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The COVID-19 pandemic has generated an infodemic, an overabundance of online and offline information. In this context, accurate information as well as misinformation and disinformation about the links between nutrition and COVID-19 have circulated on Twitter since the onset of the pandemic.
The purpose of this study was to compare tweets on nutrition in times of COVID-19 published by 2 groups, namely, a preidentified group of dietitians and a group of general users of Twitter, in terms of themes, content accuracy, use of behavior change factors, and user engagement, in order to contrast their information sharing behaviors during the pandemic.
Public English-language tweets published between December 31, 2019, and December 31, 2020, by 625 dietitians from Canada and the United States, and Twitter users were collected using hashtags and keywords related to nutrition and COVID-19. After filtration, tweets were coded against an original codebook of themes and the Theoretical Domains Framework (TDF) for identifying behavior change factors, and were compared to reliable nutritional recommendations pertaining to COVID-19. The numbers of likes, replies, and retweets per tweet were also collected to determine user engagement.
In total, 2886 tweets (dietitians, n=1417; public, n=1469) were included in the analyses. Differences in frequency between groups were found in 11 out of 15 themes. Grocery (271/1417, 19.1%), and diets and dietary patterns (n=507, 34.5%) were the most frequently addressed themes by dietitians and the public, respectively. For 9 out of 14 TDF domains, there were differences in the frequency of usage between groups. “Skills” was the most used domain by both groups, although they used it in different proportions (dietitians: 612/1417, 43.2% vs public: 529/1469, 36.0%;
Differences in tweets between groups, notably ones related to content accuracy, themes, and engagement in the form of likes, shed light on potentially useful and relevant elements to include in timely social media interventions aiming at fighting the COVID-19–related infodemic or future infodemics.
On January 7, 2020, Chinese health authorities officially announced the emergence of the disease caused by the 2019 novel coronavirus [
From its onset, the pandemic has triggered multiple studies as clinical data were rapidly needed to face and fight the infection [
In parallel, social media are equally being used as sources of health information and as platforms to disseminate health-related recommendations [
Indeed, recently, the COVID-19 pandemic has played a major role in demonstrating how social media can be helpful as well as detrimental. The pandemic has led to what the World Health Organization calls an “infodemic,” an overabundance of information online and offline, which may be true or false. Although an infodemic is not solely characterized by false information, it certainly contributes to its propagation. This situation can result in different repercussions, including damage to physical and mental health, increased stigma and conflict, and a lack of compliance with public health measures [
Nutrition has received interest from researchers, official health organizations, and the general population since the beginning of the pandemic. In parallel, social media posts to this effect have also risen, and it is possible that misinformation and disinformation have also reached some of these communication platforms. Knowing this, some sources of information, including lay people, can be unreliable and could contribute to the proliferation and dissemination of misinformation and disinformation on nutrition-related topics. Conversely, dietitians are recognized as nutrition experts and should be prioritized when seeking information on food and nutrition [
Researchers have started exploring how social media publications regarding COVID-19 could influence intention, behavior, and protection against the virus [
The aim of this study was to compare the information sharing behaviors of registered dietitians (RDs) and Twitter users during the infodemic by analyzing their tweets related to nutrition in times of COVID-19. To do so, we compared the tweets of the 2 groups in terms of their themes, the user engagement they generated, content accuracy, and whether tweets included behavior change factors. To this end, we elaborated some research questions to be answered. Research questions are normally inquisitive in nature and better suited for exploratory studies where too little data are available to develop hypotheses [
1. What are the differences between dietitians’ tweets and the public’s tweets in terms of the themes they discuss?
2. What are the differences between dietitians’ tweets and the public’s tweets in terms of the engagement they receive from users?
3. What is the difference in content accuracy between dietitians’ tweets and the public’s tweets?
4. What are the differences between dietitians’ tweets and the public’s tweets in terms of the TDF domains they use, and could their tweets influence behavior?
This study’s methods can be divided in 2 phases, namely, preanalytical procedures and analyses, as represented in
Study’s synthesized methodology.
In order to identify our sample of RDs from Canada and the United States with Twitter accounts, the Dietitians of Canada Member Blogs list [
Flow chart detailing the steps for creating the registered dietitians (RDs) list using the Nutrition Blog Network (NBN) author directory and the Dietitians of Canada (DC) Member Blogs list.
A predetermined list of 2561 hashtags and keywords related to COVID-19, 41 hashtags related to nutrition, and 16 hashtags related to both was used to filter tweets from the public and RDs (eg, “coronavirus,” “#immunity,” “#coviddiet,” “#health,” and “#nutrition”). The method for identifying hashtags and keywords was inspired by previous studies [
To be considered for the analysis, tweets had to be written in English, discuss at least one aspect of nutrition in times of COVID-19, and be published between December 31, 2019, and December 31, 2020. December 31, 2019, marks the date when cases of an unknown acute respiratory disease in Wuhan were first reported by Chinese health authorities [
Thus, the first step consisted of collecting the data using a predetermined list of hashtags and keywords, which resulted in 6670 tweets for the public group and 4627 tweets for the dietitian group. After revising a subsample of each group, we observed that only 26.0% and 41.4% of the public’s tweets and dietitians’ tweets, respectively, were about both nutrition and COVID-19. The predetermined list of hashtags and keywords was thus enriched to render our data more specific to COVID-19 and nutrition. First, using tweets pertaining to COVID-19/nutrition from our 2 revised subsamples (see step 1 in
Steps detailing tweet collection resulting in the final samples for analysis.
Considering the difficulties associated with this type of data collection [
The infodemic has generated multiple discussions on social media, which can reduce access to reliable information [
Members of the public are not necessarily reliable sources of information on nutrition, while dietitians are considered reliable sources. This can become problematic when members of the public generate more engagement in their posts than their expert counterparts. In order to find out whether certain themes were more popular than others from a reader’s perspective, the user engagement generated by themes was evaluated based on the numbers of likes, replies, and retweets associated with tweets. More specifically, for both subsamples separately, the mean numbers of likes, replies, and retweets for a single tweet were calculated for each theme. The means were then compared between groups to determine if certain themes were more popular in one group than the other. Additionally, the proportion of dietitians’ tweets related to COVID-19 and nutrition out of their total yearly publications was calculated to evaluate their own engagement in this conversation on Twitter.
To determine tweets’ content accuracy and thus reveal the presence of misinformation, 2 team members (EC and LJC) compared the 2886 tweets against evidence-based nutrition and food-related recommendations regarding COVID-19. First, a database of recommendations from reliable and expert sources that covered COVID-19 and nutrition-related themes, such as Dietitians of Canada, Health Canada, and the Academy of Nutrition and Dietetics, was elaborated through web searches. However, when a tweet’s content was too specific to be compared to the aforementioned recommendations, it became necessary to use more specialized sources of information (eg, PubMed and Mayo Clinic). For instance, the following tweet’s content could not be found in our database of recommendations: “If your body happens to change during the pandemic, it could be because of stress […].” Second, during coding, coders read the tweet and verified its information using one or many reliable recommendations pertaining to the specific content of that tweet. If its content was in line with the recommendation, it was deemed accurate. If the content differed from the recommendation in any way, it was deemed inaccurate. Thus, tweets were categorized as accurate, inaccurate, or not applicable. The “not applicable” category was used when it was impossible to determine the tweet’s accuracy for one or more of the following reasons: (1) the tweet is sharing a recipe or meal idea, (2) it is formulated as a question, (3) it reports on study results, and (4) it is considered as a nonscientific declaration or an opinion. For this study, it was decided that although study results pertaining to COVID-19 and nutrition could be compared to other studies, which are part of a body of evidence still in development, they include emerging data and not suggestions or advice to be followed. Moreover, they are too preliminary and specific to their study’s methodology and population to be compared against nutritional recommendations about COVID-19. Moreover, although opinions or nonscientific declarations can be based on unsupported claims, for this study, it was decided that they could not be evaluated for accuracy. Indeed, this category could include tweets related to, for instance, what the users ate that day, a new nutrition-related habit they developed during the pandemic, or words of encouragement for workers in the food industry. As the evaluation went on from April through July 2021 and was then based on the current and available recommendations at that time, it is possible that the categorization would be different at the time when this paper has been written or published. Nevertheless, we made sure to use the most up to date information by regularly verifying updates in recommendations and available documentation. Saturation, which was determined by identifying the point where the 3 possible categorizations had been coded at least once, was reached after 25 tweets for the dietitian group and 13 tweets for the public group. Finally, the frequencies of accurate and inaccurate tweets were compared between groups. The frequencies of the nonapplicable categorization and of the 4 reasons why a tweet’s accuracy could not be evaluated were also compared between groups. Moreover, further analyses were performed to compare the numbers of accurate and inaccurate mentions for each theme, so as to bring out those more frequently inaccurate than accurate.
Acting upon misinformation and disinformation can have detrimental effects. Therefore, to verify if tweets could potentially influence readers’ behaviors, the 2886 tweets were deductively coded by 2 team members (EC and LJC) using the second version of the TDF [
Description of the Theoretical Domains Framework domains.
Domain | Description [ |
Knowledge | Awareness of something |
Skills | Ability or competence developed through practice |
Social and professional role and identity | Individual behaviors and qualities displayed in a social or work setting |
Beliefs about capabilities | Recognition of one’s competences and abilities that can be put to constructive use |
Optimism | Confidence that goals and desires will be reached |
Beliefs about consequences | Expectancies about outcomes of a behavior in a situation |
Reinforcement | Increasing the probability of a behavior with a stimulus |
Intentions | Decision to accomplish a behavior or to act in a certain way |
Goals | Mental representations of outcomes one wants to attain |
Memory, attention, and decision processes | Ability to remember information, focus, and choose between different alternatives |
Environmental context and resources | Situational or environmental aspect of one’s life that encourages or discourages the adoption of an adaptive behavior, skill, or competence |
Social influences | Interpersonal processes that lead one to modify their thoughts, feelings, or behaviors |
Emotion | Complex reaction by which one attempts to manage a personally significant matter or event |
Behavioral regulation | Something done to manage or change one’s actions |
The 14 domains were not mutually exclusive. Saturation, which was determined by identifying the point where all domains had been addressed at least once, was reached after 54 tweets for the dietitian group and 13 tweets for the public group. The frequency of each domain was compared between groups. Exploratory analyses were also conducted to reveal the most and least frequent domains for each theme.
Lastly, intercoder agreement, which measures the degree of similarity in codes assigned to a data set by different coders, was determined so as to preserve the consistency of results during individual coding [
Kappa scores obtained after 2 rounds of reliability coding.
Group | COVID-19/nutrition or not (1st round) | Content accuracy (1st round) | Themes (1st and 2nd rounds) | Domains (1st and 2nd rounds) |
Public | 0.78 | 0.67 | 0.54 and 0.65 | 0.42 and 0.63 |
Dietitian | 0.95 | 0.78 | 0.51 and 0.79 | 0.66 and 0.75 |
Statistical analyses were performed in SAS OnDemand for Academics (SAS Institute Inc). A
The Université Laval Research Ethics Board exempted this project from ethical review as analyses were completed with publicly available content. However, complete examples of tweets have not been presented in order to preserve the anonymity of the Twitter users.
The number of themes about nutrition and COVID-19 found in this study supports the fact that the infodemic has also reached this thematic.
Comparison of theme frequencies between groups.
Theme | Description | Dietitian group (N=1417), n (%) | Public group (N=1469), n (%) | |
Weight loss | Tips, mention, desire, and promotion. Not necessarily due to the pandemic. | 24 (1.7) | 106 (7.2) | <.001 |
Cooking and recipes | Sharing of recipes or meal/snack ideas. Mentions of what the next meal will be. | 215 (15.2) | 214 (14.6) | .65 |
Immune health | Linking nutrients, supplements, and foods, as well as physical activity, healthy eating, and hydration with immunity. | 177 (12.5) | 87 (5.9) | <.001 |
Food support and food system | Food support programs, food services/systems, buying local, gardening, and food insecurity. | 206 (14.5) | 59 (4.0) | <.001 |
Specific foods | Mention, consumption, or promotion of foods of various nutritional values. | 178 (12.6) | 487 (33.2) | <.001 |
Alcohol consumption | Reference to alcohol or mention of consumption. | 19 (1.3) | 86 (5.9) | <.001 |
Nutrients and supplements | Mention or promotion of a nutrient or supplement, regardless of immunity. | 80 (5.7) | 81 (5.5) | .88 |
Overeating | Mention of eating a large quantity of food in one sitting. | 18 (1.3) | 65 (4.4) | <.001 |
Food tips and recommendations | Hydration, suggestion of certain foods or practices, healthy restaurant food choices, and sanitary measures in restaurants. | 253 (17.9) | 108 (7.4) | <.001 |
Food changes | Modification of food choices, habits, and offers due to the pandemic, except for diets. | 173 (12.2) | 149 (10.1) | .08 |
Body appearance | References to physical appearance regardless of weight loss; includes weight gain. | 86 (6.1) | 67 (4.6) | .07 |
Diets and dietary patterns | Mention or promotion of diets, dietary patterns, and related practices. | 26 (1.8) | 507 (34.5) | <.001 |
Other lifestyle habits | References to physical activity (without mention of weight loss), stress/anxiety, sleep, tobacco, and cannabis. | 259 (18.3) | 453 (30.8) | <.001 |
Grocery | Food safety, in-store sanitary measures, healthy food choices at the store, ways to reduce grocery bills, and increased/decreased availability of products. | 271 (19.1) | 68 (4.6) | <.001 |
Health care system | Changes in dietetics practice, underlying health conditions, and nutrition of infected patients. | 209 (14.8) | 23 (1.6) | <.001 |
Comparison of themes between the first 2 waves of the pandemic revealed that none of the themes were more frequently addressed in the second wave than in the first by either of the groups. Indeed, 83.0% of dietitians’ tweets were published during the first wave. Weight loss (
Comparison of the mean number of retweets per tweet between groups.
Theme | Dietitian group | Public group | |||
|
Number of tweets | Number of retweets per tweet, mean (SD) | Number of tweets | Number of retweets per tweet, mean (SD) |
|
Weight loss | 24 | 23.96 (77.49) | 106 | 0.066 (0.42) | <.001 |
Cooking and recipes | 215 | 149.91 (2181.49) | 214 | 0.028 (0.17) | <.001 |
Immune health | 177 | 11.99 (65.36) | 87 | 0.092 (0.33) | <.001 |
Food support and food system | 206 | 569.18 (5040.55) | 59 | 0.017 (0.13) | <.001 |
Specific foods | 178 | 182.53 (23.97) | 487 | 0.037 (0.22) | <.001 |
Alcohol consumption | 19 | 76.16 (327.37) | 86 | 0.047 (0.21) | <.001 |
Nutrients and supplements | 80 | 9.61 (43.43) | 81 | 0.11 (0.39) | <.001 |
Food overconsumption | 18 | 7.78 (23.37) | 65 | 0.015 (0.12) | <.001 |
Food tips and recommendations | 253 | 45.71 (677.18) | 108 | 0.14 (0.50) | <.001 |
Food changes | 173 | 1197.90 (15693.72) | 149 | 0.013 (0.12) | <.001 |
Body appearance | 86 | 242.36 (1424.06) | 67 | 0.045 (0.21) | <.001 |
Diets and dietary patterns | 26 | 5.31 (15.71) | 507 | 0.018 (0.15) | <.001 |
Other lifestyle habits | 259 | 22.37 (141.21) | 453 | 0.15 (0.67) | <.001 |
Grocery | 271 | 1176.28 (9564.04) | 68 | 0.074 (0.31) | <.001 |
Health care system | 209 | 65.30 (624.76) | 23 | 0 (0) | <.001 |
Comparison of the mean number of replies per tweet between groups.
Theme | Dietitian group | Public group | |||||
|
Number of tweets | Number of replies per tweet, mean (SD) | Number of tweets | Number of replies per tweet, mean (SD) |
|
||
Weight loss | 24 | 0 (0) | 106 | 0.75 (5.073) | .02 | ||
Cooking and recipes | 215 | 0 (0) | 214 | 0.32 (0.99) | <.001 | ||
Immune health | 177 | 0 (0) | 87 | 0.023 (0.15) | .04 | ||
Food support and food system | 206 | 0 (0) | 59 | 0.068 (0.31) | .001 | ||
Specific foods | 178 | 0 (0) | 487 | 0.44 (1.15) | <.001 | ||
Alcohol consumption | 19 | 0 (0) | 86 | 0.52 (1.49) | .01 | ||
Nutrients and supplements | 80 | 0 (0) | 81 | 0.21 (1.03) | .003 | ||
Food overconsumption | 18 | 0 (0) | 65 | 0.74 (1.57) | .01 | ||
Food tips and recommendations | 253 | 0 (0) | 108 | 0.13 (0.91) | .002 | ||
Food changes | 173 | 0 (0) | 149 | 0.66 (1.43) | <.001 | ||
Body appearance | 86 | 0 (0) | 67 | 1.06 (6.37) | <.001 | ||
Diets and dietary patterns | 26 | 0 (0) | 507 | 0.55 (2.57) | .005 | ||
Other lifestyle habits | 259 | 0 (0) | 453 | 0.080 (0.34) | <.001 | ||
Grocery | 271 | 0 (0) | 68 | 0.13 (0.39) | <.001 | ||
Health care system | 209 | 0 (0) | 23 | 0.044 (0.21) | .003 |
Comparison of the mean number of likes per tweet between groups.
Theme | Dietitian group | Public group | |||
|
Number of tweets | Number of likes per tweet, mean (SD) | Number of tweets | Number of likes per tweet, mean (SD) |
|
Weight loss | 24 | 14.58 (32.60) | 106 | 1.44 (3.73) | .03 |
Cooking and recipes | 215 | 2.44 (5.88) | 214 | 2.02 (5.20) | .18 |
Immune health | 177 | 5.23 (30.95) | 87 | 0.29 (0.59) | <.001 |
Food support and food system | 206 | 2.67 (6.67) | 59 | 0.41 (1.04) | .007 |
Specific foods | 178 | 2.76 (7.66) | 487 | 2.04 (4.72) | .65 |
Alcohol consumption | 19 | 4.84 (13.87) | 86 | 2.21 (5.37) | .66 |
Nutrients and supplements | 80 | 2.00 (5.53) | 81 | 1.02 (4.83) | .003 |
Food overconsumption | 18 | 1.61 (2.48) | 65 | 3.03 (6.22) | .51 |
Food tips and recommendations | 253 | 1.66 (5.51) | 108 | 0.69 (2.98) | <.001 |
Food changes | 173 | 1.67 (2.90) | 149 | 2.40 (5.30) | .46 |
Body appearance | 86 | 5.70 (18.49) | 67 | 1.78 (4.70) | .81 |
Diets and dietary patterns | 26 | 13.85 (66.33) | 507 | 2.11 (4.85) | .52 |
Other lifestyle habits | 259 | 1.57 (4.23) | 453 | 1.63 (11.74) | .04 |
Grocery | 271 | 1.89 (6.06) | 68 | 0.92 (1.66) | .34 |
Health care system | 209 | 2.13 (8.89) | 23 | 0.22 (0.42) | .06 |
Content accuracy analyses revealed the presence of misinformation, but mostly in the public’s tweets. In fact, a higher proportion of dietitians’ tweets were accurate compared with the public’s tweets (
Content accuracy of individual themes.
Theme | Accurate (N=782), n (%) | Inaccurate (N=175), n (%) | |
Weight loss | 11 (1.4) | 30 (17.1) | <.001 |
Cooking and recipes | 45 (5.8) | 15 (8.6) | .16 |
Immune health | 128 (16.4) | 77 (44.0) | <.001 |
Food support and food system | 91 (11.6) | 1 (0.6) | <.001 |
Specific foods | 105 (13.4) | 28 (16.0) | .37 |
Alcohol consumption | 20 (2.6) | 7 (4.0) | .30 |
Nutrients and supplements | 78 (10.0) | 26 (14.9) | .06 |
Food overconsumption | 12 (1.5) | 4 (2.3) | .51 |
Food tips and recommendations | 224 (28.6) | 17 (9.7) | <.001 |
Food changes | 57 (7.3) | 4 (2.3) | .02 |
Body appearance | 14 (1.8) | 3 (1.7) | <.99 |
Diets and dietary patterns | 35 (4.5) | 22 (12.6) | <.001 |
Other lifestyle habits | 219 (28.0) | 34 (19.4) | .02 |
Grocery | 215 (27.5) | 14 (8.0) | <.001 |
Health care system | 74 (9.5) | 5 (2.9) | .004 |
Furthermore, 842 (59.4%) of the dietitians’ tweets and 1087 (74.0%) of the public’s tweets were deemed not applicable for accuracy evaluation. More specifically, there were differences between groups for 3 reasons out of 4. First, a recipe or meal idea was shared more often in the public’s tweets than in dietitians’ tweets (332/1087, 30.5% vs 205/842, 24.4%;
Comparison of the frequency of Theoretical Domains Framework domains between groups.
Domain | Dietitian group (N=1417), n (%) | Public group (N=1469), n (%) | |
Knowledge | 576 (40.7) | 265 (18.0) | <.001 |
Skills | 612 (43.2) | 529 (36.0) | <.001 |
Social and professional role and identity | 123 (8.7) | 17 (1.2) | <.001 |
Beliefs about capabilities | 100 (7.1) | 114 (7.8) | .47 |
Optimism | 121 (8.5) | 106 (7.2) | .19 |
Beliefs about consequences | 354 (25.0) | 306 (20.6) | .008 |
Reinforcement | 303 (21.4) | 375 (25.5) | .009 |
Intentions | 43 (3.0) | 64 (4.4) | .06 |
Goals | 61 (4.3) | 290 (19.7) | <.001 |
Memory, attention, and decision processes | 105 (7.4) | 49 (3.3) | <.001 |
Environmental context and resources | 471 (33.2) | 482 (32.8) | .81 |
Social influences | 50 (3.5) | 41 (2.8) | .26 |
Emotion | 130 (9.2) | 61 (4.2) | <.001 |
Behavioral regulation | 246 (17.4) | 465 (31.7) | <.001 |
The frequency of Theoretical Domains Framework domains for individual themes.
Theme | Most frequent domain | Frequency, n (%) | Least frequent domain | Frequency, n (%) |
Weight loss (N=130) | Goals | 59 (45.4) | Memory, attention and decision processes, and emotion | 3 (2.3) |
Cooking and recipes (N=429) | Skills | 343 (80.0) | Social and professional role and identity | 5 (1.2) |
Immune health (N=264) | Knowledge | 200 (75.8) | Intentions | 2 (0.8) |
Food support and food system (N=265) | Environmental context and resources | 153 (57.7) | Social influences | 5 (1.9) |
Specific foods (N=665) | Environmental context and resources | 273 (41.1) | Social and professional role and identity, and emotion | 6 (0.9) |
Alcohol consumption (N=105) | Environmental context and resources | 60 (57.1) | Optimism and social influences | 2 (1.9) |
Nutrients and supplements (N=161) | Knowledge | 105 (65.2) | Social and professional role and identity, and emotion | 2 (1.2) |
Food overconsumption (N=83) | Environmental context and resources | 55 (66.3) | Social and professional role and identity | 1 (1.2) |
Food tips and recommendations (N=361) | Skills | 258 (71.5) | Intentions | 8 (2.2) |
Food changes (N=322) | Environmental context and resources | 232 (72.1) | Social and professional role and identity | 9 (2.8) |
Body appearance (N=153) | Environmental context and resources | 69 (45.1) | Social and professional role and identity | 3 (2.0) |
Diets and dietary patterns (N=533) | Environmental context and resources | 326 (61.2) | Social and professional role and identity, and memory, attention, and decision processes | 4 (0.8) |
Other lifestyle habits (N=712) | Behavioral regulation | 389 (54.6) | Social and professional role and identity | 15 (2.1) |
Grocery (N=339) | Skills | 205 (60.5) | Social influences | 7 (2.1) |
Health care system (N=232) | Knowledge | 101 (43.5) | Social influences | 4 (1.7) |
This study found differences between dietitians’ tweets and the public’s tweets about the themes they discuss, the engagement they received from users, the TDF domains they used, and their content accuracy.
Differences about more frequently discussed themes were found between groups. Grocery was the most addressed theme by dietitians. Immune health, food support and food system, food tips and recommendations, grocery, and health care system were also more frequent in this group than in the public group. Conversely, the public group was mostly interested in discussing diets and dietary patterns, while weight loss, specific foods, alcohol consumption, food overconsumption, diets and dietary patterns, and other lifestyle habits emerged as more salient themes in this group than in the dietitian group.
Indeed, concerns have been raised by the population over grocery store safety practices, grocery bills, and an altered food supply [
Moreover, as could be expected, thematic analyses between waves demonstrated that most of the discussions on nutrition and COVID-19 took place during the first wave, but more so in the case of dietitians. These results are supported by other studies. For instance, between January and October 2020, Google Search trends about COVID-19 and wine, ginger, 5G network spread, and the sun generally peaked in March and April 2020 [
In addition, contrary to our expectations, no general thematic popularity was revealed across the 3 types of user engagement reactions, as only the number of likes differed between groups according to the theme. As opposed to the study by Hand et al [
Contrary to other studies that have used the TDF to analyze specific aspects of nutrition or COVID-19, the model served a different purpose in this paper, as multiple nutrition and COVID-19–related behaviors were evaluated in tweets. Hence, all domains were addressed, suggesting that tweets could potentially contribute to behavior change. Additionally, differences were found between groups. However, in general, literature on the TDF mostly addressed the facilitators and barriers to the implementation of various behaviors by specific groups, which differs from how it was used in this study and renders the group comparison difficult. For instance, research on COVID-19 vaccine uptake has shown that themes related to the TDF domains of knowledge, beliefs about consequences, environmental context and resources, social influence, and emotion explain hesitancy [
Furthermore, a high proportion of tweets were considered not applicable for accuracy evaluation, which could be explained by the fact that Twitter is a means “to share quickly where one is, and what one is doing, thinking, or feeling” [
Content accuracy results support the dietitians’ role in sharing reliable information on nutrition during a pandemic. Health and governmental agencies should make use of their valuable expertise during health crises, namely by identifying and allying with dietitians who are present and active on social media. This collaboration could also result in more sustained engagement not only in the COVID-19 and nutrition discourse on Twitter but also in other nutrition-related situations and conditions on the part of dietitians.
Moreover, differences in themes addressed by groups, engagement in the form of likes, and theme inaccuracy shed light on the themes that should be prioritized, further discussed, and made more engaging by dietitians to counter the potentially inaccurate tweets of the public. For instance, other lifestyle habits were more interesting to readers when addressed by the public, while weight loss had more inaccurate than accurate tweets. Characterizing the conversation on nutrition and COVID-19 is equally necessary to bring other health professionals to help dietitians in their work toward reducing misinformation and disinformation on Twitter.
Likewise, knowing the behavior change factors employed by each group helps in orienting social media interventions aiming at the adoption of favorable pandemic-related practices. It does so by prioritizing behavior change techniques associated with the most popular determinants (eg, skills), by further integrating ones that tend to be less used or ones recognized as facilitators and barriers of similar behaviors, and by considering the fact that a pandemic acts as a socioenvironmental factor that largely influences behavior.
Lastly, comparison of the frequency of tweets between waves demonstrated that most of the conversation on COVID-19 and nutrition happened during the first few months of the pandemic. Thus, efforts should be made early to counter misinformation and disinformation. Without giving support to a piece of false information, it becomes important to correct it as soon as it starts to spread widely [
This study is not without limitations. First, although the methodology used to collect and validate tweets was rigorous, some of the keywords and hashtags were not specific to COVID-19 or nutrition, but were only related to it (eg, mask, disinfectant, and health). This resulted in a data collection that was possibly very sensitive but not specific enough. However, during coding, tweets were manually filtered to only keep those pertaining to the research theme. Hence, a lesser number of COVID-19 and nutrition-specific words should have been used to collect tweets. A keywords list should indeed be reviewed iteratively before initiating data collection [
This study sheds light on the information sharing behaviors of RDs from Canada and the United States, and Twitter users in the COVID-19 and nutrition infodemic on Twitter. Differences were found in discussed themes, use of TDF domains, content accuracy, and generated user engagement. Studies and results like these are needed to support the role of practical, timely, and theory-informed social media interventions led by dietitians, as well as other health professionals specialized in their respective fields, for encouraging sound and evidence-based pandemic-related practices and behaviors.
registered dietitian
Theoretical Domains Framework
Virginie Drolet-Labelle, RD, who is a candidate of the Master of Nutrition at the School of Nutrition, Université Laval, helped with dietitian Twitter account identification. Alexandra Bédard, PhD, RD, who is a research professional at the Institute of Nutrition and Functional Foods, and Centre Nutrition, santé et société, Université Laval, assisted with statistical analyses. This project was supported financially by a grant and a scholarship from the Centre Nutrition, santé et société (NUTRISS), as well as a scholarship from the Fonds de nutrition publique de l’Université Laval.
None declared.