The JORD System

Ahmad, Kashif; Riegler, Michael; Riaz, Ans; Conci, Nicola; Dang-Nguyen, Duc-Tien; Halvorsen, Pål

doi:10.1145/3078971.3079013

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…In the case of combining visual and textual data, state-of-the-art methods such as Sheharyar et al [23] use AlexNet and the keyword ranking approach which combines visual and textual information. However, the AlexNet is a relatively shallow CNN compared to ResNet50, which allows for capturing more complex features and intricate representations of visual data.…”

Section: Results Of Deepsdc: Dirsm Datasetmentioning

confidence: 99%

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DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

et al. 2023

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Disasters such as earthquakes, droughts, floods, and volcanoes adversely affect human lives and valuable resources. Therefore, various response systems have been designed, which assist in mitigating the impact of disasters and facilitating relief activities in the aftermath of a disaster. These response systems require timely and accurate information about affected areas. In recent years, social media has provided access to high-volume real-time data, which can be used for advanced solutions to numerous problems, including disasters. Social-media data combines two modalities (text and associated images), and this information can be used to detect disasters, such as floods. This paper proposes an ensemble learning-based Deep Social Media Data Classification (DeepSDC) approach for social-media flood-event classification. The proposed algorithm uses datasets from Twitter to detect the flooding event. The Deep Social Media Data Classification (DeepSDC) uses a two-staged ensemble-learning approach which combines separate models for textual and visual data. These models obtain diverse information from the text and images and combine the information using an ensemble-learning approach. Additionally, DeepSDC utilizes different augmentation, upsampling and downsampling techniques to tackle the class-imbalance challenge. The performance of the proposed algorithm is assessed on three publically available flood-detection datasets. The experimental results show that the proposed DeepSDC is able to produce superior performance when compared with several state-of-the-art algorithms. For the three datasets, FRMT, FCSM and DIRSM, the proposed approach produced F1 scores of 46.52, 92.87, and 92.65, respectively. The mean average precision (MAP@480) of 91.29 and 98.94 were obtained on textual and a combination of textual and visual data, respectively.

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Section: Results Of Deepsdc: Dirsm Datasetmentioning

confidence: 99%

“…One example is classifying social-media images and text data based on the availability of flooding events [13][14][15]. Several flood-detection algorithms have been used successfully based on social-media textual and visual data [16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

et al. 2023

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“…Social media content has also been widely explored for different tasks during natural disasters [27]. For instance, Ahmad et al [8] proposed an end-to-end system able to detect disaster events in social media and automatically collect relevant images, videos, and text, providing a detailed overview of the events. The system also utilizes satellite imagery to provide a better view of the disaster-affected areas.…”

Section: Related Workmentioning

confidence: 99%

A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media

Mehmood,

Ayub,

Zamir

et al. 2024

Preprint

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Over the last decade, similar to other application domains, social media content has been widely explored for disaster informatics. However, due to the unstructured nature of the data, several challenges are associated with disaster analysis in social media content. For example, social media content is generally very noisy containing disaster-related words (e.g., floods, wildfires) used in a different context. Similarly, most of social media posts either do not contain geo-location information or do not represent the actual disaster location. To fully explore the potential of social media content in disaster informatics, access to relevant content and the correct geo-location information is very critical. In this paper, we propose a three-step solution to tackling these challenges. Firstly, the proposed solution aims at the classification of social media posts into relevant and irrelevant posts followed by the automatic extraction of location information from the posts' text itself through Named Entity Recognition (NER) analysis. Finally, to quickly analyze the topics covered in large volumes of social media posts, we perform topic modeling resulting in a list of top keywords, that highlight the issues discussed in the tweet. For the classification of the tweets, we proposed a merit-based fusion framework combining the capabilities of multiple Large Language Models (LLMs), obtaining the highest F1-score of 0.933 on a benchmark dataset. For the Location Extraction from Twitter Text (LETT), we evaluated four LLMs obtaining the highest F1-score of 0.960. For topic modeling, we used the BERTopic library to discover the hidden topic patterns in the relevant tweets. The experimental results of all the components of the proposed end-to-end solution are encouraging and hint at the effectiveness of social media content and NLP in disaster management.

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Disaster Severity Prediction from Twitter Images

Kumar

Singh

2020

Advances in Intelligent Systems and Computing

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The JORD System

Cited by 7 publications

References 11 publications

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events

A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media

Disaster Severity Prediction from Twitter Images

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