Cross-domain informativeness classification for disaster situations

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

doi:10.1145/3281375.3281385

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…We consider 48 features 2 represented in numerical and binary form. Some of these features have been previously used for classification of crisis messages (Graf et al 2018;Khare et al 2018). These features describe traditional message characteristics such as the number of characters, words, links, mentions, hashtags, question marks, among others.…”

Section: Data Representationsmentioning

confidence: 99%

“…Its short-text messages, called tweets, have been widely studied to extract useful and timely knowledge for crisis management (Olteanu et al 2014;Olteanu, Vieweg, and Castillo 2015;Cresci et al 2015;Imran, Mitra, and Castillo 2016;Alam, Ofli, and Imran 2018). Accurate and well-timed information about a crisis allows emergency relief agencies to act quickly and effectively, thus reducing the negative impact on society (Graf et al 2018). In this context, automatically identifying user messages related to crises becomes relevant.…”

Section: Introductionmentioning

confidence: 99%

“…We consider this particular task to be relevant because of the high volume of noisy data that is found in Twitter during crisis events. This noise consists mostly of unrelated content, such as spam and advertising, that can be found even when message retrieval is done using event-specific keywords (such as hashtags or locations associated with the crisis) (Olteanu, Vieweg, and Castillo 2015;Graf et al 2018). Identifying related and unrelated messages is one of the first and foremost tasks required by emergency practitioners when using social media data.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios

Sánchez

Sarmiento

Abeliuk

et al. 2023

ICWSM

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Social media data has emerged as a useful source of timely information about real-world crisis events. One of the main tasks related to the use of social media for disaster management is the automatic identification of crisis-related messages. Most of the studies on this topic have focused on the analysis of data for a particular type of event in a specific language. This limits the possibility of generalizing existing approaches because models cannot be directly applied to new types of events or other languages. In this work, we study the task of automatically classifying messages that are related to crisis events by leveraging cross-language and cross-domain labeled data. Our goal is to make use of labeled data from high-resource languages to classify messages from other (low-resource) languages and/or of new (previously unseen) types of crisis situations. For our study we consolidated from the literature a large unified dataset containing multiple crisis events and languages. Our empirical findings show that it is indeed possible to leverage data from crisis events in English to classify the same type of event in other languages, such as Spanish and Italian (80.0% F1-score). Furthermore, we achieve good performance for the cross-domain task (80.0% F1-score) in a cross-lingual setting. Overall, our work contributes to improving the data scarcity problem that is so important for multilingual crisis classification. In particular, mitigating cold-start situations in emergency events, when time is of essence.

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Section: Data Representationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios

Sánchez

Sarmiento

Abeliuk

et al. 2023

ICWSM

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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%

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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Situation Awareness in Human‐Machine Systems

D’Aniello

Gaeta

2023

Handbook of Human‐Machine Systems

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Cross-domain informativeness classification for disaster situations

Cited by 10 publications

References 23 publications

Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios

Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios

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

Situation Awareness in Human‐Machine Systems

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