A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications
Abstract Background Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related beha...
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| Format: | Article |
| Language: | English |
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BMC
2023-08-01
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| Series: | BMC Digital Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44247-023-00029-w |
| _version_ | 1797743971054649344 |
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| author | Shaina Raza Brian Schwartz Sahithi Lakamana Yao Ge Abeed Sarker |
| author_facet | Shaina Raza Brian Schwartz Sahithi Lakamana Yao Ge Abeed Sarker |
| author_sort | Shaina Raza |
| collection | DOAJ |
| description | Abstract Background Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications. Methods We collected Twitter data for four medications—fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication. Results The quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities. Conclusions NLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders. |
| first_indexed | 2024-03-12T15:03:09Z |
| format | Article |
| id | doaj.art-2319447e80174f96a0d71f8204f106d3 |
| institution | Directory Open Access Journal |
| issn | 2731-684X |
| language | English |
| last_indexed | 2024-03-12T15:03:09Z |
| publishDate | 2023-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Digital Health |
| spelling | doaj.art-2319447e80174f96a0d71f8204f106d32023-08-13T11:23:56ZengBMCBMC Digital Health2731-684X2023-08-011111710.1186/s44247-023-00029-wA framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medicationsShaina Raza0Brian Schwartz1Sahithi Lakamana2Yao Ge3Abeed Sarker4Dalla Lana School of Public Health, University of TorontoDalla Lana School of Public Health, University of TorontoDepartment of Biomedical Informatics, School of Medicine, Emory UniversityDepartment of Biomedical Informatics, School of Medicine, Emory UniversityDepartment of Biomedical Informatics, School of Medicine, Emory UniversityAbstract Background Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications. Methods We collected Twitter data for four medications—fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication. Results The quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities. Conclusions NLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.https://doi.org/10.1186/s44247-023-00029-wSubstance useNatural language processingMachine learningSocial media |
| spellingShingle | Shaina Raza Brian Schwartz Sahithi Lakamana Yao Ge Abeed Sarker A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications BMC Digital Health Substance use Natural language processing Machine learning Social media |
| title | A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications |
| title_full | A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications |
| title_fullStr | A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications |
| title_full_unstemmed | A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications |
| title_short | A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications |
| title_sort | framework for multi faceted content analysis of social media chatter regarding non medical use of prescription medications |
| topic | Substance use Natural language processing Machine learning Social media |
| url | https://doi.org/10.1186/s44247-023-00029-w |
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