A decision support system for extracting artificial intelligence-driven insights from live twitter feeds on natural disasters

Sufi, Fahim

doi:10.1016/j.dajour.2022.100130

Cited by 13 publications

(8 citation statements)

References 23 publications

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“…Figure 12 shows the deployment of the GPT-based solution in the latest Samsung Galaxy S23 Ultra mobile phone using Microsoft Power BI's deployed App. The application of this deployment process has been showcased in recent studies through the utilization of low-code platforms [27,[30][31][32]34]. As this study exclusively solved the labeled data scarcity for training machine learning models within medical domain (as discussed in [1,2,10]), it needs to be demonstrated how the generated synthetic data could be used in machine leanirng.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…Data Generation, Augmentation, and Labeling: To generate new features from data with limited fields, machine learning techniques like entity recognition, category classification, sentiment analysis, and others have traditionally been used [27][28][29][30][31][32][33][34].…”

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confidence: 99%

“…Information 2024, 15, x FOR PEER REVIEW 6 of 46 generating new features, the augmented data can be used to effectively train the machine learning models [27][28][29][30][31][32][33][34]. However, with the advent of GPT, new features could be generated either from synthetic data or from existing data, without using traditional feature extraction approaches, as shown in .…”

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confidence: 99%

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Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

Sufi

2024

Information

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This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves the synthetic generation of comprehensive patient discharge messages, setting a new standard in the field with GPT autonomously generating 20 fields. Through a meticulous review of the existing literature, we systematically explore GPT’s aptitude for synthetic data generation and feature extraction, providing a robust foundation for subsequent phases of the research. The empirical demonstration showcases the transformative potential of our proposed solution, presenting over 70 patient discharge messages with synthetically generated fields, including severity and chances of hospital re-admission with justification. Moreover, the data had been deployed in a mobile solution where regression algorithms autonomously identified the correlated factors for ascertaining the severity of patients’ conditions. This study not only establishes a novel and comprehensive methodology but also contributes significantly to medical machine learning, presenting the most extensive patient discharge summaries reported in the literature. The results underscore the efficacy of GPT in overcoming data scarcity challenges and pave the way for future research to refine and expand the application of GPT in diverse medical contexts.

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

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Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

Sufi

2024

Information

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“…According to [69], influential users are considerably more important to be examined as they can provide valuable knowledge within the tweets concerning opinions related to COVID-19 vaccines. Thus, the findings enabled researchers to thoroughly examine the ego networks of the three user clusters: pro-vaxxers, neutrals, and anti-vaxxers.…”

Section: B the Relevant Sectors Of Activity Where The Clustering Algo...mentioning

confidence: 99%

Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

Pavaloaia

2024

IJACSA

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Based on the high dynamic of Sentiment Analysis (SA) topic among the latest publication landscape, the current review attempts to fill a research gap. Consequently, the paper elaborates on the most recent body of literature to extract and analyze the papers that elaborate on the clustering algorithms applied on social media datasets for performing SA. The current rapid review attempts to answer the research questions by analyzing a pool of 46 articles published in between Dec 2020 -Dec 2023. The manuscripts were thoroughly selected from Scopus (Sco) and WebOf-Science (WoS) databases and, after filtering the initial pool of 164 articles, the final results (46) were extracted and read in full.

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“…A multitude of active social media users regularly disseminate real-time updates concerning critical crisis events, including earthquakes, landslides, floods, shootings, wildfires, and even pandemics. Owing to the dearth of officially curated disaster data repositories, recent scholarly endeavors have turned to harnessing social media posts to identify and evaluate natural calamities such as landslides, floods, earthquakes, typhoons, wildfires, and others [6][7][8]. Moreover, the dissemination, evaluation, and repercussions of the COVID-19 pandemic have been elucidated through the innovative application of ML and AI algorithms to live social media content, exemplified by platforms such as Twitter [9,10].…”

Section: Introductionmentioning

confidence: 99%

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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A decision support system for extracting artificial intelligence-driven insights from live twitter feeds on natural disasters

Cited by 13 publications

References 23 publications

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

Clustering Algorithms in Sentiment Analysis Techniques in Social Media – A Rapid Literature Review

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

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