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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

Hip-Hop librarianship for scholarly communication: An approach to introducing topics

Hip-Hop music, business, distribution, and culture exhibit highly-comparable trends in the scholarly communication and publication industry. This article discusses Hip-Hop artists and research authors as content creators, each operating within marketplaces still adjusting to digital, online connectivity. These discussions are intended for classroom use, where students may access their existing knowledge framework of popular media and apply it to a new understanding of the scholarly communication environment. Research instructors and librarians may discover new perspectives to familiar issues through conversations with students engaging with this material in a novel way

Towards a better understanding of music playlist titles and descriptions

Music playlists, either user-generated or curated by music streaming services, often come with titles and descriptions. Although informative, these titles and descriptions make up a sparse and noisy semantic space that is challenging to be leveraged for tasks such as making music recommendations. This dissertation is dedicated to developing a better understanding of playlist titles and descriptions by leveraging track sequences in playlists. Specifically, work has been done to capture latent patterns in tracks by an embedding approach, and the latent patterns are found to be well aligned with the organizing principles of mixtapes identified more than a decade ago. The effectiveness of the latent patterns is evaluated by the task of generating descriptive keywords/tags for playlists given tracks, indicating that the latent patterns learned from tracks in playlists are able to provide a good understanding of playlist titles and descriptions. The identified latent patterns are further leveraged to improve model performance on the task of predicting missing tracks given playlist titles and descriptions. Experimental results show that the proposed models yield improvements to the task, especially when playlist descriptions are provided as model input in addition to titles. The main contributions of this work include (1) providing a better solution to dealing with ``cold-start'' playlists in music recommender systems, and (2) proposing an effective approach to automatically generating descriptive keywords/tags for playlists using track sequences

Cognitive vs. aesthetic musical experiences : an examination of the relationships between music aptitude and musical preference in third grade students.

The purpose of this study was to measure correlations between third grade students’ music aptitude and preferences for music. Students (N = 60) from two elementary schools in Central Kentucky participated in the study. Students took Gordon’s Intermediate Measures of Music Audiation (IMMA) and a researcher-designed test called the Children’s Music Preference Index. Correlations between IMMA scores and music preference were tabulated using a two-tail bivariate correlation computing a Pearson’s product-moment correlation coefficient. No significant correlations were found between IMMA scores and the overall preference for music (r = -.018). There was an apparent weak negative correlation between aptitude and preference for Rock music (r = -.346). The overall preference score was slightly higher for those with exceptionally high and exceptionally low music aptitude than those with average aptitude. Exceptions of this finding include Rock and Pop, which showed a negative relationship, but not correlation, between strong preference as aptitude scores decreased, and Jazz music, which was rated progressively higher as aptitude scores increased. Suggestions for further areas of research are discussed

Country Music Annual 2002

In the third volume of this acclaimed country music series, readers can explore topics ranging from the career of country music icon Conway Twitty to the recent phenomenal success of the bluegrass flavored soundtrack to the film O Brother, Where Art Thou?. The tricky relationship between conservative politics and country music in the sixties, the promotion of early country music artists with picture postcards, the history of “the voice of the Blue Ridge Mountains” (North Carolina radio station WPAQ), and the formation of the Country Music Association as a “chamber of commerce” for country music to battle its negative hillbilly stereotype are just a few of the eclectic subjects that country music fans and scholars won’t want to miss. Charles K. Wolfe, professor of English and folklore at Middle Tennessee State University, is the author of numerous books, including Kentucky Country. James E. Akenson, professor of curriculum and instruction at Tennessee Technological University, is the founder of the International Country Music Conference. While offering serious ideas and information, it manages to be lively, fun to read, and fairly free of jargon. Most of the articles in this issue deal with how country musicians and the industry sought to be true to [their] roots while testing and seeking to broaden them. —Arkansas Review If you’re into country music, and especially if you enjoy reading about the history behind the music, then this book is worth the read. —Modern Mountain Magazine A book that any true country music lover would want in their collection. —No Depression Offers a wide range of topics examining the socioculutral, political, business, and historical aspects of country music. Although aimed at music scholars, these twelve essays are very readable and should capture the interest of serious country music fans as well. —Register of the Kentucky Historical Societyhttps://uknowledge.uky.edu/upk_music/1005/thumbnail.jp

A comparative analysis of metal subgenres in terms of lexical richness and keyness

Metal music is realized under a vast variety of subgenres all of which have their unique (or shared) characteristics not only in sound but also in their lyrics. Much research has been done to distinguish or classify subgenres but little has addressed the linguistic differences across them. This study seeks to find out the lexical richness and keyness levels of heavy metal, thrash metal and death metal using a corpus of 200 songs from each subgenre with a total of 600 songs. The selection of the bands and songs was carried out finding references in the metal literature. The metal literature in the present study takes into account the academic books and articles on metal as well as noteworthy media productions, websites and metal blogs such as Metal Evolution and Encyclopaedia Metallum. The song lyrics were manually processed and meta-data, mark-ups and repeats have been removed so that the differences in repeat lengths do not affect the comparisons. Furthermore, the analyses used in the study are sensitive to repeats as they measure the frequencies and repeat ratios of the words. The song lengths – after the processing – were limited to lower and upper thresholds of 100 and 400 words. The songs were analyzed for their lexical richness levels in three aspects: 1) lexical variation, 2) lexical sophistication and 3) lexical density. Lexical variation was operationalized as TTR, Guiraud, Uber and HD-D. Lexical sophistication was measured using lexical frequency profile with two different frequency lists – the GSL and the BNC/COCA – by looking at the ratios of tokens and types which fell beyond the most frequent two thousand words (Laufer 1995). Another sophistication measure – P_Lex – which also runs on GSL, was applied. Lexical density analysis was based on the ratio of content words to all tokens in the texts. In order to complement this quantitative and data-driven approach, a keyness analysis was administered to add a qualitative dimension to the research. All lexical richness analyses pointed out to statistically significant differences between all subgenres, marking heavy metal as the least and death metal as the most lexically rich one. Keyness analysis indicated differences among all three subgenres as well. Heavy metal key words tended to be Dionysian whereas thrash and death metal keywords were more Chaotic as proposed by Weinstein (2000). Finally, a correlation analysis showed that all lexical richness measures were statistically significantly correlated to each other. Based on the findings, it could be claimed that 1) these three subgenres differ from each other not only in terms of music but also of lexical richness levels and key words and 2) lexical richness analyses, coupled with keyness, are capable of reflecting the genre differences in song lyrics. However, as a result of a discriminant analysis of the present corpus, a reverse approach whereby genres are attempted to be classified based on lexical features does not provide a pattern which fully corresponds to the existing classifications