The perception of LGBTQ influencers on Social Media. YouTube Analysis


Research Paper (undergraduate), 2019
19 Pages, Grade: 3.5

Excerpt

Introduction

In the past few years, brands have started catching on to another type of endorser who possesses traits of both celebrities and peers: the social media celebrity (Booth & Matic, 2011). YouTube has 1.57 billion monthly active users, giving businesses the chance to share company content with daily active users who are likely to watch it (LYFE Marketing, 2018). Social media brand influencers are on the rise, especially those who promote lifestyle brands (Glucksman, 2017). Given that there is still stigma and discrimination associated with the LGBTQ community, it’s important to analyze how people perceive and engage with the LGBTQ influencers. A comparative analysis was conducted between LGBTQ and heterosexual influencers to understand the credibility and perception of both communities, testing the source of credibility theory; it explains how communication’s persuasiveness is affected by the perceived credibility of the source of the communication (Hovland et al, 1951).

Methodology

This study was conducted using netnography, this type of research is important to understand cultures and its presence on the Internet. Kovinetz (2015) defined netnography as specific set of related data collection, analysis, ethical and representational research practices where participant-observational approach manifest the data shared on the Internet. Netnography is really important to analyze social media because it allow us to understand different communities and their behavior towards specific topics. The field site for the data collection was the YouTube platform. Since YouTube is a constructed website, I proceed to set the boundaries on how I contextualized my data and what I consider data for this specific project. According to Kovinets (2015), data are considered to be information and they must contain evidence that they are real. On this study, I have archival data from the YouTube platform, comments of brand influencers on specific videos during the time frame from December through February of 2018-2019. The LGBTQ influencers analyzed were Ingrid Nilsen, Bretman Rock, and Nikita Dragun. On the other hand, the heterosexual influencers analyzed were Nash Grier, Camila Coelho, and Marcus Butler.

Ingrid Nielsen videos didn’t generate a great number of comments, I decided to code them all to have a better idea of the perception of this channel. In total, I coded 371 comments. Bretman Rock’s videos generated 19,026 comments and after extracting the duplicates and filters were 16,432 comments. I coded the 10% of the comments, 1,600 comments exactly. This coding process was different from the other users due to the language used by the influencer. Nikita Dragun’s videos generated 42,790 comments, after extracting the duplicates and applying the filters it left me with 13,961 comments. I coded 10% of the comments, 1400 comments. The videos of Nash Grier generated a total of 1094 comments and due to the amount, I decided to code 55% of the comments to have a better understanding of his community. In total, I coded 600 comments. Camila Coelho videos had a total of 118 comments, after generating the exact duplicates I had 114 comments to code. Due to the small number of comments, I decided to code them all. Marcus Butler’s videos generated 589 comments, after extracting the duplicates and filters I end up having 561 comments to code. Procedure

The comments were extracted from the YouTube platform with YouTube comment scrapper. This website allows you to extract information such as comment text, replies, username and date (Klostermann, 2015). After this process, I imported the data to the DiscoverText platform to began my coding process. This platform allows social network text analytics in data science software. According to Shulman (2018), text analytics are computer-assisted techniques to reach valid and reliable insights about a collection text. Once my data was imported, I proceed to extract the duplicates and I filtered by just showing me comments that used English as a language. After the filter, I began coding my comments using binary coding. The binary code was invented by Leibniz (1689) and according to Computer Hope, it is a coding system that uses binary digits to represent letters, digits or other characters found in a computer. My codes were positive, negative and neutral.

I considered positive comments as texts that included feedback about the products, opinions about the videos quality and recommendations. Emotional, motivational and inspiring comments towards the influencer were considered positive. Another type of comments that were analyzed were the ones that just encountered emojis. Emojis have evolved into characters for a new millennial language (Khalaf, 2017). The negative comments are texts that present deceptive comments about the videos or the channel itself. Emojis were also analyzed but the most important thing was the presence of bullying comments and the perception users had towards the products and their prices. The neutral comments are texts that encounter a positive and negative context in the same sentence. Personal decisions and opinions, quoting part of the videos, minute references, and questions that weren’t related to the video or the influencer were considered neutral.

After coding the comments, I generated a word cloud for each one of them focusing on the top twenty-five words found among the comments. Word clouds are a graphical representation of frequent words that offer a greater prominence to words that appear more frequently in a source text (Feinberg, 2013). Also, I exported my data and imported it to the Gephi platform. On this platform, I generated the graphics to see the connection between the users and the relevance of them within the network. The weight and degree of the different nodes and edges can be perceived through the change of colors they present.

Sample

Since my data are YouTube comments, I picked the two videos with more views of each influencer. Ingrid Nilsen represents the lesbian community and her videos are mostly based on beauty products. The videos I analyzed were “2018 Favorites: Makeup & SkinCare” and “What’s in my bathroom cabinet?”. In total, between the two videos, I collected 454 comments. To represent the gay community I chose Bretman Rock, he focuses on beauty products and sometimes dress like a drag queen. A drag queen is usually a gay-identified man that dresses as a woman and performs as an entertainer to caricature stereotypically vampish women (Webster, 1941). The videos analyzed were “Doing and reviewing my makeup litty - a mess” and “Bretman Rock x Colourpop wet and lit collection”, between the two videos 19,026 comments were collected. To represent the transgender community I chose Nikita Dragun, she usually focuses on make-up tutorials but tends to create videos sharing personal stories of her process as a self-identified transgender person. The videos analyzed were “I got kicked out for being transgender” and “Celebrity make up artist does my makeup”, between the two videos 42,790 comments were gathered.

On the other hand, the heterosexual influencers had different focuses. Nash Grier videos are more focused on life experiences he shares with his community. The videos analyzed were “We’re not pregnant” and “Donating my hair”, between the two videos 1,345 comments were collected. To represent beauty products, I chose Camila Coelho English version YouTube Channel. The videos analyzed were “Favorites of 2018” and “Top 5 matte foundations”, between the two videos 473 comments were collected. My last influencer is Marcus Butler, his videos are more focused on comedy than focusing on a specific brand itself. The videos analyzed were “Why my YouTube channel died?” and “Strip challenges”, between the two videos 656 comments were gathered.

Results

This comparative analysis of both communities focused on answering my research questions:

RQ1: What type of information is included in the videos posted by LGBTQ and heterosexual influencers on YouTube?

RQ2: How do the users respond to content posted by beauty vloggers?

RQ3: Does sexual orientation of the influencer impact engagement with their content? LGBTQ Influencers

Ingrid Nielsen

Between the two YouTube videos, 63% of the comments were positive, 30% were neutral and 7% were negative. This can be seen more specifically in Appendix 1, this pie chart represents the coding and the relevance for this study. The positive comments of this YouTube channel focused on users giving and requesting feedback of products, the quality of the video, supportive comments towards the channel and emotional comments such as “I love you” or “You inspire me”. The neutral comments focused more on questions about the clothes she was wearing and their stories about using the products she recommends. The negative comments focused on discussing changes in her appearance and the high cost of the products she recommended. In appendix 2, we can see that most of the words are positive ones which have a strong connection with the results and coding generated. The graph of this YouTube channel didn’t present a strong connection between the nodes and edges. It presented a network diameter of one and a density of 0.002. Also, it presented 34 connected components In appendix 3, we can see the connection between the community. The network diameter focuses on the distance between two network participants and the density focuses on the total of ties in a network (Hansen, 2010).

Bretman Rock

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Excerpt out of 19 pages

Details

Title
The perception of LGBTQ influencers on Social Media. YouTube Analysis
College
Rochester Institute of Technology
Course
Social Media Research
Grade
3.5
Author
Year
2019
Pages
19
Catalog Number
V494224
ISBN (eBook)
9783668981805
ISBN (Book)
9783668981812
Language
English
Tags
LGBTQ, influencers, marketing, community, engagement
Quote paper
Stephanie Sabala (Author), 2019, The perception of LGBTQ influencers on Social Media. YouTube Analysis, Munich, GRIN Verlag, https://www.grin.com/document/494224

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