Using photos for public health communication: A computational analysis of the Centers for Disease Control and Prevention Instagram photos and public responses

Using an Instagram campaign to influence knowledge, subjective norms, perceived behavioral control, and behavioral intentions for sustainable behaviors

Introduction

Climate change poses one of the most pervasive threats to the planet today. Intervention is required to promote pro-environmental behaviors among individuals to curb its effects. Borrowing several constructs from the Theory of Planned Behavior, we designed and evaluated a campaign, delivered primarily through Instagram, to shift sustainability-related cognitions and behaviors among university students.

Methods

An online survey was distributed to undergraduate students at a Northern California university and collected responses from 1,552 participants.

Results

Comparing students who self-reported exposure to the campaign with those who were not exposed, students who observed the campaign materials had more knowledge about (p < .001), greater perceived social norms about (p < .001), and greater intentions to perform sustainable behaviors (p < .001). There were also increases in sustainable behaviors during the campaign, compared to the previous academic term.

Discussion

Implementing campaigns inspired by behavior change theories may be one viable strategy to increase individual sustainable behaviors for climate change mitigation.

#SharingHEOR: Developing Modern Media for Communication and Dissemination of Health Economics and Outcomes Research

Questions regarding the effectiveness and safety of health interventions and allocation of health care resources are frequently discussed in mainstream and social media. Additionally, government and foundation funders are increasingly mandating that results be disseminated to the lay public and patients may benefit from being able to digest scientific research regarding their health conditions. Therefore, it is important to widely disseminate and clearly communication health economics and outcomes research (HEOR) findings to a range of interested parties. Digital media features such as graphical or visual abstracts, infographics and videos are informative and add value to research articles by improving reader engagement with articles, potentially increasing their impact, and allowing results to be more widely disseminated. However, use of novel digital media for research dissemination has been relatively limited to date. In this article, we discuss the rationale for developing novel media to communicate and disseminate research findings and offer practical advice for doing so. We conclude by outlining a future agenda for research regarding HEOR communication and dissemination.

Understanding Mental Health Organizations' Instagram Through Visuals: A Content Analysis

This study analyzed the content, visual features, and audience engagement data of Instagram posts from two mental health organizations over one year (N = 725). For content features, mental health literacy and communicative strategies were examined. Posts that promoted knowledge of mental disorders and treatments, used information and community strategy were more likely to receive higher audience engagement. Visual features of demographic segments, visual composition, and visual framing topics were analyzed. Images that covered an unspecific population, used illustrated images, and focused on anti-stigma topical frames obtained more engagement. Theoretical contributions and practical applications regarding visual message design and management on social media to promote mental health are also offered.

A Model of Social Media Effects in Public Health Communication Campaigns: Systematic Review

BACKGROUND

Social media platforms are frequently used in health communication campaigns. Common understandings of campaign effects posit a sequential and linear series of steps from exposure to behavior change, commonly known as the hierarchy of effects model (HOE). These concepts need to be reevaluated in the age of social media, which are interactional and communal.

OBJECTIVE

This review aims to update the traditional HOE for health communication campaigns in the context of social media, including identifying indicators of effectiveness and how these are conceptualized to lead to health-related outcomes.

METHODS

We conducted a systematic review of studies following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines reporting on the use of social media as part of health communication campaigns, extracting campaign information such as objectives, platforms used, and measures of campaign performance. We used these data, combined with our understanding of the HOE, to develop an updated conceptual model of social media campaign effects.

RESULTS

We identified 99 eligible studies reporting on 93 campaigns, published between 2012 and 2022. The campaigns were conducted in over 20 countries, but nearly half (n=42) were conducted in the United States. Campaigns targeted a variety of health issues and predominantly used Facebook, Twitter, Instagram, and YouTube. Most campaigns (n=81) set objectives targeting awareness or individual behavior change. Process measures (n=68; eg, reach and impressions) and engagement measures (n=73; eg, likes and retweets) were reported most frequently, while two-fifths (n=42) did not report any outcomes beyond engagement, such as changes in knowledge, behavior, or social norms. Most campaigns (n=55) collected measures that did not allow them to determine if the campaign objective had been met; that is, they were process evaluations only. Based on our review, our updated model suggests that campaign exposure can lead to individual behavior change and improved health outcomes, either through a direct or indirect pathway. Indirect pathways include exposure through social and policy changes. "Engagement" is positioned as critical to success, replacing awareness in the traditional HOE, and all types of engagement are treated as equal and good. No consideration is being given to potential negative engagement, such as the distribution of misinformation. Additionally, the process is no longer linear and sequential, with circular pathways evident, such as engagement not only influencing behavior change but also generating additional exposure to campaign messages.

CONCLUSIONS

Our review has highlighted a change in conventional understandings of how campaigns can influence health outcomes in the age of social media. The updated model we propose provides social media campaigners with a starting point to develop and tailor campaign messages and allows evaluators to identify critical assumptions to test, including the role and value of "engagement."

TRIAL REGISTRATION

PROSPERO CRD42021287257; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=287257.

Personality of nonprofit organizations’ Instagram accounts and its relationship with their photos’ characteristics at content and pixel levels

Nonprofit organizations (NPO) can utilize social networking sites (SNSs) for their activities. Like individual users, they can create SNS accounts, upload posts to show what they are doing, and communicate with other users. Thus, their accounts can be investigated from the same perspective of personality which has been one of the key lenses through which SNS posts of individual users was investigated. In the line of literature that analyzed the personality of non-human objects such as products, stores, brands, and websites, the present research analyzed the personality of NPOs’ Instagram accounts using an online AI service. Also, it investigated how their personality traits were related to the characteristics of the uploaded photos at content and pixel levels. The results of analysis of 223,446 photos on 177 Instagram accounts suggested that the personality of NPOs’ Instagram accounts can be summarized as being high in openness and agreeableness but low in extraversion and neuroticism. And it was found that openness and agreeableness were the personality traits that associated the most with the photo features. Also, the personality traits of NPOs’ Instagram accounts, except neuroticism, were predicted from the photo features with an acceptable level of accuracy. Implications of this research and suggestions for further research were presented.

Analysis of Content, Social Networks, and Sentiment of Front-of-Pack Nutrition Labeling in the European Union on Twitter

In recent years, concerted political efforts have been made at the national and European Union (EU) level to promote the consumption of healthy foods. The European Commission (EC) expressed the need for a harmonized and mandatory front-of-pack nutrition labeling (FOPL) system at the EU level. The EC will adopt the proposal by the end of 2022. Our research work aims to understand the public discourse on FOPL in the EU via Twitter, by analyzing tweet content, sentiment, and mapping network characteristics. Tweet search and data collection were performed using the Twitter application programming interface (API), with no time or language restrictions. The content was coded with the QRS Nvivo software package and analyzed thematically. Automatic sentiment analysis was performed with QSR Nvivo, and network analysis was performed with Gephi 0.9.2. A total of 4,073 tweets were posted, mostly from the UK, Spain, and France. Themes that have emerged from the discussion on Twitter include the types of food labeling, food industry, healthy vs. unhealthy foods in the context of food labeling, EU regulation, political conflicts, and science and education. Nutri-Score dominated the discussion on Twitter. General topics were perceived negatively by Twitter users with more positive sentiments toward the food industry, while negative sentiments were observed toward the discourse of political conflicts. The network analysis showed that a centralized communication was hardly existed between countries. Our results reveal that the discussion of FOPL on Twitter is limited to a very limited group of people, and it seems necessary to inform a wide range of consumers about existing and upcoming FOPL schemes. Educational programs should empower consumers to understand what a healthy diet is and how it relates to FOPL, regardless of the existing labeling system.

Evaluating the effectiveness of Internet-based communication for public health: a systematic review (Preprint)

Background

Communicating strategically is a key issue for health organizations. Over the past decade, health care communication via social media and websites has generated a great deal of studies examining different realities of communication strategies. However, when it comes to systematic reviews, there is fragmentary evidence on this type of communication.

Objective

The aim of this systematic review was to summarize the evidence on web institutional health communication for public health authorities to evaluate possible aim-specific key points based on these existing studies.

Methods

Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, we conducted a comprehensive review across 2 electronic databases (PubMed and Web of Science) from January 1, 2011, to October 7, 2021, searching for studies investigating institutional health communication. In total, 2 independent researchers (AN and SS) reviewed the articles for inclusion, and the assessment of methodological quality was based on the Kmet appraisal checklist.

Results

A total of 78 articles were selected. Most studies (35/78, 45%) targeted health promotion and disease prevention, followed by crisis communication (24/78, 31%), general health (13/78, 17%), and misinformation correction and health promotion (6/78, 8%). Engagement and message framing were the most analyzed aspects. Few studies (14/78, 18%) focused on campaign effectiveness. Only 23% (18/78) of the studies had an experimental design. The Kmet evaluation was used to distinguish studies presenting a solid structure from lacking studies. In particular, considering the 0.75-point threshold, 36% (28/78) of the studies were excluded. Studies above this threshold were used to identify a series of aim-specific and medium-specific suggestions as the communication strategies used differed greatly.

Conclusions

Overall, the findings suggest that no single strategy works best in the case of web-based health care communication. The extreme variability of outcomes and the lack of a unitary measure for assessing the end points of a specific campaign or study lead us to reconsider the tools we use to evaluate the efficacy of web-based health communication.

Radiology on Instagram: Analysis of Public Accounts and Identified Areas for Content Creation

RATIONALE AND OBJECTIVES

Radiology social media research has focused on Twitter, Facebook, and YouTube, with minimal attention to Instagram, which may be a natural platform for engagement. We evaluated features of public radiology-related Instagram accounts.

MATERIALS AND METHODS

Instagram accounts were searched using the term "radiology." Two independent raters used a coding system developed through initial content review to categorize account user types and post aims over the study month (October 2019). Multivariate linear regressions were performed.

RESULTS

49 Instagram accounts and their 459 posts were retrieved. Users had median 117 total posts (interquartile range 43-203), 9 posts during the study month (1-16), 3079 followers (695-19,600), and 106 followed users (50-351). Most users were radiologists (41%, most commonly academic), radiology practices (12%, most commonly academic) and 10% radiology professional societies. The 459 posts had median 178 likes (interquartile range 51-353) and 2 comments (0-5); single videos had median 4693 views (2183-6080). 75% of posts were radiology images or videos (of these, 29% MRI, 28% CT). 65% of posts were clinical imaging cases (most commonly neuroradiology). Seven posts violated HIPAA regulations, all originating outside the U.S. When controlling for number of account followers, posts by radiology society (β = -130.6, p = 0.017) and practice accounts (β = -90.6, p = 0.049) had fewer post likes. An artwork post was the only significant predictor of greater post likes (β = +866.2, p < 0.001) and comments (β = +14.3, p < 0.001).

CONCLUSION

Instagram is commonly used by academic radiologists to present clinical imaging cases. Radiologists should consider opportunities for greater application of this unique image-based social media platform.

Understanding citizen engagement and concerns about the COVID-19 pandemic in China: a thematic analysis of government social media

Purpose

In crises such as the coronavirus disease 2019 (COVID-19) pandemic, governments need to act in time to lead citizens toward rational reactions and disclose information effectively to the public. This paper aims to understand the content published by the government and identify how citizen engagement relates to content type and emotional valence.

Design/methodology/approach

The grounded theory approach was adopted and nine types of content posted by the government were observed. The data were obtained from “People's Daily”, an official Sina Weibo account representing the voice of the Chinese government, from January 3 to June 22 in 2020.

Findings

The analysis shows that information related to emotional support and social mobilization were the most reposted, while those mentioning immoral and illegal incidents were the most liked and commented. Also, it was found that positive posts tend to attract more likes, yet with fewer reposts than neutral posts.

Originality/value

The authors adopted thematic analysis and focused on the impact of post content and valence on user participation behavior. This study expands the existing literature. The government can improve crises management capability by learning about citizen engagement behaviors on social media.

Public health agencies outreach through Instagram during the COVID-19 pandemic: Crisis and Emergency Risk Communication perspective

BACKGROUND

Governmental and non-governmental institutions increasingly use social media as a strategic tool for public outreach. Global spread, promptness, and dialogic potentials make these platforms ideal for public health monitoring and emergency communication in crises such as COVID-19.

OBJECTIVE

Drawing on the Crisis and Emergency Risk Communication framework, we sought to examine how leading health organizations use Instagram for communicating and engaging during the COVID-19 pandemic.

METHODS

We manually retrieved Instagram posts together with relevant metadata of four health organizations (WHO, CDC, IFRC, and NHS) shared between January 1, 2020, and April 30, 2020. Two coders manually coded the analytical sample of 269 posts related to COVID-19 on dimensions including content theme, gender depiction, person portrayal, and image type. We further analyzed engagement indices associated with the coded dimensions.

RESULTS

The CDC and WHO were the most active of all the assessed organizations with respect to the number of posts, reach, and engagement indices. Most of the posts were about personal preventive measures and mitigation, general advisory and vigilance, and showing gratitude and resilience. An overwhelming level of engagement was observed for posts representing celebrity, clarification, and infographics.

CONCLUSIONS

Instagram can be an effective tool for health organizations to convey their messages during crisis communication, notably through celebrity involvement, clarification posts, and the use of infographics. There is much opportunity to strengthen the role of health organizations in countering misinformation on social media by providing accurate information, directing users to credible sources, and serving as a fact-check for false information.

Mining Textual and Imagery Instagram Data during the COVID-19 Pandemic

Instagram is perhaps the most rapidly gaining in popularity of photo and video sharing social networking applications. It has been widely adopted by both end-users and organizations, posting their personal experiences or expressing their opinion during significant events and periods of crises, such as the ongoing COVID-19 pandemic and the search for effective vaccine treatment. We identify the three major companies involved in vaccine research and extract their Instagram posts, after vaccination has started, as well as users’ reception using respective hashtags, constructing the datasets. Statistical differences regarding the companies are initially presented, on textual, as well as visual features, i.e., image classification by transfer learning. Appropriate preprocessing of English language posts and content analysis is subsequently performed, by automatically annotating the posts as one of four intent classes, thus facilitating the training of nine classifiers for a potential application capable of predicting user’s intent. By designing and carrying out a controlled experiment we validate that the resulted algorithms’ accuracy ranking is significant, identifying the two best performing algorithms; this is further improved by ensemble techniques. Finally, polarity analysis on users’ posts, leveraging a convolutional neural network, reveals a rather neutral to negative sentiment, with highly polarized user posts’ distributions.

Personality of Public Health Organizations' Instagram Accounts and According Differences in Photos at Content and Pixel Levels

Organizations maintain social media accounts and upload posts to show their activities and communicate with the public, as individual users do. Thus, organizations’ social media accounts can be examined from the same perspective of that of individual users’ accounts, with personality being one of the perspectives. In line with previous studies that analyzed the personality of non-human objects such as products, stores, brands, and websites, this study analyzed the personality of Instagram accounts of public health organizations. It also extracted features at content and pixel levels from the photos uploaded on the organizations’ accounts and examined how they were related to the personality traits of the accounts. The results suggested that the personality of public health organizations can be summarized as being high in openness and agreeableness but lower in extraversion and neuroticism. Openness and agreeableness were the personality traits associated the most with the content-level features, while extraversion and neuroticism were the ones associated the most with the pixel-level features. In addition, for each of the two traits associated the most with either the content- or pixel- level features, their associations tended to be in opposite directions with one another. The personality traits, except for neuroticism, were predicted from the photo features with an acceptable level of accuracy.

Vaping and Instagram: A Content Analysis of e-Cigarette Posts Using the Content Appealing to Youth (CAY) Index

The promotion of flavors, perceptions of "coolness," and general curiosity are characteristics of electronic nicotine delivery systems (ENDS) that have appealed to young adults. However, little is known about the characteristics of popular social media posts related to ENDS on the social media network, Instagram. Methods: Content analysis was performed using the Content Appealing to Youth (CAY) index. Over 700 posts were collected from August 2019 - December 2019 by searching the Instagram hashtags, #vape and #vapelife. Frequencies and percentages were calculated for each of the six major categories and 35 sub-categories. Results: Nearly all of the images were color photographs and 84% featured an ENDS device (mod) as the focal point. The style of the device was often matte (75%) in only one or two main colors (55%). Warnings about age restrictions and nicotine were included in 28% of images, but commonly used promotional tactics, such as humor, presence of vapor puffs, and flavors were rarely utilized. Conclusions: Instagram posts featuring ENDS are visually appealing and like cigarette packaging, may have the capacity to influence perceptions about the product. Since it is culturally normative for appealing images to be shared on Instagram, greater attention should be placed on media literacy skills to educate young adults about ENDS viewed on social media.

Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach

Background

It is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring.

Objective

The objective of this study is to examine COVID-19–related discussions, concerns, and sentiments using tweets posted by Twitter users.

Methods

We analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, “coronavirus,” “COVID-19,” “quarantine”) from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets.

Results

Popular unigrams included “virus,” “lockdown,” and “quarantine.” Popular bigrams included “COVID-19,” “stay home,” “corona virus,” “social distancing,” and “new cases.” We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics.

Conclusions

This study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.

#Antivaccination on Instagram: A Computational Analysis of Hashtag Activism through Photos and Public Responses

A dramatic increase has been registered in the number of social media posts in photo form as well as in hashtag activism. Hashtags, which manifest thoughts and feelings clearly and concisely, originated on Twitter, where the length of a post is limited; their use, however, has expanded into other social media services, including Instagram. Hashtags, which make it easy to find and express support for posts of interest, have been widely used for online activism, although they have been criticized for fostering confirmation bias. Moreover, hashtag activism in photo form has been relatively understudied. This research analyzed Instagram photos with antivaccination hashtags as an example of hashtag activism through photos. In addition, we examined how the photo features were related to public response, which was manifested via engagement and comment sentiment. The results suggest that the photos which were categorized into “text” took the largest share. It was also found that the major way of claiming was to imprint key messages that persuade people not to vaccinate with remarks from professionals on photos and provide a source of supporting information in the post text with hashtags of antivaccine intention. Various photo features showed associations with engagement and comment sentiment, but the directions of correlation were usually the opposite: these results suggest that engagement and comment sentiment are separate domains that reveal different public responses.

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter

The study aims to understand Twitter users’ discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including “updates about confirmed cases,” “COVID-19 related death,” “cases outside China (worldwide),” “COVID-19 outbreak in South Korea,” “early signs of the outbreak in New York,” “Diamond Princess cruise,” “economic impact,” “Preventive measures,” “authorities,” and “supply chain.” Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.