Foodborne Event Detection Based on Social Media Mining: A Systematic Review
Foodborne illnesses represent a significant global health challenge, causing substantial morbidity and mortality. Conventional surveillance methods, such as laboratory-based reporting and physician notifications, often fail to enable early detection, prompting the exploration of innovative solutions...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Foods |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-8158/14/2/239 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588512592920576 |
|---|---|
| author | Silvano Salaris Honoria Ocagli Alessandra Casamento Corrado Lanera Dario Gregori |
| author_facet | Silvano Salaris Honoria Ocagli Alessandra Casamento Corrado Lanera Dario Gregori |
| author_sort | Silvano Salaris |
| collection | DOAJ |
| description | Foodborne illnesses represent a significant global health challenge, causing substantial morbidity and mortality. Conventional surveillance methods, such as laboratory-based reporting and physician notifications, often fail to enable early detection, prompting the exploration of innovative solutions. Social media platforms, combined with machine learning (ML), offer new opportunities for real-time monitoring and outbreak analysis. This systematic review evaluated the role of social networks in detecting and managing foodborne illnesses, particularly through the use of ML techniques to identify unreported events and enhance outbreak response. This review analyzed studies published up to December 2024 that utilized social media data and data mining to predict and prevent foodborne diseases. A comprehensive search was conducted across PubMed, EMBASE, CINAHL, Arxiv, Scopus, and Web of Science databases, excluding clinical trials, case reports, and reviews. Two independent reviewers screened studies using Covidence, with a third resolving conflicts. Study variables included social media platforms, ML techniques (shallow and deep learning), and model performance, with a risk of bias assessed using the PROBAST tool. The results highlighted Twitter and Yelp as primary data sources, with shallow learning models dominating the field. Many studies were identified as having high or unclear risk of bias. This review underscored the potential of social media and ML in foodborne disease surveillance and emphasizes the need for standardized methodologies and further exploration of deep learning models. |
| format | Article |
| id | doaj-art-d2b539c5bbd9427187ffc73b0f5f4efe |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-d2b539c5bbd9427187ffc73b0f5f4efe2025-01-24T13:32:59ZengMDPI AGFoods2304-81582025-01-0114223910.3390/foods14020239Foodborne Event Detection Based on Social Media Mining: A Systematic ReviewSilvano Salaris0Honoria Ocagli1Alessandra Casamento2Corrado Lanera3Dario Gregori4Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences, University of Padova, via Loredan, 18, 35121 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences, University of Padova, via Loredan, 18, 35121 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences, University of Padova, via Loredan, 18, 35121 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences, University of Padova, via Loredan, 18, 35121 Padova, ItalyUnit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, and Vascular Sciences, University of Padova, via Loredan, 18, 35121 Padova, ItalyFoodborne illnesses represent a significant global health challenge, causing substantial morbidity and mortality. Conventional surveillance methods, such as laboratory-based reporting and physician notifications, often fail to enable early detection, prompting the exploration of innovative solutions. Social media platforms, combined with machine learning (ML), offer new opportunities for real-time monitoring and outbreak analysis. This systematic review evaluated the role of social networks in detecting and managing foodborne illnesses, particularly through the use of ML techniques to identify unreported events and enhance outbreak response. This review analyzed studies published up to December 2024 that utilized social media data and data mining to predict and prevent foodborne diseases. A comprehensive search was conducted across PubMed, EMBASE, CINAHL, Arxiv, Scopus, and Web of Science databases, excluding clinical trials, case reports, and reviews. Two independent reviewers screened studies using Covidence, with a third resolving conflicts. Study variables included social media platforms, ML techniques (shallow and deep learning), and model performance, with a risk of bias assessed using the PROBAST tool. The results highlighted Twitter and Yelp as primary data sources, with shallow learning models dominating the field. Many studies were identified as having high or unclear risk of bias. This review underscored the potential of social media and ML in foodborne disease surveillance and emphasizes the need for standardized methodologies and further exploration of deep learning models.https://www.mdpi.com/2304-8158/14/2/239foodborne illnesssystematic reviewmachine learningsocial media |
| spellingShingle | Silvano Salaris Honoria Ocagli Alessandra Casamento Corrado Lanera Dario Gregori Foodborne Event Detection Based on Social Media Mining: A Systematic Review Foods foodborne illness systematic review machine learning social media |
| title | Foodborne Event Detection Based on Social Media Mining: A Systematic Review |
| title_full | Foodborne Event Detection Based on Social Media Mining: A Systematic Review |
| title_fullStr | Foodborne Event Detection Based on Social Media Mining: A Systematic Review |
| title_full_unstemmed | Foodborne Event Detection Based on Social Media Mining: A Systematic Review |
| title_short | Foodborne Event Detection Based on Social Media Mining: A Systematic Review |
| title_sort | foodborne event detection based on social media mining a systematic review |
| topic | foodborne illness systematic review machine learning social media |
| url | https://www.mdpi.com/2304-8158/14/2/239 |
| work_keys_str_mv | AT silvanosalaris foodborneeventdetectionbasedonsocialmediaminingasystematicreview AT honoriaocagli foodborneeventdetectionbasedonsocialmediaminingasystematicreview AT alessandracasamento foodborneeventdetectionbasedonsocialmediaminingasystematicreview AT corradolanera foodborneeventdetectionbasedonsocialmediaminingasystematicreview AT dariogregori foodborneeventdetectionbasedonsocialmediaminingasystematicreview |