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A machine learning based framework for identifying consumer product injuries from social media data.

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Τίτλος: A machine learning based framework for identifying consumer product injuries from social media data.
Συγγραφείς: Bhatt H; Department of Computer Science, Purdue University, West Lafayette, IN, USA., Das S; National Institute of Technology Rourkela, Odisha, India., Han YJ; Simplisafe, Boston, MA, USA., Moghaddam M; H Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA., Nanda G; School of Engineering Technology, Purdue University, West Lafayette, IN, USA. Electronic address: gnanda@purdue.edu.
Πηγή: Injury [Injury] 2026 Feb; Vol. 57 (2), pp. 112927. Date of Electronic Publication: 2025 Dec 04.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0226040 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0267 (Electronic) Linking ISSN: 00201383 NLM ISO Abbreviation: Injury Subsets: MEDLINE
Imprint Name(s): Publication: <2002->: Amsterdam : Elsevier
Original Publication: Bristol, Wright.
Ιατρικοί όροι (MeSH): Social Media*/statistics & numerical data , Wounds and Injuries*/epidemiology , Machine Learning* , Consumer Product Safety*, United States/epidemiology ; Humans
Περίληψη: Background: Safety is a critical aspect of consumer products. However, there are millions of product related injuries reported each year. Traditional injury surveillance efforts led by public health agencies involve product related injury data collection from hospitals and subsequent injury causation analysis. This approach often requires long processing time and leads to delays in identifying emergent consumer product-related injury patterns and preventive intervention steps such as product recalls, which causes continued product related injuries.
Methods: We propose a machine learning (ML) based framework for improving injury surveillance by extracting crucial product injury related details from real-time social media posts to quickly identify emerging trends of product injuries and potentially facilitate timely interventions. We evaluated the efficacy of the proposed framework by analyzing injuries related to skateboard from the Redditt platform. The framework has two stages. In stage1, the social media posts scrapped based on product-related keywords were classified whether they were injury related or not using ML models trained on non-injury related data of Amazon product reviews and injury-related data obtained from the National Electronic Injury Surveillance System (NEISS) database. In stage2, the posts identified as injury related were further analyzed by another ML model trained on NEISS dataset to predict the body-part injured and the injury diagnosis code based on the content of Redditt post.
Results: In stage1 for classifying whether social media posts are injury related or not the deep learning models LSTM and GRU yielded an F-1 score of 72 %. In stage2, for the posts that were classified as injury related, the SGD model yielded an F-1 score of 86 % for predicting the body-part-injured and 76 % for injury diagnosis-code.
Conclusions: The results of the study indicate that the proposed machine learning framework yielded decent accuracy levels for injury surveillance purposes. Therefore, the framework can be used for analyzing social media data for identifying emerging trends in product-related injuries and can bolster the existing injury surveillance efforts.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)
Competing Interests: Declaration of competing interest The authors of the submitted manuscript entitled, “A Machine Learning Based Framework for Identifying Consumer Product Injuries from Social Media Data" - Harmya Bhatt, Dr. Souvik Das, Dr. Yi Han, Dr. Mohsen Moghaddam, and Dr. Gaurav Nanda do not have any conflicts of interest to declare.
Contributed Indexing: Keywords: Injury surveillance; Natural language processing; Product safety; Social media analysis; Text mining
Entry Date(s): Date Created: 20251209 Date Completed: 20260202 Latest Revision: 20260202
Update Code: 20260203
DOI: 10.1016/j.injury.2025.112927
PMID: 41365280
Βάση Δεδομένων: MEDLINE
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  Data: A machine learning based framework for identifying consumer product injuries from social media data.
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  Data: &lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Bhatt+H%22&quot;&gt;Bhatt H&lt;/searchLink&gt;; Department of Computer Science, Purdue University, West Lafayette, IN, USA.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Das+S%22&quot;&gt;Das S&lt;/searchLink&gt;; National Institute of Technology Rourkela, Odisha, India.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Han+YJ%22&quot;&gt;Han YJ&lt;/searchLink&gt;; Simplisafe, Boston, MA, USA.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Moghaddam+M%22&quot;&gt;Moghaddam M&lt;/searchLink&gt;; H Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Nanda+G%22&quot;&gt;Nanda G&lt;/searchLink&gt;; School of Engineering Technology, Purdue University, West Lafayette, IN, USA. Electronic address: gnanda@purdue.edu.
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  Data: Background: Safety is a critical aspect of consumer products. However, there are millions of product related injuries reported each year. Traditional injury surveillance efforts led by public health agencies involve product related injury data collection from hospitals and subsequent injury causation analysis. This approach often requires long processing time and leads to delays in identifying emergent consumer product-related injury patterns and preventive intervention steps such as product recalls, which causes continued product related injuries.&lt;br /&gt;Methods: We propose a machine learning (ML) based framework for improving injury surveillance by extracting crucial product injury related details from real-time social media posts to quickly identify emerging trends of product injuries and potentially facilitate timely interventions. We evaluated the efficacy of the proposed framework by analyzing injuries related to skateboard from the Redditt platform. The framework has two stages. In stage1, the social media posts scrapped based on product-related keywords were classified whether they were injury related or not using ML models trained on non-injury related data of Amazon product reviews and injury-related data obtained from the National Electronic Injury Surveillance System (NEISS) database. In stage2, the posts identified as injury related were further analyzed by another ML model trained on NEISS dataset to predict the body-part injured and the injury diagnosis code based on the content of Redditt post.&lt;br /&gt;Results: In stage1 for classifying whether social media posts are injury related or not the deep learning models LSTM and GRU yielded an F-1 score of 72 %. In stage2, for the posts that were classified as injury related, the SGD model yielded an F-1 score of 86 % for predicting the body-part-injured and 76 % for injury diagnosis-code.&lt;br /&gt;Conclusions: The results of the study indicate that the proposed machine learning framework yielded decent accuracy levels for injury surveillance purposes. Therefore, the framework can be used for analyzing social media data for identifying emerging trends in product-related injuries and can bolster the existing injury surveillance efforts.&lt;br /&gt; (Copyright &#169; 2025 Elsevier Ltd. All rights reserved.)
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  Data: Declaration of competing interest The authors of the submitted manuscript entitled, “A Machine Learning Based Framework for Identifying Consumer Product Injuries from Social Media Data&quot; - Harmya Bhatt, Dr. Souvik Das, Dr. Yi Han, Dr. Mohsen Moghaddam, and Dr. Gaurav Nanda do not have any conflicts of interest to declare.
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