Exploring social contextual influences on healthy eating using big data analytics

Yeruva, Vijaya Kumari; Junaid, Sidrah; Lee, Yugyung

doi:10.1109/bibm.2017.8217885

Cited by 5 publications

(11 citation statements)

References 23 publications

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“…A map was generated using the sentiments from food likings and food types, such as healthy and unhealthy, on top of CDC's obesity prevalence map for correlation. Liking of unhealthy food correlated with high obesity states and vice versa (Yeruva et al, 2017).…”

Section: Sentiment Analysis In Health Carementioning

confidence: 97%

“…The proposed model was able to predict flu outbreaks 5 days ahead of the Chinese Influenza Center using social media microblogging sites with location and time information (Yang et al, 2014). Another framework was developed by Yeruva et al (2017) to perform sentiment analysis on tweets and classify food sentiments and food types. A map was generated using the sentiments from food likings and food types, such as healthy and unhealthy, on top of CDC's obesity prevalence map for correlation.…”

Section: Sentiment Analysis In Health Carementioning

confidence: 99%

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Machine Learning-based Flu Forecasting Study Using the Official Data from the Centers for Disease Control and Prevention and Twitter Data

Wahid

Munkeby²,

Sambasivam

2021

IISIT

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Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

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Section: Sentiment Analysis In Health Carementioning

confidence: 97%

Section: Sentiment Analysis In Health Carementioning

confidence: 99%

Machine Learning-based Flu Forecasting Study Using the Official Data from the Centers for Disease Control and Prevention and Twitter Data

Wahid

Munkeby²,

Sambasivam

2021

IISIT

View full text Add to dashboard Cite

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“…The fourth theme involved public health including two sub-themes: public health policy and programmes in the areas of school meals, food security and sugar consumption (n = 3) (58,63,69) and food prices (n = 1) (74) . The fifth theme involved nutrition and food in general (n = 6) (66,68,73,77,78,92) including the sub-theme food and mood (n = 1) (60) . This theme also covered topics ranging from different health foods to diets, food trends and foods considered healthy and unhealthy.…”

Section: Characteristics Of Social Media Datamentioning

confidence: 99%

“…The aims and objectives of the studies varied widely and included: gathering social media users sentiment and opinions on their topic (n = 15) (62,63,(68)(69)(70)(72)(73)(74)(75)(76)(85)(86)(87)(88)92) , building a sentiment classification system for their social media data (n = 8) (71,78,79,81,84,90,93,94) , exploring their topic area and who is discussing it (n = 6) (58,59,65,66,77,83) , understanding food consumption patterns and emotion (n = 4) (60,61,67,89) and building an online system or application to apply sentiment findings (n = 1) (91) . Other studies focused on developing a system to recommend recipes based on sentiment (n = 1) (80) , monitoring health status (n = 1) (64) and exploring potential applications for machine learning in their topic area (n = 1) (82) .…”

Section: Characteristics Of Social Media Datamentioning

confidence: 99%

“…Computer Applications Journal article Food habits, physical activity and diabetes mellitus risk factors Yeruva et al 2019 (94) Twitter and PubMed The United States Computing and Engineering Journal article Obesity and healthy eating reported dates, the timeframe in which social media data were collected ranged from a 5-d period to a 9-year and 4-month-long period. Data were collected from a specific location in seven papers, the United States (63,69,77,78) , China (86) and India (93) , with other papers not specifying a location or focusing on social media across the world. Some papers collected only data that were published in a specific language, including English (60,62,63,65,68,(70)(71)(72)83,91) , Hinglish (Hindi/English) (81,82) , Marglish (Marathi/English) or Devanagiri (84) and Latvian (73) , while others did not specify.…”

Section: Indiamentioning

confidence: 99%

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The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review

et al. 2023

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Social media data is rapidly evolving and accessible which presents opportunities for research. Data science techniques, such as sentiment or emotion analysis which analyse textual emotion, provide an opportunity to gather insight from social media. This paper describes a systematic scoping review of interdisciplinary evidence to explore how sentiment or emotion analysis methods alongside other data science methods have been used to examine nutrition, food and cooking social media content. A PRISMA search strategy was used to search nine electronic databases in November 2020 and January 2022. Of 7325 studies identified, 36 studies were selected from 17 countries, and content was analysed thematically and summarised in an evidence table. Studies were published between 2014-2022 and used data from seven different social media platforms (Twitter, YouTube, Instagram, Reddit, Pinterest, Sina Weibo and mixed platforms). Five themes of research were identified including Dietary patterns, Cooking and recipes, Diet and Health, Public Health and Nutrition and Food in general. Papers developed a sentiment or emotion analysis tool or used available open-source tools. Accuracy to predict sentiment ranged from 33.33% (open-source engine) to 98.53% (engine developed for the study). The average proportion of sentiment was 38.8% positive, 46.6% neutral and 28.0% negative. Additional data science techniques used included topic modelling and network analysis. Future research requires optimising data extraction processes from social media platforms, the use of interdisciplinary teams to develop suitable and accurate methods for the subject and the use of complementary methods to gather deeper insights into these complex data.

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A Methodology to Characterize and Compute Public Perception via Social Networks

Bibi

Hussain

Ahmed

et al. 2019

Advances in Intelligent Systems and Computing

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Exploring social contextual influences on healthy eating using big data analytics

Cited by 5 publications

References 23 publications

Machine Learning-based Flu Forecasting Study Using the Official Data from the Centers for Disease Control and Prevention and Twitter Data

Machine Learning-based Flu Forecasting Study Using the Official Data from the Centers for Disease Control and Prevention and Twitter Data

The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review

A Methodology to Characterize and Compute Public Perception via Social Networks

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