Using AI and Social Media Multimodal Content for Disaster Response and Management: Opportunities, Challenges, and Future Directions

Imran, Muhammad; Ofli, Ferda; Caragea, Doina; Torralba, Antonio

doi:10.1016/j.ipm.2020.102261

Cited by 118 publications

(84 citation statements)

References 44 publications

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“…During a crisis, whether natural or man-made, people tend to spend relatively more time on social media than the normal. As crisis unfolds, social media platforms such as Facebook and Twitter become an active source of information [20] because these platforms break the news faster than official news channels and emergency response agencies [23]. During such events, people usually make informal conversations by sharing their safety status, querying about their loved ones' safety status, and reporting ground level scenarios of the event [11,20].…”

Section: Social Media and Crisis Eventsmentioning

confidence: 99%

Design and analysis of a large-scale COVID-19 tweets dataset

Lamsal

2020

Appl Intell

152

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As of July 17, 2020, more than thirteen million people have been diagnosed with the Novel Coronavirus (COVID-19), and half a million people have already lost their lives due to this infectious disease. The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. Since then, social media platforms have experienced an exponential rise in the content related to the pandemic. In the past, Twitter data have been observed to be indispensable in the extraction of situational awareness information relating to any crisis. This paper presents COV19Tweets Dataset (Lamsal 2020a), a large-scale Twitter dataset with more than 310 million COVID-19 specific English language tweets and their sentiment scores. The dataset's geo version, the GeoCOV19Tweets Dataset (Lamsal 2020b), is also presented. The paper discusses the datasets' design in detail, and the tweets in both the datasets are analyzed. The datasets are released publicly, anticipating that they would contribute to a better understanding of spatial and temporal dimensions of the public discourse related to the ongoing pandemic. As per the stats, the datasets (Lamsal 2020a, 2020b) have been accessed over 74.5k times, collectively.

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Section: Social Media and Crisis Eventsmentioning

confidence: 99%

Design and analysis of a large-scale COVID-19 tweets dataset

Lamsal

2020

Appl Intell

152

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“…has been widely studied [8]. Recent approaches propose to retrieve visual evidence on the events by combining both textual data mining and automated image classification, in order to reduce the information overload needed to inspect images manually [9], [10].…”

Section: A Social Sensing Versus Traditional Surveysmentioning

confidence: 99%

Image-Based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter

Negri

Scuratti

Agresti

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS)

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Social Media provides a trove of information that, if aggregated and analysed appropriately can provide important statistical indicators to policy makers. In some situations these indicators are not available through other mechanisms. For example, given the ongoing COVID-19 outbreak, it is essential for governments to have access to reliable data on policy-adherence with regards to mask wearing, social distancing, and other hardto-measure quantities. In this paper we investigate whether it is possible to obtain such data by aggregating information from images posted to social media. The paper presents VisualCit, a pipeline for image-based social sensing combining recent advances in image recognition technology with geocoding and crowdsourcing techniques. Our aim is to discover in which countries, and to what extent, people are following COVID-19 related policy directives. We compared the results with the indicators produced within the CovidDataHub behavior tracker initiative. Preliminary results shows that social media images can produce reliable indicators for policy makers.

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“…While some of the disaster datasets like Crisislex and CrisisNLP, CrisisMMD [30,113,109,115] contain datasets from various languages, majority of the current research works focus upon utilizing English language datasets while developing machine learning models. However, one of the recent works on the Covid-19 dataset (GeoCov19) focus upon datasets from diverse languages [112] published by Umair Qazi et al The authors published a large dataset of 524 million multi-lingual tweets from various countries pertaining to a COVID-19 disaster event.…”

Section: Challengesmentioning

confidence: 99%

Automated Machine Learning Approaches for Emergency Response and Coordination via Social Media in the Aftermath of a Disaster: A Review

et al. 2021

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Social media communication serves as an integral part of the crisis response following a mass emergency (disaster) event. Regardless of the kind of disaster event, whether it is a hurricane, a flood, an earthquake or a man-made disaster event like a riot or a terrorist attack, social media platforms like Facebook, Twitter etc. have proven to be a powerful facilitator of communication and coordination between disaster victims and other communities. Consequently, several research articles have been published on social media utilization for disaster response. Many of those recent research articles discuss automated machine learning approaches to extract disaster, indicating posts useful for coordination from various social media posts. Despite this, there is a scarcity of comprehensive review of all the major research works pertaining to the utilization of machine learning approaches for disaster response using social media posts. Thus, this study reviews academic research articles in the domain and classifies them across three disaster phase dimensionsearly warning and event detection, post-disaster coordination and response, damage assessment. This review would help researchers in choosing further research topics pertaining to automated approaches for actionable information classification and disaster coordination and would help the emergency teams to make well-informed decisions in disaster situations.

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Using AI and Social Media Multimodal Content for Disaster Response and Management: Opportunities, Challenges, and Future Directions

Cited by 118 publications

References 44 publications

Design and analysis of a large-scale COVID-19 tweets dataset

Design and analysis of a large-scale COVID-19 tweets dataset

Image-Based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter

Automated Machine Learning Approaches for Emergency Response and Coordination via Social Media in the Aftermath of a Disaster: A Review

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