Food Communication and its Related Sentiment in Local and Organic Food Videos on YouTube

An Exploration of e-Cigarette-Related Search Items on YouTube: Network Analysis

Background

e-Cigarette use among youth is high, which may be due in part to pro–e-cigarette content on social media such as YouTube. YouTube is also a valuable resource for learning about e-cigarette use, trends, marketing, and e-cigarette user perceptions. However, there is a lack of understanding on how similar e-cigarette–related search items result in similar or relatively mutually exclusive search results. This study uses novel methods to evaluate the relationship between e-cigarette–related search items and results.

Objective

The aim of this study is to apply network modeling and rule-based classification to characterize the relationships between e-cigarette–related search items on YouTube and gauge the level of importance of each search item as part of an e-cigarette information network on YouTube.

Methods

We used 16 fictitious YouTube profiles to retrieve 4201 distinct videos from 18 keywords related to e-cigarettes. We used network modeling to represent the relationships between the search items. Moreover, we developed a rule-based classification approach to classify videos. We used betweenness centrality (BC) and correlations between nodes (ie, search items) to help us gain knowledge of the underlying structure of the information network.

Results

By modeling search items and videos as a network, we observed that broad search items such as e-cig had the most connections to other search items, and specific search items such as cigalike had the least connections. Search items with similar words (eg, vape and vaping) and search items with similar meaning (eg, e-liquid and e-juice) yielded a high degree of connectedness. We also found that each node had 18 (SD 34.8) connections (common videos) on average. BC indicated that general search items such as electronic cigarette and vaping had high importance in the network (BC=0.00836). Our rule-based classification sorted videos into four categories: e-cigarette devices (34%-57%), cannabis vaping (16%-28%), e-liquid (14%-37%), and other (8%-22%).

Conclusions

Our findings indicate that search items on YouTube have unique relationships that vary in strength and importance. Our methods can not only be used to successfully identify the important, overlapping, and unique e-cigarette–related search items but also help determine which search items are more likely to act as a gateway to e-cigarette–related content.

Modeling the public attitude towards organic foods: a big data and text mining approach

This study aims to identify the topics that users post on Twitter about organic foods and to analyze the emotion-based sentiment of those tweets. The study addresses a call for an application of big data and text mining in different fields of research, as well as proposes more objective research methods in studies on food consumption. There is a growing interest in understanding consumer choices for foods which are caused by the predominant contribution of the food industry to climate change. So far, customer attitudes towards organic food have been studied mostly with self-reported methods, such as questionnaires and interviews, which have many limitations. Therefore, in the present study, we used big data and text mining techniques as more objective methods to analyze the public attitude about organic foods. A total of 43,724 Twitter posts were extracted with streaming Application Programming Interface (API). Latent Dirichlet Allocation (LDA) algorithm was applied for topic modeling. A test of topic significance was performed to evaluate the quality of the topics. Public sentiment was analyzed based on the NRC emotion lexicon by utilizing Syuzhet package. Topic modeling results showed that people discuss on variety of themes related to organic foods such as plant-based diet, saving the planet, organic farming and standardization, authenticity, and food delivery, etc. Sentiment analysis results suggest that people view organic foods positively, though there are also people who are skeptical about the claims that organic foods are natural and free from chemicals and pesticides. The study contributes to the field of consumer behavior by implementing research methods grounded in text mining and big data. The study contributes also to the advancement of research in the field of sustainable food consumption by providing a fresh perspective on public attitude toward organic foods, filling the gaps in existing literature and research.

Unfolding Media Source Characteristics Regarding Food Fraud Misinformation: A Comparative Study of the Health Information National Trends Survey (HINTS) in China

BACKGROUND

The ongoing rumors and fake news regarding food fraud, adulteration, and contamination are highly visible. Health risk information circulating through media and interpersonal communication channels has made health crisis an important research agenda.

OBJECTIVE

This study explores the issue of food fraud and the effect of rumors, incidents, and misinformation. Further, it studies whether and how these issues have provided evidence-based interventions for food handlers and regulators to mitigate food fraud misinformation.

METHODS

The Health Information National Trends Survey (HINTS) was adopted for collaborative study in China, after which a cross-sectional survey with door-to-door interviews was performed. Participants from Beijing and Hefei were selected using multistage sampling of adults during May, 2017. Based on four government surveillance reports on food rumors and safety incidents, a descriptive, correlation, and multivariate variance analysis was applied to the data.

RESULTS

A total of 3,090 results were gathered and analyzed. An average of 83.6% (n = 2,584) respondents heard at least one food rumor. Learning about food fraud is correlated with interpersonal connections (e.g., doctors or health specialists) for accessing food health information. Overall, Chinese citizens with a higher level of interpersonal connection were more likely to be concerned about food incidents with the statistical difference (P< .001). The Interpersonal connection was the highest frequency of communication sources (n = 698, 55.7%), followed by traditional media (n = 325, 25.9%) and Internet portal (n = 144, 11.5%). There was a significant relationship between media use and media category in Beijing (P<.001) and in Hefei (P<.001). Overall, Beijing's responses to the food fraud and incidents risks were lower than in Hefei (P < .01). The respondents in Beijing were confronted more frequently by food rumors (range 346-1253) than those in Hefei (range 155-946). The urban dwellers in Beijing and their rural counterparts in Hefei also differ in terms of perceiving different levels of food risk from different media sources. The food rumor narratives examined the conspiracy belie finds that social media play a more important role in influencing attitude against misinformation for users in Hefei, rather than in Beijing.

CONCLUSIONS

A media complementarity and food fraud information acquisition examined food fraud rumors and incidents with intent to harm, mainly done for economic gain. The HINTS China reports that around 73.6% out of 2,584 Chinese respondents prefer to go to their physicians for quarrying food health information first; however, when asked where they actually went and got access to food rumors, up to 36.6% of out of 1,462 Beijing respondents and 55.6% out of 1,122 Hefei respondents reported going online first. This study extends beyond local food products to foreign countries that import conspiracy beliefs with fake food. Nonetheless, consumers have to be on guard not just against fake food, but also spreading fake information and rumors about food. The aim of this study is to focus on characterizing media sources, types of food fraud misinformation, and risk perception of food safety, which mixes urgency and suspicions, and to provide evidence-based interventions for risk management guidance, with the hypotheses of the significant correlations between media types and sources and consumers' exposing and perceiving levels of food rumors and risks.

CLINICALTRIAL

Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

BACKGROUND

Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change.

OBJECTIVE

The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms.

METHODS

We performed a systematic review of the literature in September 2020 by searching three databases-PubMed, Web of Science, and Scopus-using relevant keywords, such as "social media," "online health communities," "machine learning," "data mining," etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies.

RESULTS

The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies.

CONCLUSIONS

Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.