Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world's second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science Conference (WebSci 2020). Please cite the WebSci version; Second version includes corrected typo

Describing and Forecasting Video Access Patterns

Computer systems are increasingly driven by workloads that reflect large-scale social behavior, such as rapid changes in the popularity of media items like videos. Capacity planners and system designers must plan for rapid, massive changes in workloads when such social behavior is a factor. In this paper we make two contributions intended to assist in the design and provisioning of such systems.We analyze an extensive dataset consisting of the daily access counts of hundreds of thousands of YouTube videos. In this dataset, we find that there are two types of videos: those that show rapid changes in popularity, and those that are consistently popular over long time periods. We call these two types rarely-accessed and frequently-accessed videos, respectively. We observe that most of the videos in our data set clearly fall in one of these two types. For each type of video we ask two questions: first, are there relatively simple models that can describe its daily access patterns? And second, can we use these simple models to predict the number of accesses that a video will have in the near future, as a tool for capacity planning? To answer these questions we develop two different frameworks for characterization and forecasting of access patterns. We show that for frequently-accessed videos, daily access patterns can be extracted via principal component analysis, and used efficiently for forecasting. For rarely-accessed videos, we demonstrate a clustering method that allows one to classify bursts of popularity and use those classifications for forecasting

Integration of informal music technologies in secondary school music lessons

date-added: 2011-08-12 11:03:06 +0100 date-modified: 2011-08-12 11:03:38 +0100date-added: 2011-08-12 11:03:06 +0100 date-modified: 2011-08-12 11:03:38 +0100This project was supported by EPSRC grant EP/I001832/1, ‘Musicology for the masses’

ImTV: Towards an Immersive TV experience

3rd International Workshop on Future Television: Making Television Integrated and Interactive, Adjunct Proceeding of EuroiTVThe media marketplace has witnessed an increase in the amount and types of viewing devices available to consumers. Moreover, a lot of these are portable, and offer tremendous personalization opportunities. Technology, distribution, reception and content developments all influence new 'television' viewing/using habits. In this paper, we report results and findings of a transnational three year research project on the Future of TV. Our main contributions are organized into three main dimensions: (1) a user survey concerning behaviors associated with media engagement; (2) technologies driving the social and personalized TV of the 21st century, e.g. crowdsourcing and recommendation systems; and (3) technologies enabling interactions and visualizations that are more natural, e.g. gestures and 360º video.info:eu-repo/semantics/publishedVersio