Assessing Canadians Health Activity and Nutritional Habits Through Social Media

Abstract

When conducting data analysis in the twenty-first century, social media is crucial to the analysis due to the ability to provide information on a variety of topics such as health, food, feedback on products, and many others. Presently, users utilize social media to share their daily lifestyles. For example, travel locations, exercises, and food are common subjects of social media posts. By analyzing such information collected from users, health of the general population can be gauged. This analysis can become an integral part of federal efforts to study the health of a nation's people on a large scale. In this paper, we focus on such efforts from a Canadian lens. Public health is becoming a primary concern for many governments around the world. It is believed that it is necessary to analyze the current scenario within a given population before creating any new policies. Traditionally, governments use a variety of ways to gauge the flavor for any new policy including door to door surveys, a national level census, or hospital information to decide health policies. This information is limited and sometimes takes a long time to collect and analyze sufficiently enough to aid in decision making. In this paper, our approach is to solve such problems through the advancement of natural language processing algorithms and large scale data analysis. Our in-depth results show that the proposed method provides a viable solution in less time with the same accuracy when compared to traditional methods.

Utilizing deep learning and graph mining to identify drug use on Twitter data

“…A prospective solution is in social media, which has been used as a source for studying the mental activity and behavior tendencies of users [2]. Current research has gone so far as to suggest the possible validity in utilizing the information posted online as a substitution for actual surveyed data [3][4][5]. This fact is not necessarily surprising, as there is widespread utilization and sites such as Twitter are consistently accessed by a significant population of people.…”

Implications of Twitter in Health-Related Research: A Landscape Analysis of the Scientific Literature

“…More people are using Twitter as a channel and data source to do research, because it is one of the most popular forms of social media used for healthcare communication (24). For instance, its data has been entered into machine learning models for content classification, which well-demonstrated the potential of Twitter as a source for the collection, storage, visualization, and analysis of healthcarerelated Big Data in real time, and allowed assessment of relevant parameters such as health activity and nutritional habits (25,26).…”

Artificial intelligence in nutrition research: perspectives on current and future applications

“…AI-based methods are also being developed to better understand the spatial pattern of nutritionrelated information found in social media. For example, NLP and geocoding of social media posts have provided information on the real-time distribution of nutritional behaviours, habits and health outcomes, which may help in turn identify populations at risk (Ghosh and Guha, 2013, Nguyen et al, 2016, Shah et al, 2019, De Choudhury et al, 2016, Cesare et al, 2019, Widener and Li, 2014. Such information may be proven to be of great value from a public health perspective.…”

“…Such information may be proven to be of great value from a public health perspective. For instance, NLP and ML algorithms have been developed to collect and analyze data from Twitter to assess Canadian's health and nutritional habits (Shah et al, 2019). Using NLP to analyze social media content has the advantage of being less time consuming than traditional dietary assessment methods and allow quick access to information and data collected in real time (Shah et al, 2019).…”

“…However, this information can be incomplete, incorrect or biased and algorithms have inherent limitations in this regard (Lanfranchi et al, 2017, Matheny et al, 2020. For example, people may post about what they eat in a different location from where they live, creating a geographical bias (Shah et al, 2019). Also, algorithms may not always be developed to identify food words or posts that were used in a metaphorical way (Shah et al, 2019, Widener andLi, 2014).…”