Understanding what drives popularity is critical in today's digital service economy, where content creators compete for consumer attention. Prior studies have primarily emphasized the role of content features, yet creators often misjudge what audiences actually value. This study applies Latent Dirichlet Allocation (LDA) modeling to a large corpus of TED Talks, treating the platform as a case of digital service provision in which creators (speakers) and consumers (audiences) interact. By comparing the thematic supply of creators with the demand expressed in audience engagement, we identify persistent mismatches between producer offerings and consumer preferences. Our longitudinal analysis further reveals that temporal dynamics exert a stronger influence on consumer engagement than thematic content, suggesting that when content is delivered may matter more than what is delivered. These findings challenge the dominant assumption that content features are the primary drivers of popularity and highlight the importance of timing and contextual factors in shaping consumer responses. The results provide new insights into consumer attention dynamics on digital platforms and carry practical implications for marketers, platform managers, and content creators seeking to optimize audience engagement strategies.

For weeks now, Elon Musk has been preoccupied with worries about how many people are seeing his tweets. Last week, the Twitter CEO took his Twitter account private for a day to test whether that might boost the size of his audience. The move came after several prominent right-wing accounts that Musk interacts with complained that recent changes to Twitter had reduced their reach. On Tuesday, Musk gathered a group of engineers and advisors into a room at Twitter's headquarters looking for answers. Why are his engagement numbers tanking?

Last week, I wrote about how YouTube Algorithm works and the AI workflow behind it. During my research regarding the YT algorithm, I found a really interesting article by Chris Lovejoy where using YT API, he managed to create a personalized recommendation system. Inspired by his thought process and an insightful article, I decided to create my own YT recommendation algorithm using YT APIs. The plan was to create a system that can suggest relevant videos following a personalized plan. The motive was to avoid looking for the best video in a pool of 1000s of videos but rather to get a video that statistically suits my taste.

Wondered how Google comes up with movies that are similar to the ones you like? After reading this post you will be able to build one such recommendation system for yourself. Now you might be thinking "That's interesting. But, what are the differences between these recommendation engines?". Let me help you out with that.

The topic or population polytope (Nguyen, 2015; Tang et al., 2014) is a fundamental geometric object that underlies the presence of latent topic variables in topic and admixture models (Blei et al., 2003; Pritchard et al., 2000). When data and the associated topics are indexed by time dimension, it is of interest to study the temporal dynamics of such latent geometric structures. In this paper, we will study the modeling and algorithms for learning the temporal dynamics of topic polytope that arises in the analysis of text corpora. The convex geometry of topic models provides the theoretical basis for posterior contraction analysis of latent topics (Nguyen, 2015; Tang et al., 2014). Furthermore, Yurochkin & Nguyen (2016); Yurochkin et al. (2017) exploited convex geometry to develop fast and quite accurate inference algorithms in a number of parametric and nonparametric settings.

In the first part of this series, I introduced the Outbrain Click Prediction machine learning competition. That post described some preliminary and important data science tasks like exploratory data analysis and feature engineering performed for the competition, using a Spark cluster deployed on Google Dataproc. In this post, I describe the competition evaluation, the design of my cross-validation strategy and my baseline models using statistics and trees ensembles. In that competition, Kagglers were required to rank recommended ads by decreasing predicted likelihood of being clicked. Sponsored search advertising, contextual advertising, display advertising and real-time bidding auctions have all relied heavily on the ability of learned models to predict ad click–through rates (CTRs) accurately, quickly and reliably.

In this article, we present a method for predicting the view count of a YouTube video using a small feature set collected from a synchronous sharing tool. We hypothesize that videos which have a high YouTube view count will exhibit a unique sharing pattern when shared in synchronous environments. Using a one-day sample of 2,188 dyadic sessions from the Yahoo! Zync synchronous sharing tool, we demonstrate how to predict the video's view count on YouTube, specifically if a video has over 10 million views. The prediction model is 95.8% accurate and done with a relatively small training set; only 15% of the videos had more than one session viewing; in effect, the classifier had a precision of 76.4% and a recall of 81%. We describe a prediction model that relies on using implicit social shared viewing behavior such as how many times a video was paused, rewound, or fast-forwarded as well as the duration of the session. Finally, we present some new directions for future virality research and for the design of future social media tools.