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Author (up) Cody Buntain; Richard Mccreadie; Ian Soboroff pdf  isbn
openurl 
  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  
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Author (up) Cody Buntain; Richard Mccreadie; Ian Soboroff pdf  openurl
  Title Incident Streams 2020: TRECIS in the Time of COVID-19 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 621-639  
  Keywords Emergency Management, Crisis Informatics, Twitter, Categorization, Prioritization, COVID-19  
  Abstract Between 2018 and 2019, the Incident Streams track (TREC-IS) has developed standard approaches for classifying the types and criticality of information shared in online social spaces during crises, but the introduction of SARS-CoV-2 has shifted the landscape of online crises substantially. While prior editions of TREC-IS have lacked data on large-scale public-health emergencies as these events are exceedingly rare, COVID-19 has introduced an over-abundance of potential data, and significant open questions remain about how existing approaches to crisis informatics and datasets built on other emergencies adapt to this new context. This paper describes how the 2020 edition of TREC-IS has addressed these dual issues by introducing a new COVID-19-specific task for evaluating generalization of existing COVID-19 annotation and system performance to this new context, applied to 11 regions across the globe. TREC-IS has also continued expanding its set of target crises, adding 29 new events and expanding the collection of event types to include explosions, fires, and general storms, making for a total of 9 event types in addition to the new COVID-19 events. Across these events, TREC-IS has made available 478,110 COVID-related messages and 282,444 crisis-related messages for participant systems to analyze, of which 14,835 COVID-related and 19,784 crisis-related messages have been manually annotated. Analyses of these new datasets and participant systems demonstrate first that both the distributions of information type and priority of information vary between general crises and COVID-19-related discussion. Secondly, despite these differences, results suggest leveraging general crisis data in the COVID-19 context improves performance over baselines. Using these results, we provide guidance on which information types appear most consistent between general crises and COVID-19.  
  Address New Jersey Institute of Technology; University of Glasgow; National Institute of Standards and 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 2360  
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Author (up) Richard McCreadie; Cody Buntain; Ian Soboroff pdf  isbn
openurl 
  Title Incident Streams 2019: Actionable Insights and How to Find Them Type Conference Article
  Year 2020 Publication ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2020  
  Volume Issue Pages 744-760  
  Keywords Emergency Management, Crisis Informatics, Real-time, Twitter, Categorization.  
  Abstract The ubiquity of mobile internet-enabled devices combined with wide-spread social media use during emergencies is posing new challenges for response personnel. In particular, service operators are now expected to monitor these online channels to extract actionable insights and answer questions from the public. A lack of adequate tools makes this monitoring impractical at the scale of many emergencies. The TREC Incident Streams (TREC-IS) track drives research into solving this technology gap by bringing together academia and industry to develop techniques for extracting actionable insights from social media streams during emergencies. This paper covers the second year of TREC-IS, hosted in 2019 with two editions, 2019-A and 2019-B, contributing 12 new events and approximately 20,000 new tweets across 25 information categories, with 15 research groups participating across the world. This paper provides an overview of these new editions, actionable insights from data labelling, and the automated techniques employed by participant systems that appear most effective.  
  Address University of Glasgow; InfEco Lab, New Jersey Institute of Technology (NJIT); National Institute of Standards and Technology (NIST)  
  Corporate Author Thesis  
  Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Amanda Hughes; Fiona McNeill; Christopher W. Zobel  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 978-1-949373-27-67 ISBN 2411-3453 Medium  
  Track Social Media for Disaster Response and Resilie Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management  
  Notes richard.mccreadie@glasgow.ac.uk Approved no  
  Call Number Serial 2268  
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Author (up) Richard McCreadie; Cody Buntain; Ian Soboroff pdf  isbn
openurl 
  Title TREC Incident Streams: Finding Actionable Information on Social Media Type Conference Article
  Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019  
  Volume Issue Pages  
  Keywords Emergency Management, Crisis Informatics, Real-time, Twitter, Categorization  
  Abstract The Text Retrieval Conference (TREC) Incident Streams track is a new initiative that aims to mature social

media-based emergency response technology. This initiative advances the state of the art in this area through an

evaluation challenge, which attracts researchers and developers from across the globe. The 2018 edition of the track

provides a standardized evaluation methodology, an ontology of emergency-relevant social media information types,

proposes a scale for information criticality, and releases a dataset containing fifteen test events and approximately

20,000 labeled tweets. Analysis of this dataset reveals a significant amount of actionable information on social

media during emergencies (> 10%). While this data is valuable for emergency response efforts, analysis of the

39 state-of-the-art systems demonstrate a performance gap in identifying this data. We therefore find the current

state-of-the-art is insufficient for emergency responders? requirements, particularly for rare actionable information

for which there is little prior training data available.
 
  Address University of Glasgow, United Kingdom;New York University, USA;National Institute of Standards and Technology, USA  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium  
  Track T8- Social Media in Crises and Conflicts Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)  
  Notes Approved no  
  Call Number Serial 1867  
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Author (up) Zijun Long; Richard McCreadie pdf  isbn
openurl 
  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  
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Author (up) Zijun Long; Richard Mccreadie pdf  openurl
  Title Automated Crisis Content Categorization for COVID-19 Tweet Streams 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 667-678  
  Keywords COVID-19, Tweet Classification, Crisis Management, Deep Learning  
  Abstract Social media platforms, like Twitter, are increasingly used by billions of people internationally to share information. As such, these platforms contain vast volumes of real-time multimedia content about the world, which could be invaluable for a range of tasks such as incident tracking, damage estimation during disasters, insurance risk estimation, and more. By mining this real-time data, there are substantial economic benefits, as well as opportunities to save lives. Currently, the COVID-19 pandemic is attacking societies at an unprecedented speed and scale, forming an important use-case for social media analysis. However, the amount of information during such crisis events is vast and information normally exists in unstructured and multiple formats, making manual analysis very time consuming. Hence, in this paper, we examine how to extract valuable information from tweets related to COVID-19 automatically. For 12 geographical locations, we experiment with supervised approaches for labelling tweets into 7 crisis categories, as well as investigated automatic priority estimation, using both classical and deep learned approaches. Through evaluation using the TREC-IS 2020 COVID-19 datasets, we demonstrated that effective automatic labelling for this task is possible with an average of 61% F1 performance across crisis categories, while also analysing key factors that affect model performance and model generalizability across locations.  
  Address University of Glasgow; University of Glasgow  
  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 2452593L@student.gla.ac.uk Approved no  
  Call Number ISCRAM @ idladmin @ Serial 2363  
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