Disaster Tweet Classification Based On Geospatial Data Using the BERT-MLP Method

Maulana, Iqbal; Maharani, Warih

doi:10.1109/icoict52021.2021.9527513

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…This novel application of transformer technologies underscores their potential in leveraging social media data for timely and accurate disaster response and management [18]. Researchers in [18] demonstrated how BERT and multi-layer perceptron (MLP) technologies have been directly applied to enhance disaster response outcomes. Focusing on the DKI Jakarta flood disaster in early 2020, the research utilized BERT for classifying tweets related to flooding incidents and MLP to process geospatial features, achieving an accuracy of 82% without stemming and with stop-word removal.…”

Section: Social-media-based Disaster Analyticsmentioning

confidence: 99%

“…This approach not only enabled the effective categorization of tweets into "flooded" and "not flooded" but also facilitated the visualization of classified tweets on a two-dimensional interactive map, thereby providing critical insights for disaster response and situational awareness. This novel application of transformer technologies underscores their potential in leveraging social media data for timely and accurate disaster response and management [18]. In another study, researchers introduce a novel average voting ensemble deep learning model (AVEDL model) that combines pre-trained transformer-based models like BERT, DistilBERT, and RoBERTa [19].…”

Section: Social-media-based Disaster Analyticsmentioning

confidence: 99%

“…Within these classification groups, studies that portray techniques for detecting and classifying various types of disasters, such as floods, wildfires, earthquakes, etc., from social media data are included. Thus, 22 of the 53 studies found during this literature review were included [18, (as shown in Table 4 and detailed in Appendix A, Table A1). As seen from Table 4, models within this category demonstrate a commendable ability to discern disaster-related content within the vast expanse of social media data, often yielding high accuracy rates in classification tasks.…”

Section: Disaster Event Detection and Classificationmentioning

confidence: 99%

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A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

Sufi

2024

Sustainability

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Transformer technologies, like generative pre-trained transformers (GPTs) and bidirectional encoder representations from transformers (BERT) are increasingly utilized for understanding diverse social media content. Despite their popularity, there is a notable absence of a systematic literature review on their application in disaster analytics. This study investigates the utilization of transformer-based technology in analyzing social media data for disaster and emergency crisis events. Leveraging a systematic review methodology, 114 related works were collated from popular databases like Web of Science and Scopus. After deduplication and following the exclusion criteria, 53 scholarly articles were analyzed, revealing insights into the geographical distribution of research efforts, trends in publication output over time, publication venues, primary research domains, and prevalently used technology. The results show a significant increase in publications since 2020, with a predominant focus on computer science, followed by engineering and decision sciences. The results emphasize that within the realm of social-media-based disaster analytics, BERT was utilized in 29 papers, BERT-based methods were employed in 28 papers, and GPT-based approaches were featured in 4 papers, indicating their predominant usage in the field. Additionally, this study presents a novel classification scheme consisting of 10 distinct categories that thoroughly categorize all existing scholarly works on disaster monitoring. However, the study acknowledges limitations related to sycophantic behavior and hallucinations in GPT-based systems and raises ethical considerations and privacy concerns associated with the use of social media data. To address these issues, it proposes strategies for enhancing model robustness, refining data validation techniques, and integrating human oversight mechanisms.

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Section: Social-media-based Disaster Analyticsmentioning

confidence: 99%

Section: Social-media-based Disaster Analyticsmentioning

confidence: 99%

Section: Disaster Event Detection and Classificationmentioning

confidence: 99%

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A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

Sufi

2024

Sustainability

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“…During training, BERT's goal is to predict the masked words, which increases context "awareness" of the model. BERT has been applied in studies focusing on urban areas such as sentiment analysis on energy-related complaints on Twitter [67] and the classification of flood-related tweets in Indonesia using the multilingual version of BERT [68].…”

Section: A Twitter Data Format and Pre-processingmentioning

confidence: 99%

Geoinformation Harvesting From Social Media Data: A community remote sensing approach

Zhu

Wang

Kochupillai

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysisready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.

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Mapping Sentiment: A Geospatial Analysis of Twitter Data in Indian Premier League 2023

Bhatt,

Singh,

Singh

2024

Lecture Notes in Networks and Systems

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Disaster Tweet Classification Based On Geospatial Data Using the BERT-MLP Method

Cited by 7 publications

References 6 publications

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

A Sustainable Way Forward: Systematic Review of Transformer Technology in Social-Media-Based Disaster Analytics

Geoinformation Harvesting From Social Media Data: A community remote sensing approach

Mapping Sentiment: A Geospatial Analysis of Twitter Data in Indian Premier League 2023

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