Pork production on YouTube: frame and sentiment analysis

Introduction: Social media platforms increasingly influence public perceptions of agriculture by shaping narratives around food production. This study explores the portrayal of pork production on YouTube and the corresponding audience reactions, highlighting its role in shaping public discourse.

Methods: Guided by the framing theory, the research examines 147 videos and 735 top comments to analyze media and audience frames alongside sentiments. Video titles, descriptions, and thumbnails represent media frames, while the top comments reflect audience frames. Frames are identified through literature review and sample analysis, with selected videos manually coded for frame presence. Sentiment analysis of textual elements is conducted using MaxQDA, and visual elements are coded manually.

Results: Findings show that information and technology of pork production, and entrepreneurship frames dominate media portrayals, emphasizing information sharing on production practices and business ventures. In contrast, audience responses often highlighted ethical concerns, particularly animal welfare, underscoring moral sensitivity in public discourse. Positive sentiments were prevalent across media and audience responses, though critical views remained significant. The strong alignment between media and audience frames and sentiments underscores the influence of strategic framing in shaping perceptions. Emotional appeals in videos are found to play a critical role in audience reactions.

Discussion: These findings suggest that transparent communication emphasizing ethical practices and technological advancements can effectively engage audiences. By aligning content with audience values and leveraging emotional connections, communicators can counteract misinformation, foster trust, and promote informed discussions about the pork industry. This study provides a framework for utilizing social media, specifically YouTube, to address public concerns while advancing transparency and trust in agricultural practices.

1 Introduction

Meat and meat products have historically served as essential protein sources in the human diet (Tonsor and Lusk, 2022). However, in recent decades, there has been a growing concern among consumers about food quality and health benefit information, including meat products (Teixeira and Rodrigues, 2021). Furthermore, many consumers expect farm animal production methods to incorporate factors such as animal welfare and other social and ethical considerations (Grunert et al., 2018). Consumer perceptions frequently involve strong emotions and range from concern and distrust to criticism and overt opposition (Capper, 2020; Spain et al., 2018). Over the past decade, the pork industry has encountered heightened public scrutiny, encompassing concerns such as antibiotic use, confinement in gestation stalls, and animal welfare, as well as consumer apprehensions regarding pork meat quality and environmental impact (Font-i-Furnols et al., 2019; Grunert et al., 2018; Tønnesen and Grunert, 2021).

The gradual shift from rural to urban living over the past century has distanced families from their farming roots, leaving them with little understanding of contemporary agricultural practices (Howard et al., 2017). The study noted that with the growing gap between consumers and producers, consumers are increasingly turning to social media platforms for information on food-related matters such as food production practices and safety. Simultaneously, producers have found it challenging to communicate with consumers about modern farming because of a general lack of first-hand knowledge, the rapid advancement of agricultural technology, and a prevalent “big is bad” sentiment (Rumble and Irani, 2016, p. 2). Animal agriculture has dealt with negative media publicity and communication issues pertaining to antibiotic use, animal welfare, food safety, the environment, and concerns regarding the nutritional content of products derived from animals (Specht et al., 2014). The combination of negatively framed and emotional media coverage and a lack of experience has left consumer opinions toward animal agriculture vague and pliable (Specht and Rutherford, 2013; Kovar and Ball, 2013; Van Boxtel et al., 2022). Moreover, in today’s digital world, recommendation algorithms on social media platforms shape content selection and visibility by personalizing feeds based on their interactions and preferences (Haroon et al., 2022; Narayanan, 2023). By promoting content that aligns with existing beliefs, these algorithms often create filter bubbles that limit exposure to diverse viewpoints, reinforcing confirmation bias and fueling polarization (Whittaker et al., 2021). Despite this, social media also proves valuable by enabling the showcase of farming, ranching, and food production, giving agriculturists a platform to share their experiences and challenges (Holt-Day et al., 2020).

YouTube is a widely used social media platform, facilitating user interaction, creation, sharing, and exchange of information and ideas in diverse formats, boasting an extensive audience reach as the second-largest search network behind Google (Farabi et al., 2023). Utilizing auditory and visual communication, along with closed captioning and other inclusive technologies, YouTube is readily accessible to a broad range of viewers with diverse demographic characteristics at no cost (Thomas et al., 2021). According to Van Boxtel et al. (2022), videos are among the most powerful sources of agriculture-related information for consumers. YouTube has provided viewers access to locations typically inaccessible due to geographical constraints or regulations allowing agricultural stakeholders to exhibit diverse farming, ranching, or food production processes (Holt-Day et al., 2020).

Research studies using YouTube content analysis have examined general food and farming technology-related topics (e.g., Basch et al., 2023; Chakma et al., 2022; Mandal et al., 2022; Van Gorp and van der Goot, 2012). However, frames in the context of animal production remain an understudied area. While some studies have analyzed frames in YouTube videos related to wild pig hunting (e.g., McLean et al., 2022; Mörner and Olausson, 2017), studies on framing withing the pork industry have primarily focused on news media coverage (e.g., Chan and Babbitt, 2014; Sitton et al., 2004) and Instagram posts (Bacon, 2022). Hence, a noteworthy gap in the existing literature exists related to YouTube video content associated with pork production, given the platform’s popularity and accessibility to a wide audience.

YouTube provides a wide array of features beyond uploading and viewing video clips. Viewers can comment, like, or dislike videos to share their opinions. Features like subscription counts, rankings, and like or dislike counts on YouTube indicate the popularity and receptivity of content. However, an analysis of the posted comments provides an opportunity to obtain additional nuance about audiences’ attitudes and feelings toward the video contents (Lee et al., 2017). Understanding media portrayals is crucial as it provides insights for scholars and communication practitioners within the pork industry on how pork production practices are depicted in social media and the impact of this depiction on consumers’ perceptions. Such insights can assist pork producers, agricultural communicators, and industry stakeholders to better understand public perceptions and concerns regarding pork production and may be able to more effectively tailor their subsequent communication strategies to enhance transparency and trust.

2 Literature review

Food and agriculture attract social media attention due to their essential role in daily life, making topics like health, safety, food trends, and food availability broadly appealing (Rutsaert et al., 2013; Stevens et al., 2016). Additionally, the agricultural sector’s economic impact, the cultural significance of food, technological advancements enhancing sustainability, and global food security issues ensure continuous media interest and coverage (Stevens et al., 2016; Wolfert et al., 2017). Moreover, concerns about safety, transparency, and distrust in the agricultural industry garner significant public and media interest (Stevens et al., 2018).

Concerns surrounding food safety, transparency, distrust in the agro-food industry, and sustainability controversies are particularly active topics on social media within the domain of food and agricultural (Stevens et al., 2018). Social media platforms such as YouTube serve as abundant sources of data, offering insights into the beliefs, attitudes, and behaviors of users across different temporal and spatial scales and contexts (Lopez et al., 2019). Unlike traditional media, social media enables bidirectional communication, allowing the public to engage actively in discussions while also serving as a platform for assessing consumer perceptions during controversies or agricultural crises (Rutsaert et al., 2013). For instance, de Araújo et al. (2022) found mainstream media and social networks have significantly contributed to spreading information about meat and its products. They further mentioned that in recent years, there has been considerable negative focus on these products, primarily concerning health and sustainability, which has influenced consumer perspectives and behaviors. Similarly, Howard et al. (2017) found information regarding the pink slime incident—where lean finely textured beef (LFTB) was negatively portrayed as unsafe and likened to dog food on social media platforms —had a detrimental effect on university students’ views of the beef industry. These findings suggest the significance of studying mainstream social media representation of pork production and the pork industry.

2.1 Framing

This study utilized framing theory to examine the portrayal of pork industry on YouTube. Goffman (1974) described frames as interpretive structures that enable individuals to identify, perceive, understand, and categorize events and situations in their social surroundings, providing them with context for possible actions. Following a similar concept, Gitlin (1980) characterized frames as “persistent selection, emphasis, and exclusion” (p. 7), while Gamson and Modigliani (1987) defined frames as an organizing principle or narrative that assigns significance to a piece of work.

Entman (1991) proposed two distinct layers of news frames: audience frames, representing concepts retained by individuals for information processing, and news frames, characterizing traits inherent in the news itself. According to Entman (1993), framing involves the processes of “selection and salience” (p. 52), wherein specific elements of a perceived reality are chosen and highlighted in communication texts to endorse a particular definition of the problem, causal interpretation, moral assessment, and/or recommended course of action for the subject under discussion. Scheufele (1999) proposed a framing process model, where framing is seen as a continuous cycle where the outcomes of one stage feed into the next. This model outlines four framing processes: frame building, connecting internal and external factors shaping media coverage; frame setting, involving the transmission of frames from media to audiences; individual-level effects of framing, exploring how audience frames influence behavior, attitudes, and cognition; and journalists as audiences, suggesting that the frames they use can also impact them due to cognitive biases. This study is guided by frame setting and individual-level effects of framing where we hypothesize that media frames set by YouTube videos about pork industry are transmitted to the audiences affecting their opinions and responses in the comments.

Given the significant impact media has on audience attitudes, framing analysis has been widely used to assess media influences and audience perceptions. In the contemporary multimodal media environment, framing analysis encompasses textual elements such as headlines and captions, visual components like photographs or videos, and auditory aspects such as voiceovers or background music, all of which work together to shape audience perceptions and interpretations (Geise and Xu, 2024). Media narratives surrounding food production shapes individuals’ perceptions of what constitutes palatable, sustainable, healthy, or unhealthy diets (Brüggemann et al., 2022). Therefore, research on how journalists, mass media and social media frame food production practices could provide unique insights into how to effectively communicate. For example, a large body of literature has utilized framing to investigate concerns within agriculture in the United States. The topics that have garnered the greatest focus include genetically modified foods (e.g., Basch et al., 2023; Hunter, 2023; Pjesivac et al., 2020) and organic foods (Cahill et al., 2010; Meyers and Abrams, 2010; Vargas Meza and Yamanaka, 2020). In regard to the livestock industry, some framing studies have focused on red meat (e.g., Sievert et al., 2022), poultry production (e.g., Edgar et al., 2017; Estes et al., 2017; Van Boxtel et al., 2022) and “pink slime” in the beef industry (e.g., Runge et al., 2018). There is a limitation of studies in exploring media framing and representation of pig farming and the pork industry despite growing concern of consumers towards the industry.

According to Scheufele (1999), several factors potentially shape how a specific topic is framed in the media. Therefore, it is crucial to investigate the sources of media content as well as emerging trends in framing about the contents. YouTube videos are primarily represented by two elements: the thumbnail image and the title. A video thumbnail captures the essence of its content in a single image and the title provides a concise, compelling description of what the video is about, often highlighting key topics or emotions to spark curiosity (Lee et al., 2023). These not only convey the video’s content but also entice viewers to click, much like advertisements do (Lee et al., 2023; Zhang et al., 2021). Several studies have examined users’ pre-viewing behavior, finding that factors like informational content and negative emotions in video titles, the informational quality of descriptions and thumbnails emphasizing expressive faces, clear subject display, and descriptive text can influence views in certain YouTube categories (Shimono et al., 2020; Tafesse, 2020).

2.1.1 Media frame as independent variable

Research exploring media frames has conceptually defined media frames as variables with potential to influence attitudes, opinions, or individual frames (Entman, 1993; Scheufele, 1999). Media often employ emotional appeals in message framing, using references to subject matter that relate with the audience, audio content, and music to evoke emotions in an audience (Brader, 2006; Fischer et al., 2021). These appeals connect the viewers to the subject, making the issue more relatable and impactful, thereby shaping opinions and attitudes (Gross, 2008). The concept that the media significantly shape discourse and thereby influence perceptions and societal understanding of significant issues and events has garnered significant research focus. Goffman (1974), in attempt to furnish evidence of the effects of framing, elucidates that, individuals adopt particular interpretations of reality based on how that reality has been framed among the various interpretations available.

An increasing number of researchers have discovered evidence supporting the idea that media frames influence public perceptions of events and policies. A study conducted by Qu et al. (2017) employed a between-subject, post-test-only experiment with a control group to examine how three different message frames about local food, presented through online videos, influenced U.S. consumers’ attitudes. Each 30-s video highlighted one specific benefit of local food: superior quality, support for the local economy, or enhanced social connections. Findings revealed all three videos fostered positive attitudes toward local food, whereas the control group’s attitude remained neutral. Similarly, Kouarfaté and Durif (2023) demonstrated posts with one or more media frames had a significant impact on consumer attitudes towards cultured meat compared to posts with no media framing. Pjesivac et al. (2020) experimentally examined how different frames—highlighting the opportunities and risks of genetically modified organisms (GMOs), both separately and together—influenced attitudes, intentions, and behavior regarding GMOs among university students by controlling for participants’ prior knowledge. Findings revealed opportunity framing elicited more positive attitudes towards GMOs than risk framing. Attitudes and intentions aligned with the frame encountered. Similarly, Runge et al. (2018) investigated the impact of framing on perceptions of “lean finely textured beef (LFTB)” versus “pink slime” through a mail survey in Wisconsin. Participants were randomly assigned to versions of the survey mentioning either pink slime or LFTB in ground beef. The findings revealed those exposed to pink slime reported higher perceived risks compared to LFTB.

2.1.2 Visual framing

The same principles of selection, inclusion, and exclusion apply to visual framing as they do to textual framing (Rodriguez and Dimitrova, 2011). Although visualizations inherently involve framing and represent a version of reality, viewers often perceive them as direct windows into reality, which can significantly influence public discourse (SeppÄNen and VÄLiverronen, 2003). Visual framing has been explored in different fields, including political conflict (e.g., Fahmy, 2010), genetic engineering (e.g., Clancy and Clancy, 2016), and climate change (e.g., Wardekker and Lorenz, 2019). Images can serve as evocations, carrying cultural significance while also delivering a strong descriptive impact (Rose, 2008). Simultaneously, they can present something unexpected or unusual to provoke a reaction from the viewer. However, images are rarely neutral: factors like objects, color, season, weather, location, lighting, and camera angle all influence how the subject is perceived and with the use of technology, manipulating images through camera settings and software is easier than ever (Mahon et al., 2023). Busch and Spiller (2018) explain photographs of farm animals taken from different angles—such as a human’s perspective, an animal’s view, or a bird’s eye view—led to varied public evaluations. Space allowance was viewed more favorably in bird’s eye images, while animals lying down were often linked to illness. Similarly, it was found that lighting levels in video clips of pig farming impacted public perceptions of the farms shown (Mahon et al., 2023; Wildraut et al., 2015).

Given YouTube’s collaborative environment involving both uploaders and commenters alter the dynamics of media-audience relationships (Rutsaert et al., 2013), it is important to explore audience’s response to the content and whether YouTube video frames affect the attitude of the audience towards the agricultural industry. Therefore, this study sought to examine the portrayal of the pork industry on YouTube and viewers’ feedback on the portrayals from both frames and sentiment perspectives. Specifically, the research questions and hypotheses are:

RQ1: What are the primary frames and sentiments on YouTube videos about the pork industry?

RQ2: What are the primary frames and sentiments expressed in the comments of YouTube videos about the pork industry?

H1: The frames of YouTube videos about the pork industry are associated with audiences’ frames in the comments.

H2: The sentiments of YouTube videos about the pork industry are associated with audiences’ sentiments in the comments.

3 Materials and methods

For this study, we first conducted a literature review to identify existing frames relevant to the pork industry, followed by the collection of YouTube video data. We then reviewed a sample of videos to identify any emergent frames, and ultimately coded and analyzed all the data using a quantitative approach.

3.1 Data collection

The COVID-19 pandemic brought media attention to meat production, particularly conditions in meatpacking plants (Taylor et al., 2020). Therefore, we selected March 11, 2020, the day the World Health Organization declared COVID-19 a global health emergency (WHO, 2020), as the start date and March 11, 2024, exactly four years later, as the end date.

We obtained publicly available YouTube videos and comments through YouTube Data Tools¹ for the study (Rieder, 2015). This tool uses the YouTube Data application program interface (API), which allows the users to select six modules of YouTube data: channel info, channel list, channel network, video list, video co-commenting network and video comments. The video list module generates a compilation of video information and statistics sourced from one of four options: videos uploaded to a designated channel, a playlist, videos obtained through a specific search query, or videos specified by a list of identifiers. We used the video list module to generate the list of videos using five search queries: pig farming, pork industry, pork factory, pig industry, and pig factory. We planned to analyze the video contents that provided information about the pork industry for the study; therefore, the five queries were finalized through trial and error of various keywords such as pork, swine, hog, pig farming, etc.

The list contained other significant information such as video title, channel, description, thumbnails, views, likes and comment count, etc. For each search query, we sorted the videos’ comment count as a measure of popularity and discussion of the content among viewers. Any videos that did not serve the purpose of our study were removed, such as cartoons, gaming, and recipes. After cleaning the data, we selected the first 100 videos with maximum comments from each list generated by using each search query totaling 500 videos. Duplicated videos from the list of all five search queries were excluded, resulting in a total of 300 included videos. Videos with less than 30 comments and 3,000 views were excluded, signifying the unpopularity of the videos among the mass audience. A previous study by Thomas et al. (2021) excluded videos with fewer than 500 views to analyze YouTube videos for exploring food safety messages during COVID-19, making our study more robust by setting a higher threshold for video popularity. Exclusion thresholds for our study were established as a posteriori by the research team. The research team then reviewed every video and removed videos that were irrelevant or in languages other than English, resulting in a total of 147 videos appropriate for analysis. Lastly, video identifiers from the final list of videos were applied to obtain the top five comments manually using YouTube’s Top Comments filter, which resulted in 735 individual comments. The video selection and exclusion process is depicted in Figure 1.

Figure 1

Figure 1. YouTube videos selection and exclusion process.

3.1.1 Description of selected YouTube videos

YouTube is the top global social media platforms that reach audiences across the boundaries. The channels of selected videos were based on numerous countries, including the United States, the UK, Asian countries such as Vietnam and China, and African nations such as Uganda and Kenya. The selected YouTube videos were uploaded by different groups including pork producers worldwide, activists and non-profit organizations, television and news channels, business companies, other content creators, etc. The distribution of analyzed videos based on default YouTube labeling categories is presented in Table 1. Across all included videos, “People and Blogs” was represented most frequently followed by “Pets and Animals” and “Science and Technology”.

Table 1

Table 1. Distribution of videos in different categories as labelled by YouTube (N_videos = 147).

3.2 Data analysis

To develop a codebook, we first explored existing literature regarding the framing of animal agriculture and public attitudes and concerns toward pork production such as concerns related to factory farming, animal welfare, human health, and so forth. After establishing these frames, the research team reviewed a sample of videos to identify any emergent frames not previously considered such as challenges faced by pork producers and entrepreneurship. This process ensured our codebook was exhaustive and reflective of the diverse discourse surrounding the pork industry. The final codebook included the frames: factory farm concerns, animal welfare concerns, human health concerns, environmental concerns, societal concerns, pork production information and technology, challenges faced by pork producers, entrepreneurship, and farming lifestyle. Each frame was coded as either present (1) or not present (0).

To identify the media frames and sentiments of the YouTube videos, we used the video title, video description, and thumbnail to represent the media frame. We manually coded the media frames using the text elements, including the video title, description, and the text overlay on the thumbnail, following the codebook. If a frame was present in either the title, description, or text overlay, the frame was coded as present (1). Sentiments of the text elements were examined using the MAXQDA 24, levels of sentiments ranged from −2 (extremely negative) to 2 (extremely positive) based on the ratio of positive and negative words in the text. To simplify the analysis, we combined all negative sentiments as −1, positives as +1, and neutrals as 0. The visual elements of the thumbnails were coded manually. We took into consideration the color (e.g., bright or dark), facial expression (e.g., sad or joyful), the condition of the facility (e.g., poorly maintained or properly maintained), and the font style of text overlay (e.g., normal or exaggerated). We categorized the sentiments as either negative (−1), neutral (0), or positive (+1).

To analyze the audience frames and sentiments of those videos, we used each video’s top five comments. We first opened each video manually in a Google Chrome browser in incognito mode (incognito mode prevents the storing of any internet cookies or other tracking software potentially biasing subsequent analysis) and selected the top five comments using the Top Comments filter leading to a total of 735 individual comments. The top five comments from each video were aggregated to identify as one, resulting in 147 comments for the frame and sentiment analysis. All comments were retrieved between June 18 to 20, 2024. All comments were coded using the same codebook, methods, and procedure described previously. Sentiment analysis was conducted using MAXQDA 24 as previously described.

Two coders were involved in the manual coding process. The two coders first discussed the definitions of each frame, followed by coding 10% of the data to determine intercoder reliability. Each variable was deemed substantially reliable, with Cohen’s alpha between 0.65 and 0.85 for each variable. Based on this analysis interrater reliability was deemed sufficient given existing guidelines within the literature (Cohen, 1960; McHugh, 2012). Each coder then proceeded to code the rest of the data independently.

SPSS 29.0.2.0 (20) was used for statistical analysis. Descriptive analysis was used to report the frames and the sentiments. Chi-square tests were used to determine the relationship between media and audience frames as well as the media and audience sentiments. When less than five counts were found in a frame, the frame was eliminated from the chi-square test. The effect size was estimated using Phi and Cramer’s V based on Cohen’s (1988) guidelines where, 0.10, 0.30 and 0.50 indicate small, medium and large effect sizes, respectively.

4 Results

4.1 RQ1: primary media frames and sentiments on YouTube videos

We found pork production information and technology (n = 68, 46.26%) as the most abundantly used frame in the titles, description, and text overlay on thumbnails of YouTube videos about the pork industry. This frame demonstrated technology and production methods such as pasture pork farming, building a pig pen, and modern pork processing lines. The second frequently observed frame was entrepreneurship (n = 28, 19.05%). This frame emphasized pork production as an economic venture including tips on how to start a pork business and how to conduct the business. Another prominent frame detected in the videos was animal welfare concerns (n = 27, 18.37%). This frame suggested concerns related to the physical and mental state of pigs in relation to their living conditions and treatment in the farm or industry where languages such as animal “suffering,” “horrific” confinement, and descriptions like pig’s intelligence and “human cruelty” were used. Farming lifestyle (n = 25, 17.01%) was employed as another frame. This frame represented a snapshot of life on a pig farm, tours, and farmers’ pride and hard work. The fifth frame, factory farming concerns (n = 15, 10.2%), depicted pork production practices as “alarming,” large corporations replacing small pig farms or specifically mentioned factory/industry/standard practice related concerns with languages such as “industrial scale intensive pig farming” and “relentless quest of maximum piglet production” (Table 2). The remaining frames, human health concerns, environmental concerns, and social concerns were observed less than five times among the 147 selected videos.

Table 2

Table 2. Primary frames on YouTube videos.

Among the 147 videos, 61 (41.5%) titles exhibited positive sentiments, 35 (23.8%) exhibited negative sentiments and 51 (34.7%) exhibited neutral sentiments. Similarly, 44 (29.9%) video thumbnails demonstrated positive sentiments, 36 (24.5%) demonstrated negative sentiments and 67 (45.6%) demonstrated neutral sentiments (Table 3).

Table 3

Table 3. Sentiments of YouTube videos.

4.2 RQ2: primary audience frames and sentiments in the comments

The most prominent frame detected in the comments of the videos about the pork industry was animal welfare concerns (n = 52, 35.37%). Other frames are pork production information and technology (n = 25, 17.01%), entrepreneurship (n = 19, 12.93%), factory farming concerns (n = 18, 12.24%), and farming lifestyle (n = 14, 9.52%). Although not present in the pre-determined nine frames, we detected many comments sharing appreciation for the videos, mostly for those sharing farm life and technology. Audiences conveyed their gratitude for the information about pork production and technology in the video content.

Of the 147 YouTube video comments, 91 (61.9%) comments expressed positive sentiments, 28 (19.0%) comments expressed negative sentiments, and 28 (19.0%) comments expressed neutral sentiments (Table 4).

Table 4

Table 4. Audience frames and sentiments as detected in top five comments of each video.

4.3 H1: the frames of YouTube videos about the pork industry are associated with audiences’ frames in the comments

To test H1, we compared primary frames detected in YouTube videos and comments. Frames that were observed less than five times in both videos and comments were omitted from the analysis, resulting in seven primary frames (Table 4).

Our results indicated a significant association between media frames (YouTube video titles and thumbnails) and audience frames (the comments) in the use of five out of seven frames. A significant association was found between YouTube videos and audience comments in employing animal welfare concerns (p < 0.001), entrepreneurship (p < 0.001), environmental concerns (p = 0.007), farming lifestyle (p = 0.016), and factory farming concerns (p = 0.022) frames. However, no statistically significant association was observed between media and audience frames in the use of pork production information and technology and challenges faced by pork producers frames. Therefore, H1 was partially supported (Table 5).

Table 5

Table 5. Chi-square comparison of frames in YouTube videos and comments.

The computed phi (Φ) indicated a near large effect size for the percentage difference between the media and audience frames of entrepreneurship (Φ = 0.484) and animal welfare concerns (Φ = 0.457). A medium effect size was found for the environmental concerns (Φ = 0.315) frame, whereas small effect sizes were found for the remaining two frames.

4.4 H2: the sentiments of YouTube videos about the pork industry are associated with audiences’ sentiments in the comments

To test H2, we compared the sentiments of the media frames (YouTube video titles and thumbnails) with those expressed in audience frames (the comments). Our results indicated a significant association between sentiments of YouTube video titles and comments (p < 0.001) (Table 6) as well as between sentiments of YouTube video thumbnails and comments (p < 0.001) (Table 7). The computed Cramer’s V (Φc) indicated a medium effect size for the percentage difference of the sentiments between YouTube video titles and comments (Φc = 0.368) as well as between the video thumbnails and comments (Φc = 0.426). Therefore, H2 was supported.

Table 6

Table 6. Chi-square comparison of sentiments in YouTube video titles and comments.

Table 7

Table 7. Chi-square comparison of sentiments in YouTube video thumbnails and comments.

5 Discussion

YouTube hosts a wide range of perspectives, and our analysis identified frames centered on the pork industry and farmers as the most prominent frames in the videos related to the domain. Specifically, pork production information and technology, entrepreneurship, and farming lifestyle emerged as the leading media frames on YouTube videos about pork industry. These frames conveyed overall positive sentiments about pork production in the video titles. This was largely due to the presence of a large number of channels dedicated to sharing information about pork production, displaying and sharing pig farms and pork production practices (Holt-Day et al., 2020). However, the sentiments conveyed through thumbnails of these frames ranged significantly between neutral and positive. Correspondingly, animal welfare and factory farming concerns are still prevalent, reflecting critical perspectives on pork production practices and the industry overall. The visual elements of the thumbnails representing these frames showed sadness, fear, anger, and disgust toward pork production. This aligned with previous literature and media criticism of animal agriculture from media sources (Specht et al., 2014; Stevens et al., 2018).

The frequency distribution of audience frames does not fully align with that of media frames, even though similar frames are prominent in both cases. Animal welfare concerns stood out in more than one-third of the video comments, emerging as the top audience frame, indicating the prominence of animal welfare concerns regarding pork production and industry among consumers. This aligns with findings by Specht and Rutherford (2013) regarding the public’s sensitivity to ethical issues in animal agriculture, as well as studies by Alonso et al. (2020) and Grunert et al. (2018) that underscored growing consumer concerns over the issues of animal welfare that may be due to intensified production systems. Pork production information and technology, entrepreneurship, factory farming concerns, and farming lifestyle were other audience frames identified within the comments. Although human health, environmental, and societal concerns are commonly discussed in the literature (e.g., Font-i-Furnols et al., 2019; Grunert et al., 2018), the presence of these frames in media and audience frames in this analysis was negligible.

A significant association was found between the media and audience frames, particularly for frames related to animal welfare concerns, entrepreneurship, environmental concerns, farming lifestyle, and factory farming concerns. Similarly, sentiments in both YouTube video titles and thumbnails were significantly associated with sentiments in the comments. This implies the media frames and sentiments set by the YouTube video uploaders were effectively transmitted to the audience, influencing their comments and discussions, following Scheufele’s (1999) framing process model. The results of this study are also consistent with prior findings that highlighted the influential role of social media in shaping public opinions on agriculture (Howard et al., 2017; Rumble and Irani, 2016).

Previous studies have found that emotional appeals in videos serve an important role related to viewer reactions (Brader, 2006; Fischer et al., 2021). This was consistent with the present study, particularly in the negative sentiments associated with animal welfare and factory farming concerns. It is likely that emotional appeals in the examined videos resonated with viewers’ emotions and ethical beliefs, influencing their attitudes. These findings imply the need for strategically framing messages to address public concerns effectively. Transparency and proactive communication about ethical farming practices while highlighting technological advancements and entrepreneurial success in pork production may help to foster an informed public discourse. Creating content that resonates emotionally with the audience while providing factual information can be a powerful strategy. Researching public emotions is an important factor in understanding how individuals process information, particularly in relation to message framing. Emotions significantly impact cognitive processing and receptivity to information, shaping attitudes and behaviors (Kühne and Schemer, 2015; Nabi, 2003). For instance, positive emotions can enhance openness to persuasive messages, while negative emotions, such as fear or anger, often heighten selective attention and memory retention, making emotional framing a powerful tool in communication strategies (Gross, 2008).

6 Recommendation

In this study, we analyzed YouTube video titles, thumbnails, and descriptions for media framing. Future research could expand this by including an examination of full videos to deepen the frame analysis. Future research should also consider cross-platform analyses to offer a more comprehensive understanding of how digital media shapes public discourse around animal agriculture. Social media algorithms are largely based on audience interest, exposing audiences to the content which aligns with their preferences and subsequently creating echo chambers (Güran and Özarslan, 2022). Future studies are recommended to explore whether a randomly selected audience viewing these frames can still result in the same observed interactions and effects. In addition, future studies could also make use of social listening tools to examine audiences and their communities and further explore differences in audiences’ opinions from a variety of perspectives (e.g., demographic, sociographic, geographic, and so forth). Audience segmentation studies may help to better identify segment-based differences and therefore support the development of tailored communication strategies accordingly. Furthermore, this study focused exclusively on the pork industry. Future studies should examine other animal agriculture sectors to reveal whether similar framing patterns and audience reactions occur across different domains of food animal production.

7 Limitations

While this study provides valuable insights into the framing of pork production on YouTube and its influence on audience perceptions, several limitations should be acknowledged. First, the analysis was limited to metadata elements—specifically video titles, descriptions, thumbnails, and user comments. The actual video content, which may offer deeper insights into narrative structure, visual framing, and tone, was not analyzed. Therefore, this study does not fully capture the nuances and depth of the communicative strategies employed in the videos. Second, the use of a binary coding scheme (present/absent) for identifying frames does not fully reflect the complexity, intensity, and layering of communicative cues present in the media and audience discourse that often operates on a spectrum. Third, the influence of YouTube’s recommendation algorithm may have affected video visibility and engagement in ways not accounted for in this study. Algorithmic filtering and amplification could have shaped the prominence of certain frames or sentiments, introducing potential selection biases. Lastly, the platform-specific nature of the research confines the applicability of the findings to YouTube. Other emerging platforms like Instagram, which have different content formats, user demographics, and engagement patterns, may exhibit alternative framing dynamics that merit investigation.

8 Conclusion

This study provides insights into the framing of pork production on YouTube and its impact on public perceptions and discussions. By identifying the prominent media and audience frames and analyzing the associated sentiments, the research highlights the complex interplay between media content and audience reactions. The findings underscore the importance of strategically framing messages to address ethical concerns while promoting transparency, technological advancements, and entrepreneurial successes in the pork industry. By leveraging emotional appeals and aligning sentiments in content with audience values, communicators can effectively engage viewers, counteract misinformation, and foster trust. Incorporating visual and narrative elements that resonate with public concerns—such as ethical farming practices and animal welfare—can help bridge gaps in understanding. Additionally, this study contributes to the broader understanding of social media’s role in shaping public opinion, offering practical implications for communication strategies.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical approval was not required for the study involving human data in accordance with the local legislation and institutional requirements. Written informed consent was not required, for either participation in the study or for the publication of potentially/indirectly identifying information, in accordance with the local legislation and institutional requirements. The social media data was accessed and analyzed in accordance with the platform’s terms of use and all relevant institutional/national regulations.

Author contributions

AK: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. SQ: Conceptualization, Formal analysis, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing, Funding acquisition. FM-K: Writing – review & editing. MR: Writing – review & editing. AL: Writing – review & editing. KL: Writing – review & editing. CS: Writing – review & editing. AB: Writing – review & editing. NG: Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research is a product of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa, project no. IOW05705 and sponsored by the Hatch Act and State of Iowa funds. This research was also supported by the Pork Checkoff, through the National Pork Board.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that Gen AI was used in the creation of this manuscript. The authors used the help of the free version of ChatGPT (GPT-4o mini) to improve the wording and flow of the manuscript. All suggestions made by the tool were reviewed and edited.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

1. ^https://ytdt.digitalmethods.net

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