Going Viral: An Analysis of Advertising of Technology Products on TikTok

Social media has transformed the advertising landscape, becoming an essential tool for reaching and connecting with consumers. Its sharing and engagement features amplify brand exposure, while its cost-effective options provide businesses with flexible advertising solutions. TikTok is a more recent social media platform that has gained popularity for advertising, particularly in the realm of e-commerce, due to its large user base and viral nature. TikTok had 1.2 billion monthly active users in Q4 2021, generating an estimated $4.6 billion revenue in 2021. Virality can lead to a massive increase in brand exposure, reaching a vast audience that may not have been accessible through traditional marketing efforts alone. Advertisements for technological products are an example of such viral ads that are abundant on TikTok. The goal of this thesis is to understand how creators, community activity, and the recommendation algorithm influence the virality of advertisements for technology products on TikTok. The study analyzes various aspects of virality, including sentiment analysis, content characteristics, and the role of influencers. It employs data scraping and natural language processing tools to analyze metadata from 2,000 TikTok posts and 274,651, offering insights into the nuances of viral tech product advertising on TikTok

Affinity Paths and Information Diffusion in Social Networks

Widespread interest in the diffusion of information through social networks has produced a large number of Social Dynamics models. A majority of them use theoretical hypothesis to explain their diffusion mechanisms while the few empirically based ones average out their measures over many messages of different content. Our empirical research tracking the step-by-step email propagation of an invariable viral marketing message delves into the content impact and has discovered new and striking features. The topology and dynamics of the propagation cascades display patterns not inherited from the email networks carrying the message. Their disconnected, low transitivity, tree-like cascades present positive correlation between their nodes probability to forward the message and the average number of neighbors they target and show increased participants' involvement as the propagation paths length grows. Such patterns not described before, nor replicated by any of the existing models of information diffusion, can be explained if participants make their pass-along decisions based uniquely on local knowledge of their network neighbors affinity with the message content. We prove the plausibility of such mechanism through a stylized, agent-based model that replicates the \emph{Affinity Paths} observed in real information diffusion cascades.Comment: 11 pages, 7 figure

Success Factors in Mobile Viral Marketing: A Multi-Case Study Approach

A prior study showed that mobile viral marketing is an important issue of mobile marketing. Using a multicase study research approach, we introduce a typology of four standard types of mobile viral marketing and extract eight success factors for this new form of marketing. As a final step, we structure the relationship between both, showing success factors’significance in different standard types and deriving a success factor framework. We conclude with a consideration of research implications.