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
