns on these institutions, however, remains largely underexplored, especially during disaster periods. This work introduces and applies a quantitative investigative framework to understand how social media influence the behaviour of donors and their usage of these platforms throughout (natural) disasters. More specifically, we explore how on-line engagement – as captured by Facebook interactions and Google search trends – corresponds to the donors’ behaviour during the catastrophic 2019–2020 Australian bushfire season. To discover this relationship, we analyse the record of donations made to the Australian Red Cross throughout this period. Our exploratory study reveals that social media campaigns are effective in encouraging on-line donations made via a dedicated website. We also compare this mode of giving to more regular, direct deposit gifting.
	Address	RMIT University; RMIT University; RMIT University; UNSW Sydney; University of Technology Sydney; Australian Red Cross
	Corporate Author				Thesis
	Publisher	Massey Unversity	Place of Publication	Palmerston North, New Zealand	Editor	Thomas J. Huggins, V.L.
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-0-473-66845-7	Medium
	Track	Social Media for Disaster Response	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2499
Share this record to Facebook



	Author	Guillermo Romera Rodriguez
	Title	Parler, Capitol Riots, Alt-Right and Radicalization in Social Media			Type	Conference Article
	Year	2023	Publication	Proceedings of the ISCRAM Asia Pacific Conference 2022	Abbreviated Journal	Proc. ISCRAM AP 2022
	Volume		Issue		Pages	268-277
	Keywords	Social Media; Parler; Sentiment Analysis; Alt-Right
	Abstract	Social media platforms have risen in popularity since their inception. These platforms have since then come to be at the forefront of controversies, from being accused of election interference to, more recently, disseminating fake news and campaigns to sway political behavior. One such episode took place on January 6 when a group of individuals stormed the United States Capitol, and the social media platform Parler came under scrutiny. The platform was accused of being a place for right-wing extremists and Trump supporters who claimed the 2020 election was fraudulent. Initial reports suggested these individuals used Parler to organize and call others to action. This paper explores the feasibility of using social media to detect alt-right radicalization and examines its possible relation to the Capitol Insurrection and Parler. Moreover, we examine if those events could have been detected and averted through the investigation of the platform.
	Address	Pennsylvania State University; Pennsylvania State University; Pennsylvania State University
	Corporate Author				Thesis
	Publisher	Massey Unversity	Place of Publication	Palmerston North, New Zealand	Editor	Thomas J. Huggins, V.L.
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-0-473-66845-7	Medium
	Track	Social Media for Disaster Response	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2500
Share this record to Facebook