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Research Article

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

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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.


Mental health problems have been a global concern, particularly in the past three years, during which the mental well-being of the whole society was severely impacted by the pandemic (NAMI, Citation2019; World Health Organization [WHO], Citation2022). While the statistics about mental illness were already stark prior to the advent of COVID-19, the pandemic has exposed the public to unprecedented turbulence and health concerns, leading to a significant increase in depression and anxiety (Chavira et al., Citation2022; WHO, Citation2022). To address this challenge, the United Nations issued policy briefs that discussed the need for action on mental health (United Nations, Citation2020). In addition, the World Health Organization updated the comprehensive action plan that aimed to promote, prevent, and treat mental-health issues (WHO, Citation2013, Citation2021).

Government and health organizations have explored different strategies for promoting health-related knowledge and practices on social media. For example, CDC issued a toolkit (Centers for Decease Control and Prevention [CDC], Citation2021) to offer specific guidelines for health communication on text-based platforms such as Facebook and Twitter; however, how to promote health using visuals, which have become the most popular message format on social media (Adami & Jewitt, Citation2016), has not been clearly suggested. It is evident that different types of visuals, such as photos or cartoons, can improve reception of health information related to attention, comprehension, recall, and behavior (Guillén et al., Citation2012). In recent years, researchers and practitioners have paid increasing attention to understand how visual content can promote health information on social media due to its unique advantages compared to text-only-based information (King & Lazard, Citation2020; McCosker & Gerrard, Citation2021). Previous studies explored mental health communication on Instagram, one of the fastest growing visual-oriented social media platforms (“Instagram Demographics in 2022,” Citation2022). These studies analyzed discussions around depression (McCosker & Gerrard, Citation2021) and antidepressants (Gupta & Ariefdjohan, Citation2021) on Instagram, examined Instagram content using mental health hashtags (#mentalhealth; N. Lee et al., Citation2020), as well as artwork promoting mental health on Instagram (Griffith et al., Citation2021), among others. While they provide valuable insights of how various mental health-related topics are presented, discussed, and circulated visually by the public, less is known about how mental health is promoted visually by key nonprofit organizations. When experiencing a health crisis, individuals are usually motivated to search for information from credible sources. According to the latest report from the Edelman Trust Barometer, despite the widespread declines in trust in societal institutions and information sources during the pandemic, NGOs still ranked second among most trusted institutions with various publics (Edelman, Citation2022). Therefore, the practices of mental-health organizations on Instagram merits scholarly attention due to their perceived credibility and their mission to promote mental wellness through education and advocacy (Ju et al., Citation2021).

The goal of this study is two-fold: first, it examines how mental health is communicated by kay organizations on Instagram focusing on content and visual features; and second, it explores what message content and visual features relate to audience engagement, in terms of likes and comments. As a result, this study expands the current theoretical framework of health promotion on social media from the organizational perspective and provides evidence-based suggestions to effectively promote mental health using visuals.

Literature review

According to Nan et al. (Citation2022), the communication strategies of effective public health messaging involve two critical components: message content and message executions. Message content focuses on what to say and usually targets the outcomes of health behaviors and peoples’ ability to perform such behaviors. Message executions, or how the information is delivered, emphasizes the stylistic features of the messages, including the use of various visuals to convey the message. Guided by this framework, this study examines both the content and executive (visual) features of mental-health messages on organizations’ Instagram. For message content, messages designed to increase mental health literacy and communicative strategies will be covered. Although this is not a comprehensive list of the content features, we focus on these particular variables because of mental-health organizations’ mission to educate and engage the public. For message execution, a four-tiered model of visual frames will be used (Rodriguez & Dimitrova, Citation2011).

Content features: Messages to increase mental health literacy and communicative content strategies

Messages designed to increase mental health literacy (MHL)

One key content that is highly relevant to mental health communication is mental health literacy (MHL), which is defined as “knowledge and beliefs about mental disorders which aid their recognition, management or prevention” (Jorm et al., Citation1997, p. 182). In the past two decades, MHL has received increasing scholarly attention and has been widely discussed as a strong correlate of beneficial outcomes (e.g., Furnham & Swami, Citation2018). When MHL is improved, individuals exhibit more positive attitudes toward seeking professional help (Cheng et al., Citation2018; H. C. Kim, Citation2021), lower self-stigma (Crowe et al., Citation2018), and a higher level of self-efficacy (Beasley et al., Citation2020), among others. Informed by the previous literature, recent studies have developed a comprehensive approach to conceptualize MHL. That is, it not only includes knowledge of mental illness, but also a social commitment to reducing stigma and improving help-seeking behaviors (Kutcher et al., Citation2016). Following this line of research (Wei et al., Citation2015, Citation2019), the present study employs this multifaceted definition of MHL, which contains four components: first, to understand how to obtain and maintain good mental health; second, to identify mental disorders and their treatments; third, to decrease the stigma against mental illness; and, finally, to enhance one’s help-seeking efficacy that refers to gaining knowledge of how and where to find help (Kutcher et al., Citation2016).

Due to the benefits of improving MHL for the public, studying how social media can play a role in promoting MHL merits attention. As Jorm (Citation2015) argued, the introduction of this concept has brought a desirable impact on policy change and intervention development (also see Tay et al., Citation2018). As professional interventions and mental health related courses are limited and often unavailable to the public, social media can be a practical tool for promoting MHL education and reaching large audiences. However, very little is known about what topical categories concerning MHL are promoted on social media. Therefore, we ask:

RQ 1:

To what extent do mental health organizations attempt to increase public mental health literacy on Instagram?

Communicative content strategies

As an engaging method by organizational social media to develop and maintain good relationships with their stakeholders (Saxton & Waters, Citation2014), communicative content strategies are utilized to help achieve different organizational goals, such as mental health advocacy and promotion (Huang et al., Citation2016). Among frameworks that explain organizational use of various social media content (e.g., Men & Tsai, Citation2012; Yang & Kent, Citation2014), Lovejoy and Saxton’s (Citation2012) typology of information, community and actions is the most relevant to this study because it specifically highlights the communicative functions of the messages by nonprofit organizations. In this framework, information content covers social media messages about the organization, its activities, and facts relevant to the stakeholders; community content refers to the interactive messages to engage with and strengthen ties to the public; lastly, action content mainly focuses on getting followers to do things such as to repost, attend an event or to donate (Lovejoy & Saxton, Citation2012).

For mental health-related organizations, information content can be any message that introduces mental illnesses and treatments, educates the public about mental health care, or spreads information about the organization. This content is important to the audiences as useful information can offer specific strategies or resources on how to perform a healthy behavior to achieve health-related goals (H. S. Kim, Citation2015). Community content aims to enhance relationships, build networks and promote dialogue, thus, forming a supportive online community for stakeholders who are interested or impacted by mental health issues. Finally, action is about messages that urge specific behaviors from followers, such as joining an event to raise mental health awareness, donating to the organization to support its efforts on mental health promotion, or buying a related product such as a mental health awareness bracelet, among others. Previous literature has examined how these strategies were used to achieve organizational goals in various nonprofit contexts, such as political lobbying and advocacy (Chalmers & Shotton, Citation2016), building community in college classrooms (Friess & Lam, Citation2018), and nonprofit organizations advocacy online (Seelig et al., Citation2019). This study is interested in how these strategies are practiced by organizations promoting mental health:

RQ 2:

To what extent do mental health organizations employ information, community, and action content strategies on Instagram?

Visual features: Four-tiered model of visual frames

Emerging scholarship has explored visual communication on social media regarding various health-related topics (Nobles et al., Citation2020) including mental health or mental illness communication (Feuston & Piper, Citation2018, Citation2019; Griffith et al., Citation2021; N. Lee et al., Citation2020). Employing human or computational analysis, previous studies examined various features of images, including the image composition (e.g., photographic or illustrative, infographic or novel visual), objects and people presented (e.g., human face or body, social groups, scenery, food, drink, and animals), pixel features, and topic or meaning of the visuals (Kearney et al., Citation2019; Y. Kim & Kim, Citation2020). Despite the diversity of visual features analyzed in various studies, a systematic, theoretical investigation of visual images of health communication is lacking, making it difficult to compare the results and generate replicable and design-specific recommendations (King & Lazard, Citation2020).

To develop a more systematic investigation of visual messages, this study adopted the visual framing theory, a four-tiered model of visual frames as the analytical framework to examine visuals (Rodriguez & Dimitrova, Citation2011). We argue that with its four-tier system, this model can cover most of the image features analyzed in previous studies and categorize different image features into different levels for analysis. As a result, a systematic investigation of visuals can be generated.

The four-tiered model of visual frames suggests four levels to analyze visual images: denotative, stylistic-semiotic, connotative and ideological (Rodriguez & Dimitrova, Citation2011). The first three levels were applied to our analysis in view of their relevance. First, the denotative level is concerned literally with who or what is present in the visual messages. Health communication literature has long recognized the value of tailoring or targeting a variety of demographic constructs in health-related messages to increase their effectiveness (Pope et al., Citation2018). In relation to mental health, two demographic factors are worth noting: ethnicity and gender. Ethnic minorities suffer from mental health for various reasons. For example, African American adults who live in poverty are more likely to report serious psychological distress due to socioeconomic challenges, demonstrating the need to segment a population to achieve effective health outcomes (“Mental and Behavioral Health―African Americans,” Citation2021). The stigma of mental illness is prevalent in Asian cultures, making this population reluctant to seek help (Wynaden et al., Citation2005). Even worse, across all racial categories in the U.S., the indigenous population has the highest rate of suicide (Ivey-Stephenson et al., Citation2017). In terms of gender, men and women are vulnerable to different types of mental illness: women have higher prevalence rates of anxiety and depression, while men are more likely to be diagnosed with substance abuse or antisocial disorders (Eaton et al., Citation2012). Therefore, mental health promotion and intervention messages should be tailored to those populations’ needs and preferences (Pirkis et al., Citation2019), and the first step is to present populations at risk in the message (Lama et al., Citation2018). Accordingly, to examine the visual component of mental health Instagram posts at the denotative level, this study asks:

RQ 3:

How are demographic segments (gender and ethnicity) presented on mental health organizations’ Instagram posts?

Second, the stylistic-semiotic level examines pictorial variables, which may include but are not limited to camera position, actions or poses, tonal shades, and color (Rodriguez & Dimitrova, Citation2011). Visual persuasion theory (Messaris, Citation1997) explores how visual messages influence attitudes and behaviors of individuals. Generally, visual imagery can be categorized as photographic, presenting reality through people, places and things; or illustrated, displaying shared meaning through symbols such as logos, signs, and cartoons (King, Citation2015, Citation2016). Illustrated images may also involve an infographic, a visual representation of data highly effective in health education (Martin et al., Citation2019). These two types of composition may be used based on different purposes. Photos include real people, objectives, events or sceneries or a combination of them, and can be used to present a vivid picture of reality. Hence, demonstrate the severity or urgency of the health problem (Gibson & Zillmann, Citation2000; Yoo, Citation2016). Illustrations use symbols to present an abstract representation of the reality and are used to convey comprehensive or abstract health related information (King, Citation2015), thus, provide a macro-level understanding of the health issues. Considering the differences between these two types of visual imagery and the different effects of each category, the following question is proposed:

RQ 4:

How are visual compositions presented on mental health organizations’ Instagram posts?

Finally, the connotative level asks more generally and associatively about the concepts attached to visuals. In other words, this level of analysis concerns the “social meaning” (Rodriguez & Dimitrova, Citation2011, p. 56) as presented through visual symbols. Following this logic, this study operationalizes its social meaning as major topics that are presented in pictures. Using the framing approach (Entman, Citation1993), which is concerned about how something is presented to the audience, previous studies have analyzed textual mental health messages on social media and suggested that awareness, individual feelings, and anti-stigma were the three most prevalent framing topics (Ju et al., Citation2021; Pavlova & Berkers, Citation2022). However, how the framing topics are used in visual communication of mental health is yet to be explored, hence, on the connotative analysis level, we ask:

RQ 5:

What are the visual framing topics presented on mental health organizations’ Instagram posts?

Audience engagement with mental health information

Social media allows users to engage with the information through such functions as likes/reactions, reposts/shares and comments. Audience engagement can facilitate the consumption and dissemination of the information, therefore, widen the distribution of the message for organizations, because social media algorithms actively promote messages with high involvement (e.g., high numbers of likes; Luarn et al., Citation2015). In addition, audience engagement can drive positive behavioral outcomes such as advocacy or word-of-mouth in real life (Alhabash & McAlister, Citation2015; Men & Tsai, Citation2014), and further expand the effect of health promotion on social media. It is important for organizations to understand how to properly engage the audience with their social media messages. Focusing on content and visual elements of messages, this study explores:

RQ 6:

To what extent is audience engagement associated with mental health literacy, communicative content strategies, and three levels of visual features?


A content analysis was performed to answer the research questions. Following the procedure of generating a more generalizable sample organizations (Ju et al., Citation2021), we mocked the process that people use to search for mental-health organizations online. First, the phrase “mental health organizations in the US” was entered on google and a results page was accessed. The results showed four links before the “people also ask” box, indicating the high relevance of these links to the search phrase. These four links were “Top 20 US mental health organizations,” “National mental health organizations,” “11 organizations to support [on] mental health,” and “Leading mental health charities and organizations.” We then selected our sample organizations based on the following two criteria: First, as our goal is to analyze messages of organizations that promote mental health to the general public, the following types of organizations were excluded: 1) federal research agencies, whose mission is to promote research and policymaking, such as the National Institute of Mental Health; 2) organizations that specialize in a particular type of mental illness, such as PTSD; 3) organizations that attempt to solve related problems, such as mental health and addictions; and 4) organizations with a specific target audience like college students, LGBTQ or children. Applying these criteria of exclusion to the recommendations of the web content from the Google search, three organizations remained: Mental Health America (MHA), the National Alliance on Mental Illness (NAMI), and the Flawless Foundation (Flawless). The second criteria was popularity. Compared to the number of followers of MHA (n = 237,000) and NAMI (n = 277,000), Flawless Foundation (n = 6329) was excluded from the analysis due to its lower numbers of followers.

The Selenium Framework, an open-source automation tool for data collection (Munzert et al., Citation2014), was used to download all original posts from these organizations’ Instagram accounts, along with the numbers of associated likes and comments from August 1 2020, to July 31 2021. The two organizations together posted 831 messages within that time-period. After excluding the videos (N = 106), the final sample data comprised of 725 messages (MHA = 592, NAMI = 133).

Codebook development and coding scheme

A codebook consisting of three parts, content features, visual features and audience engagement, was developed for analysis (see Appendix). The content component analyzed the whole Instagram post, including caption and image and covered two variables. The first variable examined the communicative content strategy, which included 1) information, 2) community, and 3) action (Lovejoy & Saxton, Citation2012). Posts were first coded into one of the three categories and then into one sub-category (only two of the three categories – community and action – had subcategories. For details, see ). The second variable addressed messages designed to increase mental health literacy. Posts were coded into one or a combination of the following categories: 1) N/A; 2) maintaining good mental health, such as promoting a healthy lifestyle; 3) mental disorders and treatments, such as introducing facts and symptoms of anxiety; 4) anti-stigma; and 5) enhancing help-seeking efficacy, which informs the public how to seek detailed information or find professional help (Kutcher et al., Citation2016).

Table 1. Frequency distributions of categories.

The second component analyzed the visual part of the message only, and focused on three levels of visual analysis of the posts. First, the denotative level examined who is covered in the image, in terms of gender and ethnicity. Each image was coded as having a presence of the following gender and ethnicity or not: female and male, Caucasian and non-Caucasian, using a dichotomous coding (0=non-presence, 1=presence). For a visual containing multiple people, all the applied coding categories were chosen. Second, the stylistic-semiotic level asked what form of visuals was present. Each image was coded into one of the five visual compositions: 1) photo, 2) comic/cartoon, 3) infographic, 4) words on pictures (with no design; e.g., words on plain background) and 5) words on pictures (with design, such as posters). This category also covered the number of visuals presented in each post. The last category concerned the connotative meaning of visuals, and each post was coded into one of the visual framing topics, developed inductively from the data and deductively from previous studies (Pavlova & Berkers, Citation2022). Finally, public engagement only recorded the number of likes and comments on each post because repost data were not available from Instagram.

Intercoder reliability

Following the suggestions of Lacy et al. (Citation2015), the first two authors of the study practiced with “similar non-study content” (p. 804), which were the Instagram posts from these two organizations published prior to August 2020, until intercoder reliability was established. After two rounds of tests, with each round containing 140 (equals to 19.3% of the study sample) of messages that were not included in the final study sample, all items achieved an acceptable level of intercoder reliability using Krippendorff’s Alpha, from 0.74 to 0.87, with an average of 0.80 (Krippendorff, Citation2018; see for reliability statistics for each coding category). Due to the limited variance, the intercoder reliability of denotative level of coding (sex and ethnicity) and number of pictures were tested using percentages of agreement; both items reached 100% agreement. After the intercoder reliability was established, the two coders split the study sample data and coded independently.


Frequencies of content and visual strategies

Data were aggregated and submitted to the statistical analyses using R version 4.1.1 for descriptive analysis (https://www.r-project.org). The frequencies and percentages of each variable are reported in .

RQ 1 asked what mental health literacy was promoted on Instagram. The results showed that more than a half of the messages (n = 384, 53%) included at least one type of MHL information. Among the posts that cover only one type, 7.6% (n = 55) posts focused on obtaining and maintaining good mental health, 1.5% (n = 11) introduced disorders and treatments, 3.7% (n = 27) sought to decrease stigma against mental illness, 18.6% (n = 135) were to enhance help-seeking efficacy by offering another link that specifies where, from whom, and how to seek relevant information for details. The rest of the posts were a combination of different categories.

Regarding content strategies (RQ 2), less than half (n = 334, 46%) of the post focused on sharing information of mental health facts and knowledge, 28% (n = 204) attempted to build a community with the stakeholders, and 26% (n = 187) aimed to call for an action. For frequencies of specific categories of each strategy, see .

Regarding the demographic segments (RQ3) in the pictures, most of the visuals did not depict a specific gender, and among those which did, the visual representations of men (n = 140, 19.3%) and women (n = 167, 23%) were more evenly distributed among all posts. In terms of ethnicity, most of the images did not feature a specific ethnicity and among those which did, racial minorities (n = 141, 19.4%) were covered more than Caucasians (n = 94, 13%).

RQ 4 asked how visuals were presented. Most posts included one picture (n = 629, 86.8%) and the rest had more than one. In terms of image composition, around 90% (n = 652) were illustrated pictures and 10% (n = 73) were photographic. Among illustrated images, more than a half (n = 394, 60.4%) of the posts were words on pictures with a background design (such as posters), 20.9% (n = 136) were words on plain backgrounds, 15.2% (n = 99) were comics or cartoons, and 3.5% were infographics (charts or graphs; n = 23).

Concerning how mental health organizations covered visual framing topics (RQ 5), the most frequently depicted topics were awareness and advocacy (n = 187, 25.8%), event/product/personnel information (n = 168, 23.2%), and mental health care (n = 111, 15.3%).

Predictors of audience engagement

To answer RQ 6, the statistical significance was set to an alpha level of .05 for all the analyses. Regarding each of the categorical independent variables, two separate ANOVAs were employed to examine the differences in the number of likes and comments, respectively. Also, for each of these independent variables, categories with fewer than 10 Instagram posts were excluded from the ANOVA analyses to avoid extreme inequality in group sizes. Significant results were followed by post-hoc pairwise comparisons using Tukey adjustment, where applicable. The pictures related to stylist-semiotic visual strategies were treated as a continuous variable, and so, two separate linear regressions were employed to examine the prediction of likes and comments, respectively. presents the main effects of all the independent variables, the two ANOVAs and simple linear regressions.

Table 2. Main effects of the independent variables in the two ANOVAs and simple linear regressions.

Because all analyses are parametric tests and assume a normal distribution of the dependent variables (likes and comments), a Box-Cox power family of transformation was applied to likes and comments that are both positively skewed. Specifically, the maximum-likelihood estimation was used to optimize the tuning power parameter, λ, such that the distribution of the transformed data has the largest similarity to normality. When zero or negative values occur in the data, a second parameter, γ, was also estimated to reduce transformation bias and added to make all values positive before the data can be handled by the Box-Cox transformation (Box & Cox, Citation1964; Fox & Weisberg, Citation2019; Hawkins & Weisberg, Citation2017).

Mental health literacy

Generally, Instagram posts that involved at least one type of MHL (Mlikes = 7.35 ± 0.81, Mcomments = 2.65 ± 1.01) received more likes and comments than those that did not (Mlikes = 6.77 ± 1.04, Mcomments = 2.27 ± 1.29). Post-hoc comparisons revealed that “mental disorders and their treatments” (Mlikes = 7.87 ± 0.62, Mcomments = 3.35 ± 0.58), the combination of “mental disorders” and “enhancing help-seeking efficacy” (Mlikes = 7.60 ± 0.65, Mcomments = 2.95 ± 0.80) and “how to obtain and maintain good mental health” (Mlikes = 7.60 ± 0.75, Mcomments = 2.93 ± 0.81) had the most likes and comments.

Content strategies

Generally, Instagram posts that involved information (Mlikes = 7.36 ± 0.84[1], Mcomments = 2.68 ± 1.06) and community (Mlikes = 7.34 ± 0.93, Mcomments = 2.88 ± 1.10) received more likes and comments than those that involved action (Mlikes = 6.28 ± 0.79, Mcomments = 1.66 ± 1.02). Post-hoc comparisons revealed that “social support” (Mlikes = 7.75 ± 0.62), “acknowledgment of current & local events” (Mlikes = 7.43 ± 1.01) and “information” (Mlikes = 7.36 ± 0.84) had the most likes, and that “response solicitation” (Mcomments = 3.38 ± 1.34), “social support” (Mcomments = 3.05 ± 0.81), “information” (Mcomments = 2.68 ± 1.06) had the most comments.

Demographic segments

Specifically, post-hoc comparisons revealed that Instagram posts that involved neither gender (Mlikes = 7.31 ± 0.86, Mcomments = 2.64 ± 1.10) had the most likes and comments, compared with those that involved male only (Mlikes = 6.59 ± 1.05, Mcomments = 2.12 ± 1.20), female only (Mlikes = 6.54 ± 1.06, Mcomments = 2.14 ± 1.23), or both (Mlikes = 6.72 ± 0.91, Mcomments = 2.14 ± 1.23). Similarly, posts that involved neither Caucasian nor Non-Caucasian (Mlikes = 7.32 ± 0.86, Mcomments = 2.65 ± 1.11) had the most likes and comments, compared with those that involved Caucasian only (Mlikes = 6.31 ± 0.99, Mcomments = 2.05 ± 1.18), non-Caucasian only (Mlikes = 6.42 ± 0.97, Mcomments = 1.94 ± 1.22), or both (Mlikes = 6.66 ± 0.90, Mcomments = 2.09 ± 1.19).

Visual composition

Generally, Instagram posts that involved “illustrated” contents (Mlikes = 7.12 ± 0.97) received more likes than those that involved “photographic” contents (Mlikes = 6.72 ± 0.92), although the former (Mcomments = 2.48 ± 1.18) did not received more comments than the latter (Mlikes = 2.36 ± 1.02). Post-hoc comparisons revealed that “comics/cartoon” (Mlikes = 7.71 ± 0.72, Mcomments = 3.09 ± 0.98), “words on picture (no background-design)” (Mlikes = 7.64 ± 0.66, Mcomments = 2.95 ± 0.98) had the most likes and comments. Furthermore, posts that involved more pictures tended to have more likes (r(724)likes = .16, plikes < .001) and comments (r(724)comments = .11, pcomments = .002).

Visual framing topics

Specifically, post-hoc comparisons revealed that “anti-stigma” (Mlikes = 7.89 ± 0.87, Mcomments = 3.34 ± 0.88), “encouraging/supporting/reassurance” (Mlikes = 7.72 ± 0.70, Mcomments = 3.11 ± 0.95), and “classification (distinctive disorders, symptoms and presentations)” (Mlikes = 7.65 ± 0.64, Mcomments = 2.87 ± 0.77) had the most likes and comments, whereas “resources/services sharing (list, phone number)” (Mlikes = 6.89 ± 0.81, Mcomments = 2.22 ± 1.29) and “event/product/personnel info” (Mlikes = 6.05 ± 0.58, Mcomments = 1.59 ± 0.95) had the least likes and comments.


This study is among early attempts to examine organizational mental health promotion on Instagram. Specifically, based on the communication strategies of effective public health messaging (Nan et al., Citation2022), we investigated the underlying mechanisms of how mental health was communicated among various content and execution (visual) variables. We also explored the relations between content and visual variables with audience engagement. The results enrich the framework of Nan et al. (Citation2022) by offering evidence about how public health messaging was executed with visuals and the effects of these messages in terms of audience engagement. In addition, our findings explore mental health discourse on Instagram from an organizational perspective, which is crucial due to NGO’s perceived high trustworthiness among societal institutions (Edelman, Citation2022). The findings yield effective content and execution suggestions to these organizations with which to engage the public on visually dominant social media platforms, in response to the action plans from the United Nations and WHO.

Content features

This study examined communicative strategies and organizational efforts to increase mental health literacy as the two content features. Among three communicative strategies, information and community messages can garner more engagement than action messages, and social support was one of the most effective among all subcategories. In line with prior research on text-based platforms (Lovejoy & Saxton, Citation2012; Saxton & Waters, Citation2014), this study confirmed the effectiveness of these two engaging strategies on a visual-dominant platform. In addition, the study also contributes to MHL literature by revealing its coverage on social media and its influence on audience engagement. According to Jorm (Citation2012), compared to major physical diseases, the public is still ignorant about how to prevent mental disorders, and often delay or avoid seeking professional assistance. One possible reason is the limited mental health education and resources; and in addition, the stigma associated with mental illness also results in a negative attitude toward seeking treatment (Crowe et al., Citation2018; Ross et al., Citation2020). Therefore, social media, particularly official accounts of leading mental health organizations, should become a significant source for promoting MHL to the public. The findings showed that among half of the posts that were designed to increase at least one type of MHL, the most frequently covered was information-seeking efficacy – either alone or combined with other types of MHL categories, indicating organizations’ efforts on educating the public about how and where to find help. In addition, audiences were more likely to respond to the posts that bring illnesses to their attention, explain symptoms and possible treatments, and add external links that guide them to better understand a certain condition. Another MHL category effective in involving audiences was mental health care, which demonstrated useful tips on how to achieve and maintain positive mental health. This finding, on one hand, signals the audience’s needs and interests for concrete information to deal with mental health issues; on the other hand, it reinforces the importance of information utility in the context of healthcare (H. S. Kim, Citation2015). Mental health posts that involve extra resources in a link about certain illnesses, or suggestions on maintaining mental wellness, are all useful and practical information that can satisfy the “utility” function.

Visual features

The denotative-level visual analysis showed that most messages did not feature a specific gender or ethnicity, demonstrating a generic approach to mental health communication and promotion from organizations on social media. Although this generic approach runs counter to the recommendations from message tailoring literature (Pope et al., Citation2018) that call for targeting specific demographics in health messages, it worked in engaging social media audiences. There are two possible explanations. First, it matches the public discourse of mental health on Instagram where the majority posts only identify the general public without specifying any social groups (N. Lee et al., Citation2020). Second, this approach matches the goal of organizations, which is to raise awareness of mental health among the public, that is, people from diverse backgrounds. Also, demographic information of Instagram users showed that it has almost equal number of female (49.3%) and male (50.7%) users (McLachlan, Citation2022) and this platform has been used by different ethnicity groups with 35% of causation, 49% African Americans, and 52% Hispanic population (Auxier & Anderson, Citation2021). With this distribution of users across genders and ethnicities, the generic approach should be more effective.

At the stylistic-semiotic level, numbers of images and visual compositions were examined. In accordance with a previous study (Y. Kim & Kim, Citation2020), the results found that words on pictures with design were the most frequent visual form on Instagram, signaling the organization’s preference of communicating an abstract representation of mental health/illness. In regard to the number of pictures, although most posts only contained one picture, the number of visuals was positively related to engagement. This is somewhat surprising because other studies reported a negative correlation between the number of words in social media posts and audience engagement (Ju et al., Citation2021). More visuals, similar to more word counts, means more information, which should decrease audience involvement. However, the positive correlation in the current study indicates a difference in information processing between visual and textual messages, which triggers audience reactions. Visual and textual information is processed differently in that people use a verbal code to process language but an imagery code for visual messages (Paivio, Citation1986; Sadoski & Paivio, Citation2004). The process also produces different outcomes in which people demonstrate a greater recall of pictures and images as they enhance the sensory experience (Blanco et al., Citation2010; Hong et al., Citation2004). It is possible that due to the enhanced experience of seeing a picture, Instagram users reacted positively to more pictures through engaging with the content. The results of visual compositions, illustrative images―particularly comics/cartoons and words on plain backgrounds―were found more effective in eliciting engagement than photos. On one hand, these results align with previous findings that comics and cartoons are effective visual tools (Nan et al., Citation2022). On the other hand, it contradicts the notion that realistic images featuring real people or actions are more compelling for communicating health messages, as audiences can identify with the content (Ophir et al., Citation2019). Such results added complexity to the literature, and future studies should further explore the mechanism underlying this complexity.

Finally, the connotative level showed that anti-stigma was the most effective visual topic to elicit engagement, which confirmed the notion that stigma-reducing discourse is a powerful tool for mental health promotion on social media (Pavlova & Berkers, Citation2022). Social support topics also engage the audience because this type of visuals can meet the needs for community and support of the audience, as previous studies indicated that social media users seek mental health information to find community or support (Pavlova & Berkers, Citation2022). Lastly, classification framed visuals providing specific information about symptoms or treatment was the third most engaging visual topic. This result confirms that information utility is a predictor of user’s engagement of health information (Cappella et al., Citation2015). Taken together, the results support previous findings that various types of content meet public demands in different ways, and the differences are reflected in the likes, reposts and comments (J. Lee & Xu, Citation2018; Keib et al., Citation2018). Our study demonstrated that in the mental health communication context, anti-stigma, support and community, and information utility were the top three visual topics demanded from the audience.

Theoretical contributions and practical implications

This study yields several theoretical, methodological, and practical implications. First, it enriches the theoretical framework of communication strategies from effective public health messaging (Nan et al., Citation2022), particularly message execution, by offering evidence of effective visual strategies. Specifically, our findings showed certain stylistic-semiotic and connotative elements were effective in engaging the audiences. The next step should explore the relations between audience engagement and attitudinal or behavioral change to further test the effectiveness of these visual execution strategies.

Second, this study contributes to the methodological development of visual message analysis. As mentioned, due to the lack of a unified analysis framework, it is difficult to compare the research results to generate replicable and design specification recommendations for health promotion (King & Lazard, Citation2020). Our study used the four-tiered model of visual framing theory (Rodriguez & Dimitrova, Citation2011) and demonstrated that this analytical framework can be applied to social media visual message analysis and generate meaningful results. By employing a similar framework, future research can be compared and hopefully, through these results, replicable recommendations can be produced.

Regarding practical implications, this study provides valuable information to organizations about how to use visuals as a powerful tool to assist mental health promotion. First, we recommend using more illustrative visuals as they are more engaging and do not require professional photographers nor consent from the people being featured, saving time and resources for nonprofit organizations that are already on tight budgets. Second, anti-stigma themed visuals should also be used as they can actively engage the audience and help spread the word. In terms of content design, organizations should be cognizant of the public’s engagement with MHL and promote relevant content, including disorder diagnoses, self-care, reducing misunderstanding and stigma, along with external resources. As our health systems “have not yet adequately responded to the burden of mental disorders” (WHO, 2021, p. 4), such information can help with early detection and wellness management, thus reducing the gap between the need for treatment and its provision.

Conclusion, limitations and future studies

This study examined the content and visual features of Instagram posts of two leading mental health organizations. The findings revealed the layered nature of denotative, stylistic-semiotic, and connotative levels of these posts. Despite the contribution, a few limitations should be addressed. First, only two leading organizations were selected for analysis. Other organizations that had fewer followers and the ones that aim to target a specific population or illness, were excluded. Future studies may include a more comprehensive sample of local and national organizations. Second, this study only examined the posts but not Instagram stories, reels, or video, which are heavily consumed by users. As a result, the findings may not be comprehensive. Future studies are encouraged to examine all message forms posted on Instagram to better understand how mental health is visually represented and promoted. Third, this study only counted the number of comments and likes without exploring the comments content which can tell more detailed audience information such as sentiment. Therefore, future research should consider coding comment content to better understand how social media posts influence public sentiments and attitudes.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information


The author(s) reported there is no funding associated with the work featured in this article.


  • Adami, E., & Jewitt, C. (2016). Social media and the visual. Visual Communication, 15(3), 263–270. https://doi.org/10.1177/1470357216644153  [Crossref] [Web of Science ®][Google Scholar]
  • Alhabash, S., & McAlister, A. R. (2015). Redefining virality in less broad strokes: Predicting viral behavioral intentions from motivations and uses of Facebook and Twitter. New Media & Society, 17(8), 1317–1339. https://doi.org/10.1177/1461444814523726  [Crossref] [Web of Science ®][Google Scholar]
  • Auxier, B., & Anderson, M. (2021, April 7). Social media use in 2021. Pew Research Center. https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/  [Google Scholar]
  • Beasley, L., Kiser, R., & Hoffman, S. (2020). Mental health literacy, self-efficacy, and stigma among college students. Social Work in Mental Health, 18(6), 634–650. https://doi.org/10.1080/15332985.2020.1832643  [Taylor & Francis Online] [Web of Science ®][Google Scholar]
  • Blanco, C. F., Sarasa, R. G., & Sanclemente, C. O. (2010). Effects of visual and textual information in online product presentations: Looking for the best combination in website design. European Journal of Information Systems, 19(6), 668–686. https://doi.org/10.1057/ejis.2010.42  [Taylor & Francis Online] [Web of Science ®][Google Scholar]
  • Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, 26, 211–252. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x  [Crossref][Google Scholar]
  • Cappella, J. N., Kim, H. S., & Albarracín, D. (2015). Selection and transmission processes for information in the emerging media environment: Psychological motives and message characteristics. Media Psychology, 18(3), 396–424. https://doi.org/10.1080/15213269.2014.941112  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • Centers for Decease Control and Prevention. (2021, October 13). COVID-19 toolkits. Retrieved December 12, 2021, from https://www.cdc.gov/coronavirus/2019- ncov/communication/social-media-toolkit.html  [Google Scholar]
  • Chalmers, A. W., & Shotton, P. A. (2016). Changing the face of advocacy? Explaining interest organizations’ use of social media strategies. Political Communication, 33(3), 374–391. https://doi.org/10.1080/10584609.2015.1043477  [Taylor & Francis Online] [Web of Science ®][Google Scholar]
  • Chavira, D. A., Ponting, C., & Ramos, G. (2022). The impact of COVID-19 on child and adolescent mental health and treatment considerations. Behaviour Research and Therapy, 157, 1–8. https://doi.org/10.1016/j.brat.2022.104169  [Crossref] [Web of Science ®][Google Scholar]
  • Cheng, H.-L., Wang, C., McDermott, R. C., Kridel, M., & Rislin, J. L. (2018). Self-stigma, mental health literacy, and attitudes toward seeking psychological help. Journal of Counseling & Development, 96(1), 64–74. https://doi.org/10.1002/jcad.12178  [Crossref] [Web of Science ®][Google Scholar]
  • Crowe, A., Mullen, P. R., & Littlewood, K. (2018). Self-stigma, mental health literacy, and health outcomes in integrated care. Journal of Counseling & Development, 96(3), 267–277. https://doi.org/10.1002/jcad.12201  [Crossref] [Web of Science ®][Google Scholar]
  • Eaton, N. R., Keyes, K. M., Krueger, R. F., Balsis, S., Skodol, A. E., Markon, K. E., Grant, B. F., & Hasin, D. S. (2012). An invariant dimensional liability model of gender differences in mental disorder prevalence: Evidence from a national sample. Journal of Abnormal Psychology, 121(1), 282–288. https://doi.org/10.1037/a0024780  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • Edelman. (2022, January 24). 2022 Edelman trust Barometer. Retrieved December 22, 2022, from https://www.edelman.com/trust/2022-trust-barometer  [Google Scholar]
  • Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51–58. https://doi.org/10.1111/j.1460-2466.1993.tb01304.x  [Crossref] [Web of Science ®][Google Scholar]
  • Feuston, J. L., & Piper, A. M. (2018). Beyond the coded gaze: Analyzing expression of mental health and illness on instagram. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–21. https://doi.org/10.1145/3274320  [Crossref][Google Scholar]
  • Feuston, J. L., & Piper, A. M. (2019, May). Everyday experiences: Small stories and mental illness on Instagram. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–14). https://doi.org/10.1145/3290605.3300495  [Crossref][Google Scholar]
  • Fox, J., & Weisberg, S. (2019). An R companion to applied regression (3rd ed.). Sage.  [Google Scholar]
  • Friess, E., & Lam, C. (2018). Cultivating a sense of belonging: Using Twitter to establish a community in an introductory technical communication classroom. Technical Communication Quarterly, 27(4), 343–361. https://doi.org/10.1080/10572252.2018.1520435  [Taylor & Francis Online] [Web of Science ®][Google Scholar]
  • Furnham, A., & Swami, V. (2018). Mental health literacy: A review of what it is and why it matters. International Perspectives in Psychology, 7(4), 240–257. https://doi.org/10.1037/ipp0000094  [Crossref][Google Scholar]
  • Gibson, R., & Zillmann, D. (2000). Reading between the photographs: The influence of incidental pictorial information on issue perception. Journalism & Mass Communication Quarterly, 77(2), 355–366. https://doi.org/10.1177/107769900007700209  [Crossref] [Web of Science ®][Google Scholar]
  • Griffith, F. J., Stein, C. H., Hoag, J. E., & Gay, K. N. (2021). # MentalHealthArt: How Instagram artists promote mental health awareness online. Public Health, 194, 67–74. https://doi.org/10.1016/j.puhe.2021.02.006  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • Guillén, U., Suh, S., Munson, D., Posencheg, M., Truitt, E., Zupancic, J. A., Gafni, A., & Kirpalani, H. (2012). Development and pretesting of a decision aid to use when counseling parents facing imminent extreme premature delivery. The Journal of Pediatrics, 160(3), 382–387. https://doi.org/10.1016/j.jpeds.2011.08.070  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • Gupta, R., & Ariefdjohan, M. (2021). Mental illness on Instagram: A mixed method study to characterize public content, sentiments, and trends of antidepressant use. Journal of Mental Health, 30(4), 518–525. https://doi.org/10.1080/09638237.2020.1755021  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • Hawkins, D., & Weisberg, S. (2017). Combining the Box-Cox power and generalized log transformations to accommodate nonpositive responses in linear and mixed-effects linear models. South African Statistics Journal, 51(2), 317–328. https://doi.org/10.37920/sasj.2017.51.2.5  [Crossref][Google Scholar]
  • Hong, W., Thong, L., & Tamk, Y. (2004). Designing product listing pages on e-commerce websites: An examination of presentation mode and information format. International Journal of Human-Computer Studies, 61(4), 481–503. https://doi.org/10.1016/j.ijhcs.2004.01.006  [Crossref] [Web of Science ®][Google Scholar]
  • Huang, Y. C., Lin, Y. P., & Saxton, G. D. (2016). Give me a like: How HIV/AIDS nonprofit organizations can engage their audience on Facebook. AIDS Education and Prevention, 28(6), 539–556. https://doi.org/10.1521/aeap.2016.28.6.539  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • “Instagram Demographics in 2022: Most Important User Stats for Marketers”. (2022, March 24). Social media marketing & management dashboard. https://bloghootsuite.com/instagram-demographics/  [Google Scholar]
  • Ivey-Stephenson, A. Z., Crosby, A. E., Jack, S. P., Haileyesus, T., & Kresnow-Sedacca, M. J. (2017). Suicide trends among and within urbanization levels by sex, race/ethnicity, age group, and mechanism of death—United States, 2001–2015. MMWR Surveillance Summaries, 66(18), 1–16. https://doi.org/10.15585/mmwr.ss6618a1  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • Jorm, A. F. (2012). Mental health literacy: Empowering the community to take action for better mental health. The American Psychologist, 67(3), 231–243. https://doi.org/10.1037/a0025957  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • Jorm, A. F. (2015). Why we need the concept of “mental health literacy”. Health Communication, 30(12), 1166–1168. https://doi.org/10.1080/10410236.2015.1037423  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • Jorm, A. F., Korten, A. E., Jacomb, P. A., Christensen, H., Rodgers, B., & Pollitt, P. (1997). “Mental health literacy”: A survey of the public’s ability to recognise mental disorders and their beliefs about the effectiveness of treatment. Medical Journal of Australia, 166(4), 182–186. https://doi.org/10.5694/j.1326-5377.1997.tb140071.x  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • Ju, R., Dong, C., & Zhang, Y. (2021). How controversial businesses communicate CSR on Facebook: Insights from the Canadian cannabis industry. Public Relations Review, 47(4), 102059. https://doi.org/10.1016/j.pubrev.2021.102059  [Crossref] [Web of Science ®][Google Scholar]
  • Ju, R., Jia, M., & Cheng, J. (2021). Promoting mental health on social media: A content analysis of organizational Tweets. Health Communication. Advance online publication. https://doi.org/10.1080/10410236.2021.2018834  [Taylor & Francis Online][Google Scholar]
  • Kearney, M. D., Selvan, P., Hauer, M. K., Leader, A. E., & Massey, P. M. (2019). Characterizing HPV vaccine sentiments and content on Instagram. Health Education & Behavior, 46(25), 375–485. https://doi.org/10.1177/1090198119859412  [Crossref][Google Scholar]
  • Keib, K., Himelboim, I., & Han, J. Y. (2018). Important tweets matter: Predicting retweets in the# BlackLivesMatter talk on twitter. Computers in Human Behavior, 85, 106–115. https://doi.org/10.1016/j.chb.2018.03.025  [Crossref] [Web of Science ®][Google Scholar]
  • Kim, H. C. (2021). Mediating effect of stigma on the relationship between mental health literacy and help-seeking attitudes among university students in South Korea. International Journal of Mental Health. Advance online publication. https://doi.org/10.1080/00207411.2021.1965397  [Taylor & Francis Online][Google Scholar]
  • Kim, H. S. (2015). Attracting views and going viral: How message features and news-sharing channels affect health news diffusion. Journal of Communication, 65(3), 512–534. https://doi.org/10.1111/jcom.12160  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • Kim, Y., & Kim, J. H. (2020). Using photos for public health communication: A computational analysis of the Centers for Disease Control and Prevention Instagram photos and public responses. Health Informatics Journal, 26(3), 2159–2180. https://doi.org/10.1177/1460458219896673  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • King, A. J. (2015). A content analysis of visual cancer information: Prevalence and use of photographs and illustrations in printed health materials. Health Communication, 30(7), 722–731. https://doi.org/10.1080/10410236.2013.878778  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • King, A. J. (2016). Visual exemplification and skin cancer: The utility of exemplars in promoting skin self-exams and atypical nevi identification. Journal of Health Communication, 21, 826–836. https://doi.org/10.1080/10810730.2016.1177143  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • King, A. J., & Lazard, A. J. (2020). Advancing visual health communication research to improve infodemic response. Health Communication, 35(14), 1723–1728. https://doi.org/10.1080/10410236.2020.1838094  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • Krippendorff, K. (2018). Content analysis: An introduction to its methodology. Sage publications.  [Google Scholar]
  • Kutcher, S., Wei, Y., & Coniglio, C. (2016). Mental health literacy: Past, present, and future. Canadian Journal of Psychiatry. Revue canadienne de psychiatrie, 61(3), 154–158. https://doi.org/10.1177/0706743715616609  [Crossref][Google Scholar]
  • Lacy, S., Watson, B. R., Riffe, D., & Lovejoy, J. (2015). Issues and best practices in content analysis. Journalism & Mass Communication Quarterly, 92(4), 791–811. https://doi.org/10.1177/1077699015607338  [Crossref] [Web of Science ®][Google Scholar]
  • Lama, Y., Chen, T., Dredze, M., Jamison, A., Quinn, S. C., & Broniatowski, D. A. (2018). Discordance between human papillomavirus Twitter images and disparities in human papillomavirus risk and disease in the United States: Mixed-methods analysis. Journal of Medical Internet Research, 20(9). https://doi.org/10.2196/10244  [Crossref][Google Scholar]
  • Lee, J., & Xu, W. (2018). The more attacks, the more retweets: Trump’s and Clinton’s agenda setting on Twitter. Public Relations Review, 44(2), 201–213. https://doi.org/10.1016/j.pubrev.2017.10.002  [Crossref] [Web of Science ®][Google Scholar]
  • Lee, N., Buchanan, K., & Yu, M. (2020). Each post matters: A content analysis of #mentalhealth images on Instagram. Journal of Visual Communication in Medicine, 43(3), 128–138. https://doi.org/10.1080/17453054.2020.1781535  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • Lovejoy, K., & Saxton, G. D. (2012). Information, community, and action: How nonprofit organizations use social media. Journal of Computer-Mediated Communication, 17(3), 337–353. https://doi.org/10.1111/j.1083-6101.2012.01576.x  [Crossref] [Web of Science ®][Google Scholar]
  • Luarn, P., Lin, Y.-F., & Chiu, Y.-P. (2015). Influence of Facebook brand-page posts on online engagement. Online Information Review, 39(4), 505–519. https://doi.org/10.1108/OIR-01-2015-0029  [Crossref] [Web of Science ®][Google Scholar]
  • Martin, L., Turnquist, A., Groot, B., Huang, S., Kok, E., Thoma, B., & Merrienboer, J. (2019). Exploring the role of infographics for summarizing medical literature. Health Professions Education, 5(1), 48–57. https://doi.org/10.1016/j.hpe.2018.03.005  [Crossref][Google Scholar]
  • McCosker, A., & Gerrard, Y. (2021). Hashtagging depression on Instagram: Towards a more inclusive mental health research methodology. New Media & Society, 23(7), 1899–1919. https://doi.org/10.1177/1461444820921349  [Crossref] [Web of Science ®][Google Scholar]
  • McLachlan, S. (2022, March 24). Instagram demographic in 2023: Most important user stats for marketers. Hootsuite.https://blog.hootsuite.com/instagram-demographics/  [Google Scholar]
  • Men, L. R., & Tsai, W. H. S. (2012). How companies cultivate relationships with publics on social network sites: Evidence from China and the United States. Public Relations Review, 38(5), 723–730. https://doi.org/10.1016/j.pubrev.2011.10.006  [Crossref] [Web of Science ®][Google Scholar]
  • Men, L. R., & Tsai, W. H. S. (2014). Perceptual, attitudinal, and behavioral outcomes of organization–public engagement on corporate social networking sites. Journal of Public Relations Research, 26(5), 417–435. https://doi.org/10.1080/1062726X.2014.951047  [Taylor & Francis Online] [Web of Science ®][Google Scholar]
  • Mental and Behavioral Health - African Americans. (2021, May 18). U.S Department of Health and Human Services Office of Minority Health. Retrieved December 12, 2021, from https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=24  [Google Scholar]
  • Mental health by the numbers. (2019, September). https://www.nami.org/learn-more/mental-health-by-the-numbers  [Google Scholar]
  • Messaris, P. (1997). Visual persuasion: The role of images in advertising. Sage.  [Crossref][Google Scholar]
  • Munzert, S., Rubba, C., Meißner, P., & Nyhuis, D. (2014). Automated data collection with R: A practical guide to web scraping and text mining. John Wiley & Sons.  [Crossref][Google Scholar]
  • Nan, X., Iles, I. A., Yang, B., & Ma, Z. (2022). Public health messaging during the COVID-19 pandemic and beyond: Lessons from communication science. Health Communication, 37(1), 1–19. https://doi.org/10.1080/10410236.2021.1994910  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • Nobles, A. L., Leas, E. C., Noar, S., Dredze, M., Latkin, C. A., Strathdee, S. A., & Ayers, J. W. (2020). Automated image analysis of instagram posts: Implications for risk perception and communication in public health using a case study of# HIV. PLoS One, 15(5). https://doi.org/10.1371/journal.pone.0231155  [Crossref][Google Scholar]
  • Ophir, Y., Brennan, E., Maloney, E. K., & Cappella, J. N. (2019). The effects of graphic warning labels’ vividness on message engagement and intentions to quit smoking. Communication Research, 46(5), 619–638. https://doi.org/10.1177/0093650217700226  [Crossref] [Web of Science ®][Google Scholar]
  • Paivio, A. (1986). Mental representations: A dual coding approach. Oxford University Press.  [Google Scholar]
  • Pavlova, A., & Berkers, P. (2022). “Mental health” as defined by Twitter: Frames, emotions, stigma. Health Communication, 37(5), 637–647. https://doi.org/10.1080/10410236.2020.1862396  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • Pirkis, J., Schlichthorst, M., King, K., Lockley, A., Keogh, L., Reifels, L., Spittal, M. J., & Phelps, A. (2019). Looking for the ‘active ingredients’ in a men’s mental health promotion intervention. Advances in Mental Health, 17(2), 135–145. https://doi.org/10.1080/18387357.2018.1526095  [Taylor & Francis Online] [Web of Science ®][Google Scholar]
  • Pope, J. P., Pelletier, L., & Guertin, C. (2018). Starting off on the best foot: A review of message framing and message tailoring, and recommendations for the comprehensive messaging strategy for sustained behavior change. Health Communication, 33(9), 1068–1077. https://doi.org/10.1080/10410236.2017.1331305  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]
  • Rodriguez, L., & Dimitrova, D. V. (2011). The levels of visual framing. Journal of Visual Literacy, 30(1), 48–65. https://doi.org/10.1080/23796529.2011.11674684  [Taylor & Francis Online][Google Scholar]
  • Ross, S. G., Bruggeman, B., Maldonado, M., & Deiling, M. (2020). Examining personal, perceived, treatment, and self-stigma in college students: The role of parent beliefs and mental health literacy. Journal of College Student Psychotherapy, 34(3), 183–197. https://doi.org/10.1080/87568225.2019.1580657  [Taylor & Francis Online] [Web of Science ®][Google Scholar]
  • Sadoski, M., & Paivio, A. (2004). A dual coding theoretical model of reading. In B. Ruddellr & J. Unraun (Eds.), Theoretical models and processes of reading (5th ed., pp. 1329–1362). International Reading Association.  [Crossref][Google Scholar]
  • Saxton, G. D., & Waters, R. D. (2014). What do stakeholders like on Facebook? Examining public reactions to nonprofit organizations’ informational, promotional, and community-building messages. Journal of Public Relations Research, 26(3), 280–299. https://doi.org/10.1080/1062726X.2014.908721  [Taylor & Francis Online] [Web of Science ®][Google Scholar]
  • Seelig, M. I., Millette, D., Zhou, C., & Huang, J. (2019). A new culture of advocacy: An exploratory analysis of social activism on the web and social media. Atlantic Journal of Communication, 27(1), 15–29. https://doi.org/10.1080/15456870.2019.1540418  [Taylor & Francis Online] [Web of Science ®][Google Scholar]
  • Seventy-Fourth World Health Assembly. (2021). Comprehensive mental health action plan 2013-2030 (Agenda item 13.3). https://www.who.int/publications/i/item/9789240031029  [Google Scholar]
  • Sixty-Six World Health Assembly. (2013). Comprehensive mental health action plan 2013-2020 (Agenda item 13.3). https://www.who.int/mental_health/publications/action_plan/en/  [Google Scholar]
  • Tay, J. L., Tay, Y. F., & Klainin, Y. P. (2018). Effectiveness of information and communication technologies interventions to increase mental health literacy: A systematic review. Early Intervention in Psychiatry, 12(6), 1024–1037. https://doi.org/10.1111/eip.12695  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • United Nations. (2020, May 13). COVID-19 and the need for action on mental health; policy brief. https://unsdg.un.org/resources/policy-brief-covid-19-and-need-action-mental-health,2020  [Google Scholar]
  • Wei, Y., Baxter, A., & Kutcher, S. (2019). Establishment and validation of a mental health literacy measurement in Canadian educators. Psychiatry Research, 279, 231–236. https://doi.org/10.1016/j.psychres.2019.03.009  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • Wei, Y., McGrath, P. J., Hayden, J., & Kutcher, S. (2015). Mental health literacy measures evaluating knowledge, attitudes and help-seeking: A scoping review. BMC Psychiatry, 15(1), 291. https://doi.org/10.1186/s12888-015-0681-9  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • World Health Organization. (2022, March 2). Mental health and COVID-19: Early evidence of the pandemic's impact: Scientific brief. https://www.who.int/publications/i/item/WHO-2019-nCoV-Sci_Brief-Mental_health-2022.1  [Google Scholar]
  • Wynaden, D., Chapman, R., Orb, A., McGowan, S., Zeeman, Z., & Yeak, S. (2005). Factors that influence Asian communities’ access to mental health care. International Journal of Mental Health Nursing, 14(2), 88–95. https://doi.org/10.1111/j.1440-0979.2005.00364.x  [Crossref] [PubMed] [Web of Science ®][Google Scholar]
  • Yang, A., & Kent, M. (2014). Social media and organizational visibility: A sample of Fortune 500 corporations. Public Relations Review, 40(3), 562–564. https://doi.org/10.1016/j.pubrev.2014.04.006  [Crossref] [Web of Science ®][Google Scholar]
  • Yoo, W. (2016). The influence of celebrity exemplars on college students’ smoking. Journal of American College Health, 64(1), 48–60. https://doi.org/10.1080/07448481.2015.1074238  [Taylor & Francis Online] [PubMed] [Web of Science ®][Google Scholar]


Coder ID:

Basic Information

Message No.:

Message Link:

Organization ID:

Content Strategy

Criteria: Examine the overall post, including caption and visuals. It must be the main function of the overall message. If there is more than one strategy, choose the primary one.

How to decide which one is the primary: 1. Length of the language (including captions and words on visual). Choose the longer one. 2. If the same length, choose the one that appeared first.

1. Information

Posts that spread information about the organization, any news, facts, reports of information relevant to mental health and mental illnesses or any information of potential interest to audience

2. Community

Posts that emphasize on fostering relationships, create networks, and build communities, could include:

  1. Providing social supporte.g., posts that are encouraging, supportive in nature. Tell the audience that they are not alone, they can/should reach out for help, the community/organization supports them, etc.

  2. Giving recognition and thankse.g., thank donors, thank supporters

  3. Acknowledgement of current and local eventse.g., holiday greetings, support community events or sports teams

  4. Responses to reply messagese.g., @a specific account and reply to that account

  5. Response solicitatione.g., ask audience strategies of dealing with stress

3. Action

Posts that ask audience to do something, could include:

  1. Promoting an evente.g., beyond information, also provide a specific date, time, price or even the link of the event

  2. Selling a producte.g., introducing and promoting relevant merchandises (a T-shirt with slogans),usually include a link of the product

  3. Donating appeale.g., ask for donations

  4. Call for volunteers and employeese.g., ask people to volunteer or work for the organization, or relevant events

  5. Lobbying and advocacye.g., ask audience to perform a lobbying or advocacy related activity, such as calla senator or vote for a relevant policy

  6. Joining website/promoting organizatione.g., ask the audience to follow another relevant social media account, visit anorganization’s website

  7. Learn how to help otherse.g., ask the audience to learn different ways to help other with mental health issues. Usually outline specific actions. Different from the informational posts,this type of posts usually includes a call to action.

Messages Designed to Increase Mental Health Literacy

1. N/A

No content about MHL covered in the message

2. How to obtain and maintain good mental health

e.g., mental health care, such as how to manage stress and improve sleep

3. Mental disorders and treatments

e.g., facts, stats, symptoms and possible ways to treat a specific disorder

4. Decreasing stigma against mental illness

e.g., stories or objects that attempt to reduce stigma

5. Enhancing help-seeking efficacy

offers strategies or external sources and links of seeking professional help

6. Combinations of good mental health+efficacy

7. Disorder+efficacy

8. Stigma+efficacy

Visual_Denotative Level

Criteria: examine the visuals only

1. Gender

Focus on the human figures presented in the visuals:

Male 0) non-presence 1) presence

Female 0) non-presence 1) presence

2. Ethnicity

Focus on the human figures presented in the visuals:

Caucasian 0) non-presence 1) presence

Non-Caucasian/ethnic minority groups 0) non-presence 1) presence

Visual_ Stylistic-semiotic Level

Criteria: examine the visuals only

  • 1. Number of visuals

  • Just count how many pictures were presented in each post

  • 2. Visual composition

    1. Photographic: Visuals that are composed of a realistic photograph of persons, objects, places, etc.

    2. Illustrated: visuals containing any sort of computer-assisted illustrative format, could include:

      1. Comics/Cartoon

      2. Infographic

      3. Words on pictures (with design). Design means any other illustrative formats, but the main body of the visual is still words

      4. Words on pictures (without design). Only words on the visual, no other illustrative formats

Visual_Connotative Level

Criteria: examine the visuals and carefully read all the words on visuals

Framing topics

Code for the primary topic discussed using visuals, the topic was discussed by the majority of the words in the picture or by the majority of the pictures if multiple pictures were included in a post.

  • 1. AwarenessSpreading information on days, months, campaign, charity; asking for attention to mental health/illness; or sharing relevant research, reports, news, facts, and data.

  • 2. Mental healthcareDiscussing how to practice mental health care, usually include specific how-to tips

  • 3. ClassificationDiscussing distinctive illnesses, both symptoms and treatments

  • 4. Policy and systemDiscussing relevant policy or health care system, such as insurance system

  • 5. Anti-stigmaAny topic that fights against the stigma associated with mental illnesses; stigma may include danger to self, cost to society, danger to others, Bewitched, crazy, vulnerable, failure, asocial, etc.

  • 6. Resources and servicesSharing information about mental health relevant resources or services, such ashotline, online consulting, screening kit, or suicide prevention

  • 7. Individual storiesSharing specific individuals’ stories about mental health or mental illness

  • 8. Support and reassuranceSpreading the sense of supporting and reassurance, usually aiming at creating a sense of community that support each other, or telling audience that everything is ok/will pass

  • 9. Event/product/personnel informationProviding specific information on a relevant product that supports mentalhealth/organization, such as T-shirts and bracelets; introducing employees or volunteers working for the organization; or an event organized by the organization

  • 10. Others

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