Wall Street’s Content Wars: Financing Media Consolidation

If we frame the ongoing streaming transition occurring in the cultural industries as ‘content wars,’ with metaphoric ‘battlefronts’ in Hollywood, in Silicon Valley, and on Madison Avenue, then the silent arms dealer in this conflict is Wall Street and the investor class, whose financial engineering goes largely unacknowledged in studies of the media industries. This chapter will explore the impact of private equity in the American film, television, and music industries since 2004. The mercenaries of these content wars, private equity firms have enacted leveraged buyouts in every sector of the cultural industries: major music labels (Warner, EMI), radio networks (Cumulus, Clear Channel/iHeartMedia), film and television production and distribution companies (MGM, Miramax, Univision, Dick Clark Productions), exhibition (AMC, Odeon), the top talent agencies (CCA, WME, IMG), audience measurement (Nielsen), and the trade press (Variety, The Hollywood Reporter, Billboard). The arms race in this conflict is the ability to monetize content catalogues across streaming platforms, which is a lucrative opportunity for financialization. From a critical political economy of media perspective attuned to the significance of financial capital, this chapter demonstrates that the financialization of various components of the media sector is facilitating a dramatic extraction of value from the cultural industries, leaving further consolidation in its wake. Who is profiting from the streaming transition and who is losing out? The answers are the same as in the wider economy of the second gilded age: the wealthy are extracting private, untaxed profit from the public arena while the middle class of creatives is being hollowed out. The ‘creative destruction’ of this war is being fueled by financial engineering

Matchmakers or tastemakers? Platformization of cultural intermediation & social media’s engines for ‘making up taste’

There are long-standing practices and processes that have traditionally mediated between the processes of production and consumption of cultural content. The prominent instances of these are: curating content by identifying and selecting cultural content in order to promote to a particular set of audiences; measuring audience behaviours to construct knowledge about their tastes; and guiding audiences through recommendations from cultural experts. These cultural intermediation processes are currently being transformed, and social media platforms play important roles in this transformation. However, their role is often attributed to the work of users and/or recommendation algorithms. Thus, the processes through which data about users’ taste are aggregated and made ready for algorithmic processing are largely neglected. This study takes this problematic as an important gap in our understanding of social media platforms’ role in the transformation of cultural intermediation. To address this gap, the notion of platformization is used as a theoretical lens to examine the role of users and algorithms as part of social media’s distinct data-based sociotechnical configuration, which is built on the so-called ‘platform-logic’. Based on a set of conceptual ideas and the findings derived through a single case study on a music discovery platform, this thesis developed a framework to explain ‘platformization of cultural intermediation’. This framework outlines how curation, guidance, and measurement processes are ‘plat-formed’ in the course of development and optimisation of a social media platform. This is the main contribution of the thesis. The study also contributes to the literature by developing the concept of social media’s engines for ‘making up taste’. This concept illuminates how social media operate as sociotechnical cultural intermediaries and participates in tastemaking in ways that acquire legitimacy from the long-standing trust in the objectivity of classification, quantification, and measurement processes

Shooting ‘Yohani’ to Global Stardom: A Teaching Case of Social Media Strategy of a Top-10 YouTuber

This teaching case is about ‘Yohani’ – a Global YouTube sensation. Yohani’s song ‘Menike Mage Hithe’ reached the top hits of Amazon music and Spotify, eventually making its way to the YouTube Top-10 global charts in September 2021. Gathering in-depth insights from the executive staff behind her record label company and her creative company, this teaching case demonstrates (i) the role of social media strategy in contemporary businesses and entrepreneurs; (ii) social media hygiene factors that one must consider; (iii) how social media insights gained through analytics assisted in delivering a carefully orchestrated business strategy and (iv) how a combination of social media platforms was employed, considering a range of technological, geographical, financial and social factors. The case and its teaching notes are suitable for undergraduate and postgraduate students studying a contemporary information systems management course

MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models

AI-empowered music processing is a diverse field that encompasses dozens of tasks, ranging from generation tasks (e.g., timbre synthesis) to comprehension tasks (e.g., music classification). For developers and amateurs, it is very difficult to grasp all of these task to satisfy their requirements in music processing, especially considering the huge differences in the representations of music data and the model applicability across platforms among various tasks. Consequently, it is necessary to build a system to organize and integrate these tasks, and thus help practitioners to automatically analyze their demand and call suitable tools as solutions to fulfill their requirements. Inspired by the recent success of large language models (LLMs) in task automation, we develop a system, named MusicAgent, which integrates numerous music-related tools and an autonomous workflow to address user requirements. More specifically, we build 1) toolset that collects tools from diverse sources, including Hugging Face, GitHub, and Web API, etc. 2) an autonomous workflow empowered by LLMs (e.g., ChatGPT) to organize these tools and automatically decompose user requests into multiple sub-tasks and invoke corresponding music tools. The primary goal of this system is to free users from the intricacies of AI-music tools, enabling them to concentrate on the creative aspect. By granting users the freedom to effortlessly combine tools, the system offers a seamless and enriching music experience

DIGITAL ASSETS TRANSMISSION BETWEEN ORGANIZATIONS: MUSIC INDUSTRY CASE

This research addresses the following experiences as a contribution to the topic of Blockchain applications. First, the modeling of a Music Industry revenue distribution problem. Second, the Integration of Blockchain platforms and Legacy software. Third, the design of an algorithm that solves the distribution of Digital Assets across organizations within the Music Industry. Ultimately, the analysis of the Performance of Blockchain platforms (Ethereum and Hyperledger) in terms of Latency and Throughput. Additionally, the purpose of the research is to show that the modeling of a Music Industry payment system is possible using Blockchain Technology. Therefore, the old business model of the Music Industry, which possessed flaws and inefficiencies, could potentially change into a trustless environment benefiting all the participants y paying their contributions instantaneously. Moreover, the necessity of a solution is reinforced by an internship experienced in a MITACS project in conjunction with a company called Membran to design and implement a Blockchain solution that shortens the gap between Spotify and the payment to the Labels and Artists. The system distributes value by automatically calculating payments whenever the Digital Assets (Music Tracks revenue) are imported. The application defines specific roles and variables to simulate the Music Industry. For example, Distributors as an entry point and Artists at the end of the chain. Although, any participant within the network can create agreements and benefit from the distribution. The implementation of this research took the Hyperledger Composer framework to use the Hyperledger Fabric Blockchain as the Private Distributed Ledger, and the public Blockchain Ethereum with the Ganache Client for development purposes. Extensive research of the strengths and weaknesses of these technologies included the descriptions of features like the consensus algorithms, modular architectures, and smart contracts. Ultimately, the performance of these technologies compared Hyperledger Composer and Ethereum in terms of Latency and Throughput. The conclusion of this research pointed that Hyperledger Composer with features like the role-based architecture for applications, Programmable ChainCode (Smart Contracts), and Business Network Definitions, is better suitable for modeling customized solutions and outperforms Ethereum in terms of performance when testing the same number of transactions, the same logic of the chain code and the same machine environment

An Effective Cost-Sensitive Convolutional Neural Network for Network Traffic Classification

The volume, and density of computer network traffic are increasing dramatically with the technology advancements, which has led to the emergence of various new protocols. Analyzing the huge data in large business networks has become important for the owners of those networks. As the majority of the developed applications need to guarantee the network services, while some traditional applications may work well enough without a specific service level. Therefore, the performance requirements of future internet traffic will increase to a higher level. Increasing pressure on the performance of computer networks requires addressing several issues, such as maintaining the scalability of new service architectures, establishing control protocols for routing, and distributing information to identified traffic streams. The main concern is flow detection and traffic detection mechanisms to help establish traffic control policies. A cost-sensitive deep learning approach for encrypted traffic classification has been proposed in this research, to confront the effect of the class imbalance problem on the low-frequency traffic data detection. The developed model can attain a high level of performance, particularly for low-frequency traffic data. It outperformed the other traffic classification methods