Cross-domain informativeness classification for disaster situations

Graf, David; Retschitzegger, Werner; Schwinger, Wieland; Pröll, Birgit; Kapsammer, Elisabeth

doi:10.1145/3281375.3281385

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…In contrast to CNN-based approaches we consider BiLSTMs with attention mechanisms with an aim to better captures dependencies between word tokens. Some researchers have focused on domain adaption and cross-domain classification [24], [25]. Li et al [24] studied the feasibility of domain adaption for analyzing the disaster tweets by applying the naive Bayes classifier on the Boston Marathon bombing and Hurricane Sandy dataset.…”

Section: A Unimodal Approaches 1) Text-based Disaster Identificationmentioning

confidence: 99%

“…Li et al [24] studied the feasibility of domain adaption for analyzing the disaster tweets by applying the naive Bayes classifier on the Boston Marathon bombing and Hurricane Sandy dataset. Graf et al [25] focused on cross-domain classification so that the classifier can be used across different types disaster events. They employed a cross-domain classifier and utilized emotional, sentimental, and linguistic features extracted from the damage-related tweets.…”

Section: A Unimodal Approaches 1) Text-based Disaster Identificationmentioning

confidence: 99%

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A Deep Attentive Multimodal Learning Approach for Disaster Identification From Social Media Posts

Hossain

Hoque

et al. 2022

IEEE Access

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Microblogging platforms such as Twitter have become indispensable for disseminating valuable information, especially at times of natural and man-made disasters. Often people post multimedia contents with images and/or videos to report important information such as casualties, damages of infrastructure, and urgent needs of affected people. Such information can be very helpful for humanitarian organizations for planning adequate response in a time-critical manner. However, identifying disaster information from a vast amount of posts is an arduous task, which calls for an automatic system that can filter out the actionable and non-actionable disaster-related information from social media. While many studies have shown the effectiveness of combining text and image contents for disaster identification, most previous work focused on analyzing only the textual modality and/or applied traditional recurrent neural network (RNN) or convolutional neural network (CNN) which might lead to performance degradation in case of long input sequences. This paper presents a multimodal disaster identification system that utilizes both visual and textual data in a synergistic way by conjoining the influential word features with the visual features to classify tweets. Specifically, we utilize a pretrained convolutional neural network (e.g., ResNet50) to extract visual features and a bidirectional long-term memory (BiLSTM) network with attention mechanism to extract textual features. We then aggregate both visual and textual features by leveraging a feature fusion approach followed by applying the softmax classifier. The evaluations demonstrate that the proposed multimodal system enhances the performance over the existing baselines including both unimodal and multimodal models by attaining approximately 1% and 7% of performance improvement, respectively.

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Section: A Unimodal Approaches 1) Text-based Disaster Identificationmentioning

confidence: 99%

Section: A Unimodal Approaches 1) Text-based Disaster Identificationmentioning

confidence: 99%

A Deep Attentive Multimodal Learning Approach for Disaster Identification From Social Media Posts

Hossain

Hoque

et al. 2022

IEEE Access

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Situation Awareness in Human‐Machine Systems

D’Aniello

Gaeta

2023

Handbook of Human‐Machine Systems

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A deep multi-modal neural network for informative Twitter content classification during emergencies

Kumar

Singh

Dwivedi³

et al. 2020

Ann Oper Res

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People start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents.

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Cross-domain informativeness classification for disaster situations

Cited by 8 publications

References 23 publications

A Deep Attentive Multimodal Learning Approach for Disaster Identification From Social Media Posts

A Deep Attentive Multimodal Learning Approach for Disaster Identification From Social Media Posts

Situation Awareness in Human‐Machine Systems

A deep multi-modal neural network for informative Twitter content classification during emergencies

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