Music virality on social platforms: A literature review

With the exponential growth of various network resources, the use of search engine has become one of the most basic skills of everyone in today's society, and an efficient information retrieval model is also of more significance. The traditional text-based music information retrieval method can retrieve music data by inputting text information such as song name, composer, singer and album name. The content-based music information retrieval queries the target music through the input music melody information. In the actual music information retrieval scene, there is interaction between the user and the retrieval model. The user gives feedback on the retrieval results, and the retrieval model returns a new page of document according to this feedback. The existing ranking learning model regards ranking as a one-time process, ignores user feedback, and the ranking effect needs to be improved. With the increasing demand for digital music information and the continuous expansion of application fields based on massive music data sets, content-based music information retrieval method is attracting more and more researchers' attention.

The increased popularity of online social networking platforms has positively and negatively impacted users (humans). Among several others, one significant concern is the presence of fake profiles, which may harm users’ experience and trust. To deal with such an issue, we present our research to focus on detecting counterfeit profiles in online social networking platforms. As an instance of such an online platform, we choose Instagram. In our presented research, we have used a supervised Machine Learning Algorithm known as the Random Forest (RF) due to its flexibility and strength towards ensemble learning technique that is especially useful for classification and regression-related tasks. In our proposed approach, we precisely focus on the possible enhancement of the accuracy and efficiency of the fake profile detection technique with enhanced randomness achieved through seed selection. Experimental results confirm that the proposed model achieved 98.71 percent accuracy, an F1-score of 0.96, and an improved Recall value of 97 percent (from 85 percent), which ensures better detection of fake profiles compared to the Baseline Random Forest model.

This study was conducted at the Music Library of the Ministry of Information (ML-MOI) and the Music Library of the Cultural Affairs (ML-CA) of Eritrea. The main aim of the study was to designan appropriate Indigenous Music Information Storage and Retrieval System for Eritrea. A quantitative approach was mainly used to obtain data from a purposefully selected sample. The qualitative approach was also used in some research stages. Methods used included documentanalysis, questionnaire, personal interview, focus group discussion and observation. It was found that music data was recorded in a register, which also served as a finding aid. Users’ preference was to search for music by artist, title, producer, occasion, subject or language.Unfortunately this was not possible with the system that was being used since it only allowed searching through the name or initials of an artist. The paper recommends the establishment of an automated indigenous storage and retrieval system to enable registration of indigenous knowledge information, offering facilities for indexing, searching and report generation.

The forms of identity exploration tools and trends have been changed enormously with the rapid expansion of internet media and communication technology for the last decades. Modern dwellers are enthusiastically found to be exposed themselves in online social media through different types of digital contents for establishing their socio-situational identity every day. The random practice of sharing digital contents in online social media is not only a matter of technological instigation, but also a stimulation of late modern socio-situational condition in the process of forming identity for its’ users. Undoubtedly, the digital contents have reasonable grounds to be accepted and celebrated by the social media users in terms of their aptness in purpose. Guided by the spirit of modern technological advancement, the digital contents like digital compositions and photographs seem to occupy a huge amount of social media space today. Although the increasing endorsement and adoration of digital contents in the construction of socio-situational identity in online social platforms is alleged to manufacture a generation having less competence and compassion in the use of language in proper order, many scholars find the initiation however facilitating multidimensional avenues in the narration and construction of socio-situational self for the users. In this circumstance, with the help of recognized works and materials related to the fields, the study intends to investigate, interpret, and review the possible opportunities and challenges in the use of digital write-ups and photographs on the construction of socio-situational identity in online social media platforms.

Beware: Patients increasingly purchasing medications via social media

There has been extensive research on music information retrieval (MIR), such as signal processing, pattern mining, and information retrieval. In such studies, audio features extracted from music are commonly used, but there is no open platform for data collection and analysis of audio features. Therefore, we build the platform for the data collection and analysis for MIR research. On the platform, we represent the music data with Linked Data, which are in a format suitable for computer processing, and also link data fragments to each other. By adopting the Linked Data, the music data will become easier to publish and share, and there is an advantage that complex music analysis will be facilitated. In this paper, we first investigate the frequency of the audio features used in previous studies on MIR for designing the Linked Data schema. Then, we build a platform, that automatically extracts the audio features and music metadata from YouTube URIs designated by users, and adds them to our Linked Data DB. Finally, the sample queries for music analysis and the current record of music registrations in the DB are presented.

Online social network platform becomes a good arena to observe generous behaviors of humans. Among various online social platforms, online games mimic real world closely and embed various social interactions between players. In online games, players show generosity each other even to strangers by donating their cyber asset generously. We focus on analyzing the generous behaviors giving items or money to lower-level strangers. In this research, we focus on analyzing random acts of kindness that resembles generous behaviors in the real world, especially donation. Using a large-scale real data from a major online game company, we find that benefiting from a generous behavior increases a player’s generosity. On the other hand, we also notice that social influence does not work effectively in case that these generous behaviors are not recognizable or visible to their friends.

In musicology and music research generally, the increasing availability of digital music, storage capacities, and computing power enable and require new and intelligent systems. In the transition from traditional to digital musicology, many techniques and tools have been developed for the analysis of individual pieces of music, but large-scale music data that are increasingly becoming available require research methods and systems that work on the collection-level and at scale. Although many relevant algorithms have been developed during the past 15 years of research in Music Information Retrieval, an integrated system that supports large-scale digital musicology research has so far been lacking. In the Digital Music Lab (DML) project, a collaboration among music librarians, musicologists, computer scientists, and human-computer interface specialists, the DML software system has been developed that fills this gap by providing intelligent large-scale music analysis with a user-friendly interactive interface supporting musicologists in their exploration and enquiry. The DML system empowers musicologists by addressing several challenges: distributed processing of audio and other music data, management of the data analysis process and results, remote analysis of data under copyright, logical inference on the extracted information and metadata, and visual web-based interfaces for exploring and querying the music collections. The DML system is scalable and based on Semantic Web technology and integrates into Linked Data with the vision of a distributed system that enables music research across archives, libraries, and other providers of music data. A first DML system prototype has been set up in collaboration with the British Library and I Like Music Ltd. This system has been used to analyse a diverse corpus of currently 250,000 music tracks. In this article, we describe the DML system requirements, design, architecture, components, and available data sources, explaining their interaction. We report use cases and applications with initial evaluations of the proposed system.

The academic discipline focusing on the processing and organization of digital music information, commonly known as Music Information Retrieval (MIR), has multidisciplinary roots and interests. Thus, MIR technologies have the potential to have impact across disciplinary boundaries and to enhance the handling of music information in many different user communities. However, in practice, many MIR research agenda items appear to have a hard time leaving the lab in order to be widely adopted by their intended audiences. On one hand, this is because the MIR field still is relatively young, and technologies therefore need to mature. On the other hand, there may be deeper, more fundamental challenges with regard to the user audience. In this contribution, we discuss MIR technology adoption issues that were experienced with professional music stakeholders in audio mixing, performance, musicology and sales industry. Many of these stakeholders have mindsets and priorities that differ considerably from those of most MIR academics, influencing their reception of new MIR technology. We mention the major observed differences and their backgrounds, and argue that these are essential to be taken into account to allow for truly successful cross-disciplinary collaboration and technology adoption in MIR.

Music bibliographers and librarians are accustomed to an essential dichotomy that presents itself in the ordering of musical documents for retrieval. The documents themselves (scores and recordings, mostly) must be represented in retrieval systems according to their physical characteristics (that is, according to formatted physical descriptions) for the purposes of general bibliographic control; but the musical works that are contained in and conveyed by these documents require representation according to their intellectual origins and musical characteristics (that is, author and uniform title headings). Collections of musical documents are unique among collections of documents (in libraries, bibliographies, etc.) in that the influence of repertory is such that a given collection will have many instantiations of the same musical work--one Tchaikovsky Fifth Symphony (a musical work), but a dozen scores of different sizes and formats, and dozens of recordings, not to mention excerpts and arrangements. Thus the intellectual control of musical works assumes tremendous importance in the work of music librarians and bibliographers. At the beginning of this new century we find a rapidly evolving domain for research and very pragmatic implementation known as Music Information Retrieval (MIR). Music Information Retrieval is basically the activity of automating the retrieval of musical works, or parts of musical works. MIR embraces everything from query-by-humming systems that allow a searcher to hum a tune for which the database returns an audio output, to the design of metadata structures and standard name-title-subject querying of bibliographic databases. The field encompasses various system-Engineering components, such as audioinformation retrieval (AIR), and it also encompasses musicological engineering problems such as recognition of parts of musical works. A major component is the creation of digital music libraries--electronic repositories of musical works. We will look more closely at the components of MIR later, but for now it is important to comprehend the evolution of this new domain for music librarianship and bibliography. In information retrieval in general, as well as in music information retrieval, the objects or that will be mapped into databases for retrieval must be well-defined. An entity is simply a thing--it might be a physical characteristic or it might be a concept. Entities occupy prominent positions in the structure of information retrieval databases. Consider an online catalog: therein things such as tides, composers' names, tide-page transcriptions, etc., could all be considered entities. Entities have attributes (inherent characteristics that can be defined) that are used to structure their representations in information retrieval systems. In music information retrieval, as we will see below, there are many potential entities at play. But universal to the retrieval of music information--whether it be bibliographic or audiographic using surrogates or real-time sound--is the concept of the musical work. Musical works, then, as opposed to musical documents such as scores or recordings of musical works, form a key entity for music information retrieval. Ultimately, searches for a given musical work rely on the hope of subsequent selection of instantiation in one of several documentary formats. Musical works have been variously and industriously described by musicologists and music bibliographers. Yet, in the information retrieval domain, the work as opposed to the document has only recently received focused attention. (1) Efforts to define works as information retrieval entities and to document their occurrence empirically are quite recent. In fact, systems for bibliographic information retrieval, and more recently for information storage and retrieval, have been designed with the document as the key entity, and works have been dismissed as too abstract or difficult to define empirically to take a role in information retrieval. …

The present invention is directed to a musical sound modification apparatus and method whereby natural musical instrument sounds are analyzed to extract time variant information of pitch, amplitude and timbre, as Musical Sound Modification Data, which are stored in a temporary memory area as pitch Template, amplitude Template and timbre Template for each of attack part, sustain part and release part of a musical sound. Musical Sound Modification Data for one musical note is formed by selectively joining each Template of attack part, sustain part and release part, and for each of pitch, amplitude and timbre, and is pasted to a series of musical note data in music data. At music reproduction by the music data, the generated musical sound gives a “realistic feeling” to human ear owing to supplied time variant characteristics, because each of pitch, amplitude and timbre of the musical sounds corresponding to the musical note data is modified by said Musical Sound Modification Data.

Social media anomaly detection is a crucial component for enhancing the overall integrity and functionality of social media platforms. Its role is multifaceted, including key aspects such as security and fraud detection, where it plays a pivotal role in identifying and mitigating potential risks like fake accounts, malicious attacks, and cyberbullying. With the increasing popularity of social media, a plethora of user-generated content, including images, videos, and comments, is showcased on online social platforms. Consequently, anomaly detection on social media encounters various challenges such as data diversity, real-time requirements, and privacy preservation. To address these challenges, researchers have developed traditional and machine learning-based intelligent anomaly detection methods tailored to various types of anomalous activities. This work primarily focuses on anomalies and abnormal behaviours on online social platforms, providing a comprehensive review of recent developments in intelligent detection methods. We present a broad overview covering the background, common terminology, comparisons between traditional and intelligent approaches, application scenarios, and potential research directions. In this endeavour, we aim to contribute valuable theoretical and technical support for the future innovation and development of anomaly detection in social media.

A survey on symbolic data-based music genre classification

In this survey article, we give an overview of methods for music similarity estimation and music recommendation based on music context data. Unlike approaches that rely on music content and have been researched for almost two decades, music-context -based (or contextual ) approaches to music retrieval are a quite recent field of research within music information retrieval (MIR). Contextual data refers to all music-relevant information that is not included in the audio signal itself. In this article, we focus on contextual aspects of music primarily accessible through web technology. We discuss different sources of context-based data for individual music pieces and for music artists. We summarize various approaches for constructing similarity measures based on the collaborative or cultural knowledge incorporated into these data sources. In particular, we identify and review three main types of context-based similarity approaches: text-retrieval-based approaches (relying on web-texts, tags, or lyrics), co-occurrence-based approaches (relying on playlists, page counts, microblogs, or peer-to-peer-networks), and approaches based on user ratings or listening habits. This article elaborates the characteristics of the presented context-based measures and discusses their strengths as well as their weaknesses.

Social media users come from various circles with different social and cultural statuses which will bring up diverse language variations. Additionally, language variations in speech events on social media certainly have characteristics that are distinctive and different from speech events in general. This study aimed at investigating types and forms of Indonesian language varieties occurred in speech events on social media. A quantitative and qualitative approaches was applied to accomplish the research objective. The quantitative approach was carried out using content analysis. The qualitative approach used was based on sociolinguistic theory. The research data were collected using the listening method (uninvolved conversation observation technique, recording technique, note-taking technique) and questionnaire method. The data were analyzed using the content analysis method to determine the form and percentage of language variety used in speech events on social media. In addition, Miles and Huberman's model was also used for data reduction, verification, conclusion drawing. The research found that language varieties in speech events on social media was not dominated by non-standard varieties, and inter-language mixture (code switching and code mixing). The characteristics of speech events on social media use, among others, informal language, daily-conversation language, unnatural context, and shortened writing. The results implied that the variety of language use on social media has increased since the COVID-19 pandemic. Therefore, it is necessary to research the use of codes, politeness, or hate speech on social media. Moreover, opportunities are also open to research the use of social media for language learning.