Analyzing User Comments On YouTube Coding Tutorial Videos

Video coding tutorials enable expert and novice programmers to visually observe real developers write, debug, and execute code. Previous research in this domain has focused on helping programmers find relevant content in coding tutorial videos as well as understanding the motivation and needs of content creators. In this thesis, we focus on the link connecting programmers creating coding videos with their audience. More specifically, we analyze user comments on YouTube coding tutorial videos. Our main objective is to help content creators to effectively understand the needs and concerns of their viewers, thus respond faster to these concerns and deliver higher-quality content. A dataset of 6000 comments sampled from 12 YouTube coding videos is used to conduct our analysis. Important user questions and concerns are then automatically classified and summarized. The results show that Support Vector Machines can detect useful viewers\u27 comments on coding videos with an average accuracy of 77%. The results also show that SumBasic, an extractive frequency-based summarization technique with redundancy control, can sufficiently capture the main concerns present in viewers\u27 comments

How May I Impress You? A Content Analysis of Online Impression Management Tactics of YouTube Beauty Vloggers

This research aims at investigating how YouTube beauty vloggers utilize impression management tactics to convey the intended image in an online environment by analyzing their self-presentational behaviors. Two individual coders coded one hundred videos that top trending on YouTube, featuring some single human vloggers who used English as the primary presentation language. Results revealed that first, vloggers had engaged with all four self-presentational behavioral strategies (verbal expressions, nonverbal cues, artifactual displays, and purposive behaviors) in the seemingly amateur videos. Second, a commonly shared feature of top trending vlogs was that they were all designed with abundant and diverting content, indicating that viewers favored the content more than the structure of the vlog. Third, most presenters demonstrated extraverted and likeable personality traits. Fourth, viewers preferred to watch the vlogs with natural props and in simpler environmental settings. Lastly, vloggers chose to use more acquisitive impression management tactics than protective ones in top trending vlogs, and the results showed that viewers also displayed consent to receiving more positive framing

Social Media Influencers: A Route to Brand Engagement for their Followers

Social media has had a significant impact on current branding practices. As a result of this changing environment, brand managers are no longer sole providers of a brand’s communication messages. Specifically, consumers are now interacting with brands, media, and each other through various social media platforms. A distinct group of people who bring brands and consumers together are Social Media Influencers (SMIs). This research proposes that SMIs act as a route to brand engagement. To study this area, I bring together literature on brand engagement and SMIs and determine if and how, SMIs act as a route to brand engagement for their followers. To explore this idea, I used textual consumer comments from YouTube videos and conducted a mixed methods research design, with a focus on Automated Textual Analysis. Results revealed that SMIs do indeed act as a route to brand engagement for their followers. It is through the trust and honesty that already exists between an influencer and their followers that their followers seek out and consider the Influencer’s specific product recommendations. Their followers then take and use these recommendations when making their own purchasing decisions. These results not only advance theory in this area, but also have managerial implications regarding best practices for brand managers working with SMIs

Beyond Traditional Feedback Channels: Extracting Requirements-Relevant Feedback from TikTok and YouTube

The increasing importance of videos as a medium for engagement, communication, and content creation makes them critical for organizations to consider for user feedback. However, sifting through vast amounts of video content on social media platforms to extract requirements-relevant feedback is challenging. This study delves into the potential of TikTok and YouTube, two widely used social media platforms that focus on video content, in identifying relevant user feedback that may be further refined into requirements using subsequent requirement generation steps. We evaluated the prospect of videos as a source of user feedback by analyzing audio and visual text, and metadata (i.e., description/title) from 6276 videos of 20 popular products across various industries. We employed state-of-the-art deep learning transformer-based models, and classified 3097 videos consisting of requirements relevant information. We then clustered relevant videos and found multiple requirements relevant feedback themes for each of the 20 products. This feedback can later be refined into requirements artifacts. We found that product ratings (feature, design, performance), bug reports, and usage tutorial are persistent themes from the videos. Video-based social media such as TikTok and YouTube can provide valuable user insights, making them a powerful and novel resource for companies to improve customer-centric development

Classifying YouTube Comments Based on Sentiment and Type of Sentence

As a YouTube channel grows, each video can potentially collect enormous amounts of comments that provide direct feedback from the viewers. These comments are a major means of understanding viewer expectations and improving channel engagement. However, the comments only represent a general collection of user opinions about the channel and the content. Many comments are poorly constructed, trivial, and have improper spellings and grammatical errors. As a result, it is a tedious job to identify the comments that best interest the content creators. In this paper, we extract and classify the raw comments into different categories based on both sentiment and sentence types that will help YouTubers find relevant comments for growing their viewership. Existing studies have focused either on sentiment analysis (positive and negative) or classification of sub-types within the same sentence types (e.g., types of questions) on a text corpus. These have limited application on non-traditional text corpus like YouTube comments. We address this challenge of text extraction and classification from YouTube comments using well-known statistical measures and machine learning models. We evaluate each combination of statistical measure and the machine learning model using cross validation and $F_1$ scores. The results show that our approach that incorporates conventional methods performs well on the classification task, validating its potential in assisting content creators increase viewer engagement on their channel.Comment: This paper was accepted at 2021 International Conference on Knowledge Discovery and Machine Learning (KDML 2021), but later withdrawn. The paper should be taken as a non peer-reviewed publicatio

#Beautytok going viral

Purpose: The primary goal of this master's thesis is to examine the factors contributing to the virality of beauty product user-generated content (UGC) on TikTok. It is crucial since TikTok is a relatively new social media platform, and having a deeper understanding of it would help consumers, digital marketers, and brands expand their reach on TikTok. Problem statement: Why beauty product-related UGC goes viral on TikTok, and what characteristics do the videos have? Design/methodology/approach: It applies a mixed-method approach that combines qualitative and quantitative analysis. A qualitative content study of 350 TikTok videos is used to create the dataset. This was followed by a quantitative ANOVA analysis in SPSS to determine whether or not the hypotheses were supported. Findings: Five out of nine hypotheses were confirmed. The main findings demonstrated that several characteristics are crucial for creating a viral video. Hypotheses in all three groups, content-, product- and messenger characteristics, were supported. This demonstrates that when creating a TikTok video to go viral, the product should be unique or unusual, be innovative, and solve a problem. Lastly, the messenger of the video should be an expert on the topic. Practical implications: This thesis can be used by marketers to develop more effective marketing plans for TikTok and other social media platforms. Additionally, a deeper comprehension of how current and potential customers respond to online content can help improve and adapt current social media marketing initiatives. This is important for brands that apply social media to market and sell their products. Keywords: TikTok, user-generated content, electronic word of mouth, social media, viralit

#Beautytok going viral

Purpose: The primary goal of this master's thesis is to examine the factors contributing to the virality of beauty product user-generated content (UGC) on TikTok. It is crucial since TikTok is a relatively new social media platform, and having a deeper understanding of it would help consumers, digital marketers, and brands expand their reach on TikTok. Problem statement: Why beauty product-related UGC goes viral on TikTok, and what characteristics do the videos have? Design/methodology/approach: It applies a mixed-method approach that combines qualitative and quantitative analysis. A qualitative content study of 350 TikTok videos is used to create the dataset. This was followed by a quantitative ANOVA analysis in SPSS to determine whether or not the hypotheses were supported. Findings: Five out of nine hypotheses were confirmed. The main findings demonstrated that several characteristics are crucial for creating a viral video. Hypotheses in all three groups, content-, product- and messenger characteristics, were supported. This demonstrates that when creating a TikTok video to go viral, the product should be unique or unusual, be innovative, and solve a problem. Lastly, the messenger of the video should be an expert on the topic. Practical implications: This thesis can be used by marketers to develop more effective marketing plans for TikTok and other social media platforms. Additionally, a deeper comprehension of how current and potential customers respond to online content can help improve and adapt current social media marketing initiatives. This is important for brands that apply social media to market and sell their products. Keywords: TikTok, user-generated content, electronic word of mouth, social media, viralit

Computer-Aided Analysis of Video Comments for Requirements Analysis

In dieser Arbeit werden Anforderungen für die Anforderungsanalyse aus den Youtube Kommentaren von vision videos extrahiert. Der Prozess der Erstellung und Vorbereitung eines Datensatzes wird beschrieben und die Güte von verschiedenen automatisierten Ansätzen wird evaluiert. Die YouTube API wird benutzt um Kommentare zu extrahieren, diese werden dann in Spam bzw. Ham kategorisiert. Die manuelle Klassifikation ist nötig um die Ergebnisse der automatischen zu verifizieren. Um Einsichten in die relevanten Kommentar zu erhalten und spezifischere Kategorien zu finden werden word clouds benutzt. Die gefundenen Kategorien sind Feature Request, Flaw Report, Safety Related, Efficiency Related und manchmal Questions. Für die automatische Klassifikation in die Kategorien Spam / Ham werden die Algorithmen Random Forest, Support Vector Machine, Linear Regression Classifier, Naive Bayes und ein Voting Classifier welcher die ersten drei kombiniert benutzt. Für die Klassifizierung in spezifische Kategorien wird ebenfalls der Voting Classifier verwendet. Für die Analyse der Stimmung werden TextBlob und SentiStrength, und um die relevanten Kommentare zusammenzufassen wird SumBasic benutzt.In this thesis requirements suitable for requirements engineering are extracted from comments below vision videos on the platform YouTube. The process of creating and preparing a dataset is described and the performance of different automated approaches is evaluated. The YouTube API is used to extract the comments, that are then classified into the categories Spam / Ham according to their content and sentiment. The manual classification is necessary to evaluate the results of the automated one. Word clouds are used to get an insight into the content of the relevant comments and decide on more specific categories to classify them according to their content. More specifically the categories Feature Request, Flaw Report, Safety Related, Efficiency Related and sometimes Questions are found. For the automated classification into the categories Spam / Ham the algorithms Random Forest, Support Vector Machine, Linear Regression Classifier, Naive Bayes, and a Voting Classifier that combines the first three are used. To classify comments according to their sentiment TextBlob and SentiStrength are used. For the classification into specific categories, the Voting Classifier is used again. The SumBasic algorithm is used to summarize the relevant comments

Beauty and the Brand: A Digital Ethnography of Social Capital and Authenticity of Digital Beauty Influencers through Monetization Activities on YouTube

This dissertation explored the maintenance of social capital, projection of authenticity, alignment of beauty brands with the brand-as-person, and communicative practices of beauty influencers through a digital ethnography of YouTube beauty community. This research addressed how monetization practices by popular beauty influencers could affect the social constructs required in maintaining their position in the digital community. As beauty influencers continue to gain notoriety and engage in monetization activities through their standing on YouTube, it was important to address how the social practices utilized to build this notoriety were impacted by commoditization of content, toward understanding the sustainability of these practices for influencers and the beauty brands with whom they partner. A digital ethnography, utilizing an inductive content analysis and framework analysis, served as the method by which assessment of influencer projections and viewer reaction, within the cultural confines of the digital community, could be assessed. This research found that influencer projections were all impacted by the type of content. When influencers engaged in sponsored posts, viewers noted disparities in each of the constructs explored in this study, suggesting certain monetization activities can lower reputation engagement in the community. If influencers wish to engage in these monetization practices, they should be upfront with viewers about their intentions, choosing partnerships that are built through time and consistency. In doing so, influencers then are able to situate themselves as being genuine and honest with viewers, cementing their status in the community, while still benefitting personally and financially from monetization activities