Open Source Health Intelligence (OSHINT) for Foodborne Illness Event Characterization

Ordun, Catherine; Blake, Jane; Rosidi, Nathanael; Grigoryan, Vahan; Reffett, Christopher; Aslam, Sadia; Gentilcore, Anastasia; Cyran, Marek; Shelton, Matthew; Klenk, Juergen

doi:10.5210/ojphi.v5i1.4442

Cited by 9 publications

(5 citation statements)

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“…The bulk of social media-based health monitoring has relied on Twitter, a microblogging platform with over 300 million active users worldwide [ 2 ]. A wide variety of health topics are openly discussed on Twitter [ 3 ], providing researchers with a rich source of data for monitoring the spread of disease [ 4 , 5 ], dietary patterns [ 6 , 7 ], drug abuse [ 8 , 9 ], foodborne illness [ 10 , 11 ], and depression [ 12 , 13 ], among many other applications.…”

Section: Introductionmentioning

confidence: 99%

#Healthy Selfies: Exploration of Health Topics on Instagram

Muralidhara¹,

Paul²

2018

JMIR Public Health Surveill

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BackgroundSocial media provides a complementary source of information for public health surveillance. The dominate data source for this type of monitoring is the microblogging platform Twitter, which is convenient due to the free availability of public data. Less is known about the utility of other social media platforms, despite their popularity.ObjectiveThis work aims to characterize the health topics that are prominently discussed in the image-sharing platform Instagram, as a step toward understanding how this data might be used for public health research.MethodsThe study uses a topic modeling approach to discover topics in a dataset of 96,426 Instagram posts containing hashtags related to health. We use a polylingual topic model, initially developed for datasets in different natural languages, to model different modalities of data: hashtags, caption words, and image tags automatically extracted using a computer vision tool.ResultsWe identified 47 health-related topics in the data (kappa=.77), covering ten broad categories: acute illness, alternative medicine, chronic illness and pain, diet, exercise, health care & medicine, mental health, musculoskeletal health and dermatology, sleep, and substance use. The most prevalent topics were related to diet (8,293/96,426; 8.6% of posts) and exercise (7,328/96,426; 7.6% of posts).ConclusionsA large and diverse set of health topics are discussed in Instagram. The extracted image tags were generally too coarse and noisy to be used for identifying posts but were in some cases accurate for identifying images relevant to studying diet and substance use. Instagram shows potential as a source of public health information, though limitations in data collection and metadata availability may limit its use in comparison to platforms like Twitter.

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Section: Introductionmentioning

confidence: 99%

#Healthy Selfies: Exploration of Health Topics on Instagram

Muralidhara¹,

Paul²

2018

JMIR Public Health Surveill

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“…A study by Pelat et al (21) illustrated that searches for gastroenteritis were significantly correlated with incidence of acute diarrhea from the French Sentinel Network. Other studies leveraging data from social media (such as Twitter) have been able to track reports of foodborne illnesses and identify clusters suggesting outbreaks (22, 23). Most individuals who experience foodborne illnesses do not seek medical care but might be willing to share their experiences using social media platforms.…”

Section: Discussionmentioning

confidence: 99%

Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports

Nsoesie

Kluberg

Brownstein

2014

Preventive Medicine

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Objective Traditional surveillance systems only capture a fraction of the estimated 48 million yearly cases of foodborne illness in the United States. We assessed whether foodservice reviews on Yelp.com (a business review site) can be used to support foodborne illness surveillance efforts. Methods We obtained reviews from 2005–2012 of 5824 foodservice businesses closest to 29 colleges. After extracting recent reviews describing episodes of foodborne illness, we compared implicated foods to foods in outbreak reports from the U.S. Centers for Disease Control and Prevention (CDC). Results Broadly, the distribution of implicated foods across five categories was as follows: aquatic (16% Yelp, 12% CDC), dairy-eggs (23% Yelp, 23% CDC), fruits-nuts (7% Yelp, 7% CDC), meat-poultry (32% Yelp, 33% CDC), and vegetables (22% Yelp, 25% CDC). The distribution of foods across 19 more specific food categories was also similar, with spearman correlations ranging from 0.60 to 0.85 for 2006–2011. The most implicated food categories in both Yelp and CDC were beef, dairy, grains-beans, poultry and vine-stalk. Conclusions Based on observations in this study and the increased usage of social media, we posit that online illness reports could complement traditional surveillance systems by providing near real-time information on foodborne illnesses, implicated foods and locations.

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“…Recently, the food industry has looked into data science and "big data" for insight into monitoring and responding in (near) real time to contamination threats as they occur (Greis and Nogueira, 2017). In particular, a variety of text information sources have been investigated to support risk detection and communication for food safety and fraud surveillance, including online database, the Internet, and social media (Marvin et al, 2017;Nsoesie, Kluberg, & Brownstein, 2014;Ordun et al, 2013;Tiozzo et al, 2019;Waldner, 2017). For food risk and fraud surveillance, databases such as government databases and science databases, which are rich in text information, were leveraged.…”

Section: Food Safety and Food Fraud Surveillancementioning

confidence: 99%

“…More recently, scientists are interested in employing new sources of digital text information to detect food safety and food fraud incidences. Open source media outlets such as Twitter and Rich Site Summary feeds were used to characterize the 2012 Salmonella event related to cantaloupes for predicting the number of sick, dead, and hospitalized (Ordun et al., ). Twitter and Yelp were employed to identify unreported foodborne illnesses in several local public health departments of the United States, and tested in cities such as Chicago, New York, and Las Vegas (Harris et al., ; Harrison et al., ; Effland et al., ; Sadilek, et al., ).…”

Section: Application Of Text Data In Food‐related Studiesmentioning

confidence: 99%

Utilization of text mining as a big data analysis tool for food science and nutrition

Tao

Yang

Feng

2020

Comp Rev Food Sci Food Safe

154

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Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument‐generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word‐level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer‐opinion mining, new‐product development, food knowledge discovery, food supply‐chain management, and online food services. The goal is to provide insights for intelligent decision‐making to improve food production, food safety, and human nutrition.

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Open Source Health Intelligence (OSHINT) for Foodborne Illness Event Characterization

Cited by 9 publications

References 0 publications

#Healthy Selfies: Exploration of Health Topics on Instagram

#Healthy Selfies: Exploration of Health Topics on Instagram

Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports

Utilization of text mining as a big data analysis tool for food science and nutrition

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