Reflections on a Novel Method for Exploring Audience Reception of Fictional “Vlogs”

Introduction The notion of authenticity has gone hand in hand with research about vlogs since YouTube launched in 2005, and it maintains scholarly relevance two decades later. While authenticity is touted as integral to success in the vlogging space, many factors complicate the matter, including the mediation and commercialisation at play (Strangelove; Cunningham Kennedy). Existing research highlights that vloggers and influencers create, perform, and maintain authenticity online (Marwick; Usher; Abidin; McRae), and the audience’s perception of this authenticity is key. Importantly, the factors which make vlogs seem authentic – such as direct address, amateur production values, and conversational qualities (Tolson 286) – are easily imitable, which makes the format rife for reproduction. The vlog format has notably been imitated in fictional content through popular Web series such as Lonelygirl15 (2006-2008), The Lizzie Bennet Diaries (2012-2013), and Carmilla (2014-2016), and to date many researchers have discussed the impact and effects of imitating the seemingly authentic format (Senft; Burgess and Green; Seymour, Roth, and Flegel; Bakioğlu; Crape). Additionally, the emergence of virtual influencers and AI-generated audiovisual content prompts renewed discussions about how audiences respond to vlogs and how they perceive the authenticity of the format. The virtual influencer/”robot” Miquela (@lilmiquela), for example, demonstrates how the vlogging format is utilised in novel ways through her breakup vlog on YouTube, which ultimately promotes new music to her audience. The ongoing cultural relevance of vlogs means researchers must continue to refine methods for exploring audience reception to vlogs (both “real” and otherwise) to ensure accurate, holistic, and up-to-date knowledge. In this article, I outline the methodology used in my doctoral thesis (Adams “Media”) to explore audience reception of user-generated vlogs (standard vlogs) and fictional content which imitates vlogging conventions (mimic vlogs). This methodology is applicable to other research which aims to understand how audiences perceive factors such as authenticity, trustworthiness, and fictionality in diverse audiovisual content. I begin by discussing the role of authenticity in vlogs before explaining the method I utilised. I then reflect on the implications of different choices I made during its implementation and the strengths and weaknesses of the approach. Finally, I outline how this method is applicable to other audiovisual content, including how it is of particular use to other researchers in the vlogging space. Continued research focussing on how audiences perceive authenticity in vlogs and other online content is a crucial priority in media and audience studies, particularly in the “post-truth” era with the technological advancement and proliferation of AI-generated audiovisual content. This method offers one approach to exploring these complex topics through the eyes of the audience. Authenticity and Vlogs Before exploring the methodology, it is worth re-iterating why researching audience perceptions of vlogs is complex. While the role of authenticity in vlogs has been studied extensively, the concept itself is difficult to define (Banet-Weiser 26). Theo van Leeuwen notes that authenticity is ultimately an “evaluative concept” with many definitions depending on the application (392). What makes a vlog authentic is both subjective and inconsistent. As one example, we can consider linguistic errors in vlogs, such as when vloggers mispronounce a word or use awkward phrasing. Anne Jerslev notes that including linguistic errors in the body of a vlog shows spontaneity, which can be a marker of authenticity (5243). Conversely, speakers who remove all errors may come across as scripted and thus less authentic. Further complicating matters, Aditi Bhatia states that including errors as outtakes implies that the body of the vlog is not spontaneous (112), which could reduce the impression of authenticity, but equally, there is an argument that suggests the inclusion of “blooper reels” can help make vloggers more relatable by demonstrating behind-the-scenes moments even if they are a form of “strategic intimacy” or “calibrated amateurism” (Bishop; Marwick in Bishop; Abidin). These contradictions highlight that authenticity is subjective, contextually dependent, and open to audience interpretation. Implicit here is the role of mediation in vlogs. Greg M. Smith states that inherent to all media is a “distortion” of reality. He contends that in attempting to represent reality “media makers must select which things to include, which to condense, and which to leave out” (Smith 166). Vlogs are certainly not immune to this mediation (Lange 56-62). Existing scholarship on vlogs emphasises that the distinction between what is and is not authentic in vlogs is already blurred (Strangelove; Cunningham and Craig; McRae; Kennedy). In practice, vloggers control how they present themselves in their content by choosing what to include and exclude, which is a form of impression management in line with Erving Goffman’s dramaturgical theory. Vloggers and audiences alike are aware of the mediation at play in user-generated content, though the perceived authenticity of the vlogger is still important because their success, and commercial partnerships, depend on it (Ault in Ashton and Patel 161; Abidin; Kennedy). This dependency is because performing authenticity is a form of labour which accumulates socio-cultural capital for vloggers, and this capital can translate into monetary gain through ad deals and brand partnerships (McRae 16-17). As such, researchers interested in audience reception of vlogs must ensure their methods effectively capture the diverse ways audiences may respond to different types of content. Some methods, such as surveys or content analysis of YouTube comments, are limited in how well they can speak to different aspects of audience perspectives. It is therefore important to speak to audiences, using qualitative mixed-methods approaches, to gain deeper insights. Thus, an audience reception approach is essential to ensure participants are able to express their own understanding of a text. The Method I designed this method to explore how audiences perceive and identify standard vlogs and fictional mimic vlogs. The research explored how authentic participants believed individual vloggers to be, how participants categorised videos as either a standard vlog or a mimic vlog, and what factors participants used to determine this (Adams “It’s So”; Adams “Media”). Questions around how users make sense of vlogs, their content, and evaluations of their authenticity are inherently interpretive in nature, and this is in keeping with audience studies and media reception theory approaches. Janet Staiger asserts that media reception studies “is not a hermeneutics or truth-finding of the meanings of the text. … It asks, How does a text mean? To whom? In what circumstances?” (2). Sonia Livingstone similarly states “reception studies argues that media texts must also be interpreted, made sense of, worked on by their audiences” (1). As such, reception studies is an interpretivist approach which allows for viewers of media to draw different meanings from texts depending on their own understanding of the text, each of which is equally true (Anderson 17). Audience reception studies engages with several methods and researchers often combine these to create mixed-method approaches with complementary datasets (Kitzinger 173; Bertrand and Hughes 39; Tuominen 70). Methods such as viewing exercises and interviews are key approaches in reception studies but have their limitations (Kitzinger 176). With this in mind, I constructed a mixed-methods approach which offered multiple ways to garner participants’ responses to both standard vlogs and mimic vlogs. The method comprised of questionnaires, a viewing task, and qualitative interviews. While each approach is limited in its own way, the combination of methods develops a detailed dataset to draw from. Each participant in my study undertook an individual session comprised of four stages. In Stage 1 of the session, participants completed a questionnaire providing general information about themselves and their YouTube viewing habits, including questions about how frequently they watched YouTube, on what devices, and what kinds of videos they consumed. Stage 2 comprised of a viewing task in which participants watched a corpus of eight videos, featuring both standard user-generated vlogs and fictional mimic vlogs selected from Web series. After each of these videos, the participant filled out a standardised questionnaire responding to the video, before moving on to the next video. Questions in this stage focussed on audience responses to the person in the video, including: “Describe the person featured in this video in three words” and “How authentic do you feel the person in this video was being?” The viewing exercise provided a baseline response to each of the videos, which I used to contextualise different responses as the session progressed. This exercise also ensured that all participants were discussing the correct medium and content during the study, especially as some of the participants were not regular consumers of vlog content. Interviews alone may not have guaranteed this. Importantly, at this point participants did not know that some of the videos were fictional. Stage 3 is the point at which I informed the participants about the fictional nature of some of the videos. Participants were then asked to categorise the videos as either a standard vlog or a mimic vlog and provide a short reason as to why they chose this category for each video. The categorisation task also included some reflective questions asking participants to explain their general approach to categorising the videos. The reflective questions asked participants which elements of the video and surrounding paratexts signified reality or fiction, how difficult they found the task, and how accurate they thought their categorisations were. The two-pronged explanation for how participants categorised the videos at this stage allowed for both a nuanced answer to the specific videos as well as discussion of the broader approach to judging the authenticity of YouTube videos. Stage 4 involved a semi-structured interview to discuss each participant’s perceptions about vlogging, storytelling on YouTube, and the concept of authenticity, as well as further reflection on the videos and categorisation tasks. This section also included a series of questions aimed at understanding how participants perceived mimic vlogs in relation to other content which imitates seemingly authentic formats, such as mockumentaries and deep fakes. Together, these four stages created a detailed set of data about each participant’s perspectives and approaches to assessing the authenticity and fictionality of video content. Notably, the recruitment material did not ask for participants who were experts in vlogs. To take part in the study, participants needed to self-identify as frequent YouTube viewers (more than once per week), fluent in English, aged between 18 and 34 years, and located in Adelaide, South Australia. Participants did not need to specifically watch vlog content or be familiar with the vloggers included in the corpus of videos. These participants are users who understand how YouTube as a platform functions but might not routinely interact with vlogs. When selecting the corpus, I purposefully chose less popular vloggers and Web series, so participants were less likely to have an existing opinion of the vlogger. This “non-specialist” audience meant that the participants were able to respond to speakers for the first time, with the exception of one participant who was familiar with one of the standard vloggers. Considerations and Reflections One of the key considerations in constructing this method was deciding how to present the audiovisual content to participants. Given the research was interested in how audiences discerned different types of content, it was important to consider the impacts of how the content was shown to participants and what the resultant data were able to reflect. Paratexts influence perceptions about a text but are not a part of the text itself; rather, they exist in an “undefined zone” between the text and the world outside the text (Birke and Christ 68; Genette 2). On YouTube, paratexts include the channel name, the channel’s display picture, the description, comments from other users, and the suggested videos, amongst other factors. These paratexts are key features that participants could highlight as markers of authenticity or fictionality in their discussions. The way content is shown to participants influences the paratextual information available, which in turn affects the outcomes of the research. To recreate a typical viewing, I ensured key YouTube paratexts were included by loading the video on YouTube in a browser, starting the video in the default view, and then entering full screen. This method allowed participants to see some paratextual markers but reduced the number of paratextual elements participants could see throughout the whole video. The video was left in full screen at the conclusion of the video while participants filled out their questionnaire responses, which showed the video’s name and recommended videos. This overall viewing experience allowed the participants to take in some of the paratextual information if they were paying attention to it, but these elements were not over-emphasised. I decided this was the best compromise to navigate this viewing experience without adversely affecting the experience by making paratexts too obvious or not obvious enough. In the same vein, whether the participants could control the mouse was an important consideration. While participants would have more agency over their viewing experience if they were in control of the mouse (thus meaning it would likely be more accurate to their typical viewing experience), there was a risk that participants would encounter comments which overtly referenced the fictional nature of the mimic vlogs included in the corpus. Doing so would cause problems with the data if some participants noticed references to the fictional nature of some of the videos. Conversely, ensuring participants did not encounter the comments limited the participants’ access to certain paratextual information. What remained were the paratextual markers made largely by producers of the text, and thus capable of signifying fictionality if participants were paying attention to them (an example of this is the phrase “Ep 3” in the title of one of the mimic vlogs, which signified the video was episode three of the Web series). Removing the possibility for participants to see the comments also ensured all participants engaged in the same way, meaning that the results were more comparable across the board. Some participants stated both formally (via questionnaires) and informally (aloud while completing the questionnaires) that they wanted to control the mouse and were intrigued to look at the comments. Researchers who utilise the method should consider whether they allow participants to navigate the screen as they normally would, and contemplate the impact this might have on paratextual information offered to participants. The impact of this choice will differ depending on the precise research aims. Another key component of this study was the need for limited disclosure. I wanted to capture data of participants’ genuine initial response to the videos before they knew some were fictional. As such, I informed participants prior to their participation in the study about the general area of the research (i.e., that the research is looking at vlogs and YouTube), but they were not told that the key area of interest was mimic vlogs and fictional content which imitated user-generated formats. It was only at Stage 3, after participants had initially watched and responded to the videos, that they were informed that some of the videos were mimic vlogs and thus fictional in nature. This omission was to ensure that participants could answer questions about their perceptions regarding the authenticity of the videos in Stage 2 without the influence of knowing that some of the videos were fictional, thus minimising potential expectancy effects. On the whole, participants had varying levels of success in categorising the videos, with participants collectively categorising 61% of the videos correctly. Limited disclosure is a significant ethical consideration but was an important element that ensured participants did not divert their attention from where it would typically go. I gained ethics clearance through my institution and mitigated risk to participants by avoiding videos which might elicit distress in the viewers. I routinely highlighted to participants that the research was more focussed on how participants perceived the videos rather than whether they were “right”. In practice, the limited disclosure had no discernible negative effects on participants and instead prompted useful discussions about whether the reveal was a “surprise” or not. This approach allowed me to explore notions of “gut feelings” towards the videos, as some participants stated they suspected something was “off” about some of the videos. These “gut feelings” were particularly interesting to explore as participants often utilised subjective factors (such as their intuition about videos) to help them identify videos, rather than more tangible factors such as the aforementioned paratextual information. Participants’ use of “gut feelings” to decide whether a video was fictional relates to the concept of fluency. Fluency suggests that factors such as familiarity and repetition inform whether people perceive something to be truthful. Lewandowsky et al. state that “fluently processed information feels more familiar and is more likely to be accepted as true; conversely, disfluency elicits the impression that something doesn’t quite ‘feel right’ and prompts closer scrutiny” (112). One of the two participants who listed vlogs as being a format they frequently consumed was the only participant to correctly identify all eight videos. Importantly, this participant had also previously watched The Lizzie Bennet Diaries, meaning she was familiar with mimic vlogs as a format. This finding suggests that familiarity with the genre may play a part in audience members’ capacity to identify fictional content. That said, the other participant who also noted that she frequently consumed vlogs only correctly categorised four of the eight videos. Thus, more research is necessary to explore this aspect in more depth. In terms of the strengths and weaknesses of the method, one major strength is the richness of the data it creates. The mixed-methods approach provides researchers with information regarding the participants’ broad viewing habits, their initial reactions to the videos, comparable data detailing how participants categorise videos, and qualitative interview transcripts exploring how participants define terms and perceive the videos more broadly. This level of detail allows for cross-referencing of information and participant approaches, which promotes insightful findings that any one of these methods alone would not provide. For example, I was able to ask each participant what “authentic” meant to them, which allowed my results to speak to the subjectivity of the term. Participants defined authenticity in three ways: as vloggers “being themselves”, being spontaneous, or connecting with the audience. These distinctions in definitions were then useful to consider while comparing how participants rated the authenticity of different vloggers against whether they categorised the videos as standard vlogs or mimic vlogs. Interestingly, one of my findings showed that participants typically (though not exclusively) rated the vloggers in mimic vlogs as less authentic than those in the standard vlogs, even before they knew that some of the videos were fictional. Thus, understanding how the participants defined authenticity was important. The combination of information allowed for a deeper analysis of each participant’s responses, and this level of nuance would not have been possible through surveys or questionnaires alone. That said, a limitation of this method is that it is time-intensive for participants and researchers alike. Each participant’s session took around two hours, and the combination of methods required working with multiple types of data in the analysis stage. With only eight participants (largely due to COVID-19 restrictions in place at the time of recruitment), my overall dataset was workable despite the large amount of data per participant. However, the limited number of participants means the results of the study are not generalisable. As with most reception studies methods, this research was never intended to be representative (Hermes 383), which is a limitation of the approach. Researchers who utilise this approach should consider the suitability of the approach for their research aims, available resources, and timeframe. Applications While my own research focussed on how audiences identified fictionality and authenticity in YouTube vlogs, this approach has many other applications. This method offers an approach for exploring the intersections between reality, fiction, and authenticity in a variety of audiovisual content including standard user-generated vlogs, synthetic content featuring virtual influencers or generative AI, and user-generated content on other platforms such as TikTok and Instagram Reels. The viewing exercise followed by questionnaires, categorisation tasks, and interviews may be adapted as required, but together offer a detailed dataset to analyse. In my own work, this combination of approaches allowed for detailed discussion about what counts as “authentic” in standard vlogs even in contrast to overtly fictional content. For example, some of my participants questioned just how “authentic” a standard vlog is when the vlogger chooses what to include and exclude from their vlogs. This finding is in keeping with existing research which suggests the line between the “real” and the “fake” in vlogs is already blurry (Strangelove; Cunningham and Craig; Abidin; McRae; Kennedy). A version of this method would be one way to further examine audience perceptions of authenticity on YouTube and in vlogs. Additionally, while virtual influencers are appearing on YouTube and other social media platforms, research to this point has largely focussed on their role in marketing and advertising (see Byun and Ahn for a systematic review). Further research could apply this method to explore audience perceptions of virtual influencers across different platforms and formats. Despite the fact that scholars have been researching vlogs on YouTube for twenty years, work in this space is not complete. The proliferation of synthetic content on vlogging platforms means that there is an urgent need to explore how audiences assess, identify, and respond to “fake” content. 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