#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

Facial re-enactment, speech synthesis and the rise of the Deepfake

Emergent technologies in the fields of audio speech synthesis and video facial manipulation have the potential to drastically impact our societal patterns of multimedia consumption. At a time when social media and internet culture is plagued by misinformation, propaganda and “fake news”, their latent misuse represents a possible looming threat to fragile systems of information sharing and social democratic discourse. It has thus become increasingly recognised in both academic and mainstream journalism that the ramifications of these tools must be examined to determine what they are and how their widespread availability can be managed. This research project seeks to examine four emerging software programs – Face2Face, FakeApp , Adobe VoCo and Lyrebird – that are designed to facilitate the synthesis of speech and manipulate facial features in videos. I will explore their positive industry applications and the potentially negative consequences of their release into the public domain. Consideration will be directed to how such consequences and risks can be ameliorated through detection, regulation and education. A final analysis of these three competing threads will then attempt to address whether the practical and commercial applications of these technologies are outweighed by the inherent unethical or illegal uses they engender, and if so; what we can do in response

Systems Engineering Approaches to Minimize the Viral Spread of Social Media Challenges

Recently, adolescents’ and young adults’ use of social media has significantly increased. While this new landscape of cyberspace offers young internet users many benefits, it also exposes them to numerous risks. One such phenomenon receiving limited research attention is the advent and propagation of viral social media challenges. Several of these challenges entail self-harming behavior, which combined with their viral nature, poses physical and psychological risks for the participants and the viewers. One example of these viral social media challenges that could potentially be propagated through social media is the Blue Whale Challenge (BWC). In the initial study we investigate how people portray the BWC on social media and the potential harm this may pose to vulnerable populations. We first used a thematic content analysis approach, coding 60 publicly posted YouTube videos, 1,112 comments on those videos, and 150 Twitter posts that explicitly referenced BWC. We then deductively coded the YouTube videos based on the Suicide Prevention Resource Center (SPRC) Messaging guidelines. We found that social media users post about BWC to raise awareness and discourage participating, express sorrow for the participants, criticize the participants, or describe a relevant experience. Moreover, we found most of the videos on YouTube violate at least 50% of the SPRC safe and effective messaging guidelines. These posts might have the problematic effect of normalizing the BWC through repeated exposure, modeling, and reinforcement of self-harming and suicidal behavior, especially among vulnerable populations, such as adolescents. A second study conducted a systematic content analysis of 180 YouTube videos (~813 minutes total length), 3,607 comments on those YouTube videos, and 450 Twitter posts to explore the portrayal and social media users’ perception of three viral social media-based challenges (i.e., BWC, Tide Pod Challenge (TPC), and Amyotrophic Lateral Sclerosis (ALS) Ice Bucket Challenge (IBC)). We identified five common themes across the challenges, including: education and awareness, criticizing the participants and blaming the victims, detailed information about the participants, giving viewers a tutorial on how to participate, and understanding seemingly senseless online behavior. We found that the purpose of posting about an online challenge varies based on the inherent risk involved in the challenge itself. However, analysis of the YouTube comments showed that previous experience and exposure to online challenges appear to affect the perception of other challenges in the future. The third study investigated the beliefs that lead adolescents and young adults to participate in these activities by analyzing the ALS IBC to represent challenges with minimally harmful behaviors intended to support philanthropic endeavors and the Cinnamon Challenge (CC), to represent those involving harmful behaviors that may culminate in injury. We conducted a retrospective quantitative study with a total of 471 participants between the ages of 13 and 35 who either had participated in the ALS IBC or the CC or had never participated in any online challenge. We used binomial logistic regression models to classify those who participated in ALS IBC or CC versus those who didn’t with the beliefs from the Integrated Behavioral Model (IBM) as predictors. Our findings showed that both CC and ALS IBC participants had significantly greater positive emotional responses, value for the outcomes of the challenge, and expectation of the public to participate in the challenge in comparison to individuals who never participated in any challenge. In addition, only CC participants perceived positive public opinion about the challenge and perceived the challenge to be easy with no harmful consequences, in comparison to individuals who never participated in any challenge. The findings from this study were used to develop interventions based on knowledge of how the specific items making up each construct apply specifically to social media challenges. In the last study, we showed how agent-based modeling (ABM) might be used to investigate the effect of educational intervention programs to reduce social media challenges participation at multiple levels- family, school, and community. In addition, we showed how the effect of these educational based interventions can be compared to social media-based policy interventions. Our model takes into account the “word of mouth” effect of these interventions which could either decrease participation in social media challenge further than expected or unintentionally cause others to participate

Linking Epidemic Models and Self-exciting Processes for Online and Offline Event Diffusions

Temporal diffusion data, which comprises time-stamped events, is ubiquitous, ranging from information diffusing in online social media platforms to infectious diseases spreading in offline communities. Pressing problems, such as predicting the popularity of online information and containing epidemics, demand temporal diffusion models for understanding, modeling, and controlling diffusion dynamics. This thesis discusses diffusions of online information and epidemics by developing and connecting self-exciting processes and epidemic models. First, we propose a novel dual mixture self-exciting process for characterizing online information diffusions related to online items, such as videos or news articles. By observing that maximum likelihood estimates are separable in a Hawkes process, the model, consisting of a Borel mixture model and a kernel mixture model, jointly learns the unfolding of a heterogeneous set of cascades. When applied to cascades of the same online items, the model directly characterizes their spread dynamics and provides interpretable quantities, such as content virality and content influence decay, as well as methods for predicting the final content popularities. On two retweet cascade datasets, we show that our models capture the differences between online items at the granularity of items, publishers, and categories. Next, we propose novel ideal strategies to explore the limits of both testing and contact tracing strategies, which have been shown effective in some epidemics (e.g., SARS) but ineffective in some others (e.g., COVID-19). We then develop a superspreading random contact network that accounts for the superspreading effect of infectious diseases, where several infected cases result in most secondary infections. In simulations, we observe gaps between ideal and standard strategies by examining extensive sets of epidemic parameters, highlighting the need to explore intelligent strategies. We also present a classification of different diseases based on how containable they are under different strategies. Then, we bridge epidemic models and self-exciting processes with a novel generalized stochastic Susceptible-Infected-Recovered (SIR) model with arbitrary recovery time distributions. We articulate the relationship between recovery time distributions, recovery hazard functions, and infection kernels of self-exciting processes. We also present methods for simulating, fitting, evaluating, and predicting the generalized process. On three large Twitter diffusion datasets, we show that the modeling performance of the infection kernels varies depending on the temporal structures of diffusions and user behavior, such as the likelihood of being bots. We further improve the prediction of popularity by combining two models identified as complementary in the goodness-of-fit tests. Last, we present evently, a tool for modeling online reshare cascades, particularly retweet cascades, using self-exciting processes. This tool fills in a gap between the practitioners of online social media analysis --- usually social, political, and communication scientists --- and the accessibility to tools capable of examining online discussions. It provides a comprehensive set of functionalities for processing raw data from Twitter public APIs, modeling the temporal dynamics of processed retweet cascades and characterizing online users with a wide range of diffusion measures. Overall, this thesis studies temporal diffusions of online information and epidemics by proposing novel epidemic models and self-exciting processes. It provides tools for predicting information popularities, characterizing online items, and classifying online item categories with state-of-the-art performances. It also contributes observations in applying testing and tracing strategies in containing epidemics. Lastly, evently facilitates temporal diffusion analysis for practitioners from various fields, such as social science and epidemiology

Portrait of an Insecure Young Man; An Exploration of the Online Propagation of Mewing

As social media becomes more ubiquitous in our cultural existence as humans it plays an increasingly large role in the formation and expression of identity. On many social media platforms, the way people look is emphasized, with more attractive people garnering the most attention. This results in the phenomenon of upward social comparison, which leads to the lowering of self-esteem. Social media and the internet also enable the spread of information and ideologies, which has resulted in the propagation of pessimistic and misogynistic world views in in communities that make up the manosphere. One popular topic on the manosphere is mewing. Mewing is a concept that claims that people can improve their facial structure by holding their tongue in the proper position on the the roof of the mouth, breathing only through their nose, swallowing and chewing properly, and maintaining good posture. While mewing has the potential to make a positive impact on society, it’s association with the perceptions encompassed in the Manosphere and the resistance from institutional systems of orthodontics to engage in scientific review have hampered its growth to become a staple of western health care

Intentional Technology For Teaching Practice

In today’s era, where educational technology is in a near-constant state of evolution, the imperative is not just to adopt technology, but to do so with a defined purpose and strategy. As educators within military education there is a growing need to discern which technological tools and practices align best with our mission and the goals we set for our students. Teaching is more than just transferring knowledge—it’s about fostering environments conducive to growth, critical thinking, and lifelong learning. This e-book contains collective insights, experiences, and reflections from faculty participating in a Faculty Learning Community (FLC) a yearlong, structured, community of practice, engaged in the thoughtful exploration of educational technology topics during the academic year of 2022-2023 at the Air Force Institute of Technology. Whether by leveraging social annotation tools to engage students in reading, formulating effective methods to produce and utilize educational content, innovating with game-based learning, or seamlessly integrating multiple applications for meaningful classroom experiences, our aim is to provide you with insights and actionable guidance for use within your own classrooms

The Techne of YouTube Performance: Musical Structure, Extended Techniques, and Custom Instruments in Solo Pop Covers

They begin with a note, a chord, the tap of a button, or the triggering of a loop: through progressively layered textures, samples, and extended performance techniques, solo cover songs on YouTube often construct themselves piece by piece before the viewer’s eyes and ears. Combining virtuosity and novelty in a package ready-made for viral online popularity, this recent and rapidly growing internet phenomenon draws together traditions old and new, from the “one man band” of the nineteenth century, to the experimental live looping of 1980s performance art, to contemporary electronic music. Building on a number of recent studies that examine the affordances and restrictions of writing and performing music on various instruments, the case studies in this article explore how these YouTube performers use theoretical and instrumental expertise to convey complex textures through a minimal collection of musical materials. In each case, the instruments themselves are arranged, modified, or even created in order to make these performances possible. These videos often incorporate looped or layered elements, arranged to take advantage of a song’s harmonic or rhythmic structures; and they frequently feature customized, self-created, or otherwise unconventional instrumentation. Through their sparse, economic construction, these intricate arrangements are each the end product of a careful analysis of each song, and they have much to teach us about the harmonic, melodic, and rhythmic structures of popular music

Measuring Collective Attention in Online Content: Sampling, Engagement, and Network Effects

The production and consumption of online content have been increasing rapidly, whereas human attention is a scarce resource. Understanding how the content captures collective attention has become a challenge of growing importance. In this thesis, we tackle this challenge from three fronts -- quantifying sampling effects of social media data; measuring engagement behaviors towards online content; and estimating network effects induced by the recommender systems. Data sampling is a fundamental problem. To obtain a list of items, one common method is sampling based on the item prevalence in social media streams. However, social data is often noisy and incomplete, which may affect the subsequent observations. For each item, user behaviors can be conceptualized as two steps -- the first step is relevant to the content appeal, measured by the number of clicks; the second step is relevant to the content quality, measured by the post-clicking metrics, e.g., dwell time, likes, or comments. We categorize online attention (behaviors) into two classes: popularity (clicking) and engagement (watching, liking, or commenting). Moreover, modern platforms use recommender systems to present the users with a tailoring content display for maximizing satisfaction. The recommendation alters the appeal of an item by changing its ranking, and consequently impacts its popularity. Our research is enabled by the data available from the largest video hosting site YouTube. We use YouTube URLs shared on Twitter as a sampling protocol to obtain a collection of videos, and we track their prevalence from 2015 to 2019. This method creates a longitudinal dataset consisting of more than 5 billion tweets. Albeit the volume is substantial, we find Twitter still subsamples the data. Our dataset covers about 80% of all tweets with YouTube URLs. We present a comprehensive measurement study of the Twitter sampling effects across different timescales and different subjects. We find that the volume of missing tweets can be estimated by Twitter rate limit messages, true entity ranking can be inferred based on sampled observations, and sampling compromises the quality of network and diffusion models. Next, we present the first large-scale measurement study of how users collectively engage with YouTube videos. We study the time and percentage of each video being watched. We propose a duration-calibrated metric, called relative engagement, which is correlated with recognized notion of content quality, stable over time, and predictable even before a video's upload. Lastly, we examine the network effects induced by the YouTube recommender system. We construct the recommendation network for 60,740 music videos from 4,435 professional artists. An edge indicates that the target video is recommended on the webpage of source video. We discover the popularity bias -- videos are disproportionately recommended towards more popular videos. We use the bow-tie structure to characterize the network and find that the largest strongly connected component consists of 23.1% of videos while occupying 82.6% of attention. We also build models to estimate the latent influence between videos and artists. By taking into account the network structure, we can predict video popularity 9.7% better than other baselines. Altogether, we explore the collective consuming patterns of human attention towards online content. Methods and findings from this thesis can be used by content producers, hosting sites, and online users alike to improve content production, advertising strategies, and recommender systems. We expect our new metrics, methods, and observations can generalize to other multimedia platforms such as the music streaming service Spotify