How many paid subscribers does Spotify have? As of the first quarter of 2025, Spotify had 268 million premium subscribers worldwide, up from 239 million in the corresponding quarter of 2024. Spotify’s subscriber base has increased dramatically in the last few years and has more than doubled since early 2019. Spotify and competitors Spotify is a music streaming service originally founded in 2006 in Sweden. The platform can be used from various devices and allows users to browse through a catalogue of music licensed through multiple record labels, as well as creating and sharing playlists with other users. Additionally, listeners are able to enjoy music for free with advertisements or are also given the option to purchase a subscription to allow for unlimited ad-free music streaming. Spotify’s largest competitors are Pandora, a company that offers a similar service and remains popular in the United States, and Apple Music, which was launched in 2015. While Pandora was once among the highest-grossing music apps in the Apple App Store, recent rankings show that global services like QQ Music, NetEase Cloud Music, and YouTube Music now generate higher monthly revenues.Users are also able to register Spotify accounts using Facebook directly through the website using an app. This enables them to connect with other Facebook friends and explore their music tastes and playlists. Spotify is a popular source for keeping up-to-date with music, and the ability to enjoy Spotify anywhere at any time allows consumers to shape their music consumption around their lifestyles and preferences.

Spotify Million Playlist Dataset Challenge

Summary

The Spotify Million Playlist Dataset Challenge consists of a dataset and evaluation to enable research in music recommendations. It is a continuation of the RecSys Challenge 2018, which ran from January to July 2018. The dataset contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. The evaluation task is automatic playlist continuation: given a seed playlist title and/or initial set of tracks in a playlist, to predict the subsequent tracks in that playlist. This is an open-ended challenge intended to encourage research in music recommendations, and no prizes will be awarded (other than bragging rights).

Background

Playlists like Today’s Top Hits and RapCaviar have millions of loyal followers, while Discover Weekly and Daily Mix are just a couple of our personalized playlists made especially to match your unique musical tastes.

Our users love playlists too. In fact, the Digital Music Alliance, in their 2018 Annual Music Report, state that 54% of consumers say that playlists are replacing albums in their listening habits.

But our users don’t love just listening to playlists, they also love creating them. To date, over 4 billion playlists have been created and shared by Spotify users. People create playlists for all sorts of reasons: some playlists group together music categorically (e.g., by genre, artist, year, or city), by mood, theme, or occasion (e.g., romantic, sad, holiday), or for a particular purpose (e.g., focus, workout). Some playlists are even made to land a dream job, or to send a message to someone special.

The other thing we love here at Spotify is playlist research. By learning from the playlists that people create, we can learn all sorts of things about the deep relationship between people and music. Why do certain songs go together? What is the difference between “Beach Vibes” and “Forest Vibes”? And what words do people use to describe which playlists?

By learning more about nature of playlists, we may also be able to suggest other tracks that a listener would enjoy in the context of a given playlist. This can make playlist creation easier, and ultimately help people find more of the music they love.

Dataset

To enable this type of research at scale, in 2018 we sponsored the RecSys Challenge 2018, which introduced the Million Playlist Dataset (MPD) to the research community. Sampled from the over 4 billion public playlists on Spotify, this dataset of 1 million playlists consist of over 2 million unique tracks by nearly 300,000 artists, and represents the largest public dataset of music playlists in the world. The dataset includes public playlists created by US Spotify users between January 2010 and November 2017. The challenge ran from January to July 2018, and received 1,467 submissions from 410 teams. A summary of the challenge and the top scoring submissions was published in the ACM Transactions on Intelligent Systems and Technology.

In September 2020, we re-released the dataset as an open-ended challenge on AIcrowd.com. The dataset can now be downloaded by registered participants from the Resources page.

Each playlist in the MPD contains a playlist title, the track list (including track IDs and metadata), and other metadata fields (last edit time, number of playlist edits, and more). All data is anonymized to protect user privacy. Playlists are sampled with some randomization, are manually filtered for playlist quality and to remove offensive content, and have some dithering and fictitious tracks added to them. As such, the dataset is not representative of the true distribution of playlists on the Spotify platform, and must not be interpreted as such in any research or analysis performed on the dataset.

Dataset Contains

1000 examples of each scenario:

Title only (no tracks) Title and first track Title and first 5 tracks First 5 tracks only Title and first 10 tracks First 10 tracks only Title and first 25 tracks Title and 25 random tracks Title and first 100 tracks Title and 100 random tracks

Download Link

Full Details: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge
Download Link: https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge/dataset_files

Spotify Revenue, Expenses and Its Premium Users contains the number of premium users, number of Ad-supported users and total monthly active users (MAUs).

MAUs include number of premium users as well as number of Ad-supported users.

Note : Sum of Premium Users and Ad-supported users can have some difference from MAUs. Note : All money figures are in Euro millions except ARPU which is in Euro and as it is. **Note : All users figures are in millions. ** Note : Kindly Ignore the last row.

Following definitions: MAUs : It is defined as the total count of Ad-Supported Users and Premium Subscribers that have consumed content for greater than zero milliseconds in the last thirty days from the period-end indicated. Premium MAUs : It is defined as users that have completed registration with Spotify and have activated a payment method for Premium Service. Ad MAUs : It is defined as the total count of Ad-Supported Users that have consumed content for greater than zero milliseconds in the last thirty days from the period-end indicated. Premium ARPU : It is average revenue per user which is monthly measure defined as Premium subscription revenue recognized in the quarter indicated divided by the average daily Premium Subscribers in such quarter, which is then divided by three months. Cost of Revenue : It is expenses done by company.

Photo by Alexander Shatov on Unsplash

The Spotify Music Streaming Sessions Dataset (MSSD) consists of 160 million streaming sessions with associated user interactions, audio features and metadata describing the tracks streamed during the sessions, and snapshots of the playlists listened to during the sessions.

This dataset enables research on important problems including how to model user listening and interaction behaviour in streaming, as well as Music Information Retrieval (MIR), and session-based sequential recommendations.

About Dataset

Overview:

This dataset provides detailed metadata and audio analysis for a wide collection of Spotify music tracks across various genres. It includes track-level information such as popularity, tempo, energy, danceability, and other musical features that can be used for music recommendation systems, genre classification, or trend analysis. The dataset is a rich source for exploring music consumption patterns and user preferences based on song characteristics.

Dataset Details:

This dataset contains rows of individual music tracks, each described by both metadata (such as track name, artist, album, and genre) and quantitative audio features. These features reflect different musical attributes such as energy, acousticness, instrumentalness, valence, and more, making it ideal for audio machine learning projects and exploratory data analysis.

Schema and Column Descriptions:

Key Features:

• Comprehensive Track Data: Metadata combined with detailed audio analysis.
• Genre Diversity: Includes tracks from various music genres.
• Audio Feature Rich: Suitable for audio classification, recommendation engines, or clustering.
• Machine Learning Friendly: Clean and numerical format ideal for ML models.

Usage:

This dataset is valuable for:

• 🎵 Music Recommendation Systems: Building collaborative or content-based recommenders.
• 📊 Data Visualization & Dashboards: Analyzing genre or mood trends over time.
• 🤖 Machine Learning Projects: Predicting song popularity or clustering similar tracks.
• 🧠 Music Psychology & Behavioral Studies: Exploring how music features relate to emotions or behavior.

Data Maintenance:

• Source: https://huggingface.co/datasets/ConquestAce/spotify-songs
• Last Updated: 2025/04/26
• License: Check original source for usage terms.

Additional Notes:

• This dataset can be enhanced by merging it with user listening behavior data, lyrics datasets, or chart positions for more advanced analysis.
• Some columns like key, mode, and explicit may need to be mapped for better readability in visualization.

This dataset was created using Spotify developer API. It consists of user-created as well as Spotify-curated playlists.
The dataset consists of 1 million playlists, 3 million unique tracks, 3 million unique albums, and 1.3 million artists.
The data is stored in a SQL database, with the primary entities being songs, albums, artists, and playlists.
Each of the aforementioned entities are represented by unique IDs (Spotify URI).
Data is stored into following tables:

• album
• artist
• track
• playlist
• track_artist1
• track_playlist1

album

| id | name | uri |

id: Album ID as provided by Spotify
name: Album Name as provided by Spotify
uri: Album URI as provided by Spotify

artist

| id | name | uri |

id: Artist ID as provided by Spotify
name: Artist Name as provided by Spotify
uri: Artist URI as provided by Spotify

track

| id | name | duration | popularity | explicit | preview_url | uri | album_id |

id: Track ID as provided by Spotify
name: Track Name as provided by Spotify
duration: Track Duration (in milliseconds) as provided by Spotify
popularity: Track Popularity as provided by Spotify
explicit: Whether the track has explicit lyrics or not. (true or false)
preview_url: A link to a 30 second preview (MP3 format) of the track. Can be null
uri: Track Uri as provided by Spotify
album_id: Album Id to which the track belongs

playlist

| id | name | followers | uri | total_tracks |

id: Playlist ID as provided by Spotify
name: Playlist Name as provided by Spotify
followers: Playlist Followers as provided by Spotify
uri: Playlist Uri as provided by Spotify
total_tracks: Total number of tracks in the playlist.

track_artist1

| track_id | artist_id |

Track-Artist association table

track_playlist1

| track_id | playlist_id |

Track-Playlist association table

- - - - - SETUP - - - - -

The data is in the form of a SQL dump. The download size is about 10 GB, and the database populated from it comes out to about 35GB.

spotifydbdumpschemashare.sql contains the schema for the database (for reference):
spotifydbdumpshare.sql is the actual data dump.

Setup steps:
1. Create database

- - - - - PAPER - - - - -

The description of this dataset can be found in the following paper:

Papreja P., Venkateswara H., Panchanathan S. (2020) Representation, Exploration and Recommendation of Playlists. In: Cellier P., Driessens K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1168. Springer, Cham

This dataset is based on the subset of users in the #nowplaying dataset who publish their #nowplaying tweets via Spotify. In principle, the dataset holds users, their playlists and the tracks contained in these playlists. The csv-file holding the dataset contains the following columns: "user_id", "artistname", "trackname", "playlistname", where user_id is a hash of the user's Spotify user name artistname is the name of the artist trackname is the title of the track and playlistname is the name of the playlist that contains this track. The separator used is , each entry is enclosed by double quotes and the escape character used is . A description of the generation of the dataset and the dataset itself can be found in the following paper: Pichl, Martin; Zangerle, Eva; Specht, Günther: "Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?" in 15th IEEE International Conference on Data Mining Workshops (ICDM 2015), pp. 1360-1365, IEEE, Atlantic City, 2015. {"references": ["Pichl, Martin; Zangerle, Eva; Specht, G\u00fcnther: "Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?" in 15th IEEE International Conference on Data Mining Workshops (ICDM 2015), pp. 1360-1365, IEEE, Atlantic City, 2015."]}

Content

This is a dataset of Spotify tracks over a range of 125 different genres. Each track has some audio features associated with it. The data is in CSV format which is tabular and can be loaded quickly.

  Usage

The dataset can be used for:

Building a Recommendation System based on some user input or preference Classification purposes based on audio features and available genres Any other application that you can think of. Feel free to discuss!

  Column… See the full description on the dataset page: https://huggingface.co/datasets/maharshipandya/spotify-tracks-dataset.

1) Data Introduction • The Spotify Playlist-ORIGINS Dataset is a dataset of Spotify playlists called ORIGINS, which individuals have made with their favorite songs since 2014.

2) Data Utilization (1) Spotify Playlist-ORIGINS Dataset has characteristics that: • This dataset contains detailed music information for each playlist, including song name, artist, album, genre, release year, track ID, and structured metadata such as name, description, and song order for each playlist. (2) Spotify Playlist-ORIGINS Dataset can be used to: • Playlist-based music recommendation and user preference analysis: It can be used to develop a machine learning/deep learning-based music recommendation system or to study user preference analysis using playlist and song information. • Music Trend and Genre Popularity Analysis: It analyzes release year, genre, and artist data and can be used to study the music industry and culture, including music trends by period and genre, and changes in popular artists and songs.

💁‍♀️Please take a moment to carefully read through this description and metadata to better understand the dataset and its nuances before proceeding to the Suggestions and Discussions section.

Dataset Description:

This dataset provides a comprehensive collection of setlists from Taylor Swift’s official era tours, curated expertly by Spotify. The playlist, available on Spotify under the title "Taylor Swift The Eras Tour Official Setlist," encompasses a diverse range of songs that have been performed live during the tour events of this global artist. Each dataset entry corresponds to a song featured in the playlist.

Taylor Swift, a pivotal figure in both country and pop music scenes, has had a transformative impact on the music industry. Her tours are celebrated not just for their musical variety but also for their theatrical elements, narrative style, and the deep emotional connection they foster with fans worldwide. This dataset aims to provide fans and researchers an insight into the evolution of Swift's musical and performance style through her tours, capturing the essence of what makes her tour unique.

Data Collection and Processing:

Obtaining the Data: The data was obtained directly from the Spotify Web API, specifically focusing on the setlist tracks by the artist. The Spotify API provides detailed information about tracks, artists, and albums through various endpoints.

Data Processing: To process and structure the data, Python scripts were developed using data science libraries such as pandas for data manipulation and spotipy for API interactions, specifically for Spotify data retrieval.

Workflow:

Authentication API Requests Data Cleaning and Transformation Saving the Data

Attribute Descriptions:

• artist_name: the name of the artist (Taylor Swift)
• track_name: the title of the track
• is_explicit: Indicates whether the track contains explicit content
• album_release_date: The date when the track was released
• genres: A list of genres associated with Beyoncé
• danceability: A measure from 0.0 to 1.0 indicating how suitable a track is for - dancing based on a combination of musical elements
• valence: A measure from 0.0 to 1.0 indicating the musical positiveness conveyed by a track
• energy: A measure from 0.0 to 1.0 representing a perceptual measure of intensity and activity
• loudness: The overall loudness of a track in decibels (dB)
• acousticness: A measure from 0.0 to 1.0 whether the track is acoustic
• instrumentalness: Predicts whether a track contains no vocals
• liveness: Detects the presence of an audience in the recordings speechiness: Detects the presence of spoken words in a track
• key: The key the track is in. Integers map to pitches using standard Pitch Class notation
• tempo: The overall estimated tempo of a track in beats per minute (BPM)
• mode: Modality of the track
• duration_ms: The length of the track in milliseconds
• time_signature: An estimated overall time signature of a track
• popularity: A score between 0 and 100, with 100 being the most popular

Note: Popularity score reflects the score recorded on the day that retrieves this dataset. The popularity score could fluctuate daily.

Potential Applications:

• Predictive Analytics: Researchers might use this dataset to predict future setlist choices for tours based on album success, song popularity, and fan feedback.

Disclaimer and Responsible Use:

This dataset, derived from Spotify focusing on Taylor Swift's The Eras Tour setlist data, is intended for educational, research, and analysis purposes only. Users are urged to use this data responsibly, ethically, and within the bounds of legal stipulations.

• Compliance with Terms of Service: Users should adhere to Spotify's Terms of Service and Developer Policies when utilizing this dataset.
• Copyright Notice: The dataset presents music track information including names and artist details for analytical purposes and does not convey any rights to the music itself. Users must ensure that their use does not infringe on the copyright holders' rights. Any analysis, distribution, or derivative work should respect the intellectual property rights of all involved parties and comply with applicable laws.
• No Warranty Disclaimer: The dataset is provided "as is," without warranty, and the creator disclaims any legal liability for its use by others.
• Ethical Use: Users are encouraged to consider the ethical implications of their analyses and the potential impact on artists and the broader community.
• Data Accuracy and Timeliness: The dataset reflects a snapshot in time and may not represent the most current information available. Users are encouraged to verify the data's accuracy and timeliness.
• Source Verification: For the most accurate and up-to-date information, users are encouraged to refer directly to Spotify's official website.
• Independence Declaration: ...

A dataset comparing Spotify Premium plans including Individual, Duo, Family, and Student with pricing and user limits.

👍 If this dataset was useful to you, leave your vote at the top of the page 👍

The dataset provides information on the daily top 200 tracks listened to by users of the Spotify digital platform around the world.

I put together this dataset because I really love music (I listen to it for several hours a day) and have not found a similar dataset with track genres on kaggle.

The dataset can be useful for beginners in the field of working with data. It contains missing values, arrays in columns, and so on, which can be great practice when conducting an EDA phase.

Soon, my example will appear here as possible, based on the specified dataset, go on a musical journey around the world and understand how the musical tastes of humanity have changed around the world)))

In addition, I will be very happy to see the work of the community on this dataset.

Also, in case of interest in data by country, I am ready to place it upon request.

You can contact me through: telegram @natarov_ivan

Estimates suggest that Apple Music had 95 million subscribers worldwide in June 2024, up by 2 million from the previous year. Launched in 2015 by U.S. tech giant Apple, Apple Music is the second largest music streaming service worldwide, competing with market leader Spotify. Spotify remains market leader While Apple Music is a popular music streaming platform, accounting for 12.6 percent of subscribers worldwide, the 2008 founded streaming service Spotify remains the market leader with a subscriber share of nearly 32 percent. Financially this meant that the Swedish company generated a global revenue of 3.7 billion euros through its Premium accounts in the fourth quarter of 2024 alone.Music streaming overall increasesOverall, music streaming has experienced significant growth over the last decade. Even if the annual growth rate is gradually declining, it still stood at over 7 percent in 2024, becoming the music industry’s main revenue driver and reaching a revenue of 20 billion U.S. dollars worldwide in 2024.

Context

This dataset consists of 24000 tracks from 30 genres, and is a shrunk version of maharshipandya/spotify-tracks-dataset dataset. All non-heuristic data is cut and cleaned for better usability and performance. All data taken from Spotify API and is open source. This dataset can be used to train prediction models based on user preferences, or categorise tracks by corresponding heuristic.

  Column Description

danceability: Danceability describes how suitable a track is… See the full description on the dataset page: https://huggingface.co/datasets/engels/spotify-tracks-lite.

This dataset was constructed based on the data found in Kaggle from Spotify. The files here reported can be used to build a property graph in Neo4J: song.csv - contains all the data for the Song nodes. artist.csv - contains the data for the Artist nodes. playlist.csv - contains the data for the Playlist nodes. user.csv - contains the data for the Playlist nodes (those creating Playlists). genre.csv - contains the data for the Genre nodes (a category for the Artists). type.csv - contains the data for the Type nodes (a category for the Playlists). sing.csv - contains the data for the SING relationship from Artist to Song nodes. created.csv - contains the data for the CREATED relationship from User to Playlist nodes. in.csv - contains the data for the IN relationship from Song to Playlist nodes. of_type.csv - contains the data for the OFTYPE relationship from Playlist to Type nodes. labelled.csv - contains the data for the LABELLED relationship from Artist to Genre nodes. This data was used as test dataset in the paper "MINE GRAPH RULE: A New GQL Operator for Mining Association Rules in Property Graph Databases".

LFM-1b dataset extended by acoustic track features and cultural cues describing users

This dataset is based on the LFM-1b dataset (cf. http://www.cp.jku.at/datasets/LFM-1b/), however, adds acoustic features describing the tracks to the original dataset as well as cultural aspects describing users (taken from Hofstede's six dimension model and the World Happiness Report) on the country-level.

For the creation of the dataset, we extract all users for which the original dataset contains country information for. We extract the listening events of these users and match the tracks against the Spotify API to subsequently retrieve the acoustic features of these tracks (cf. [Spotify Audio Feature Description](https://developer.spotify.com/documentation/web-api/reference/object-model/#audio-features-object)). The final dataset contains only events of users with country information and tracks with acoustic features, which can be matched with the country-level data of the World Happiness Report and Hofstede's cultural dimensions to add cultural and socio-economic aspects for users.

This new dataset contains

• 55,190 users
• 3,471,884 tracks including acoustic features
• 351,469,333 listening events of those users for tracks we have obtained acoustic features for
• Hofstede's cultural dimensions for 47 countries
• World Happiness Report (WHR) data for 164 countries

Files
All files are tab-separated, with no quoting of strings. The dataset contains the following files, whose content we describe in more detail in the following parts.

* acoustic_features_lfm_id.tsv: acoustic features for all tracks in the dataset, identified by their LFM track identifier
* events.tsv: listening events for all users
* hofstede.tsv: Hofstede's cultural dimensions
* users.tsv: user metadata
* world_happiness_report_2018.tsv: World Happiness Report data

For further information on the contents of these files, please cf. the Readme file.

Please cite the following paper when using the dataset:
Zangerle, E., Pichl, M. and Schedl, M., 2020. User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. Transactions of the International Society for Music Information Retrieval, 3(1), pp.1–16. DOI: http://doi.org/10.5334/tismir.37

🎵 Spotify Song Preference Dataset

This dataset contains Spotify audio features for 195 songs categorized as liked or disliked by the user. It was created to build and train ML models that can predict user preferences in music based on quantitative audio features.

  📥 Dataset Overview

Total songs: 195 Format: CSV (data.csv) Source: Spotify API Target column: liked (1 = liked, 0 = disliked) Data type: Tabular Licensing: For academic and personal research use (derived from… See the full description on the dataset page: https://huggingface.co/datasets/hardik-0212/My_Dataset.

Unlock powerful insights with our custom music datasets, offering access to millions of records from popular music platforms like Spotify, SoundCloud, Amazon Music, YouTube Music, and more. These datasets provide comprehensive data points such as track titles, artists, albums, genres, release dates, play counts, playlist details, popularity scores, user-generated tags, and much more, allowing you to analyze music trends, listener behavior, and industry patterns with precision. Use these datasets to optimize your music strategies by identifying trending tracks, analyzing artist performance, understanding playlist dynamics, and tracking audience preferences across platforms. Gain valuable insights into streaming habits, regional popularity, and emerging genres to make data-driven decisions that enhance your marketing campaigns, content creation, and audience engagement. Whether you’re a music producer, marketer, data analyst, or researcher, our music datasets empower you with the data needed to stay ahead in the ever-evolving music industry. Available in various formats such as JSON, CSV, and Parquet, and delivered via flexible options like API, S3, or email, these datasets ensure seamless integration into your workflows.

Overview

Welcome to the Music Informatics for Radio Across the GlobE (MIRAGE) MetaCorpus. The current (v0.2) development release consists of metadata (e.g., artist name, track title) and musicological features (e.g., instrument list, voice type, tempo) for 1 million events streaming on 10,000 internet radio stations across the globe, with 100 events from each station.

Users who wish to access, interact with, and/or export metadata from the MIRAGE-MetaCorpus may also visit the MIRAGE online dashboard at the following url:

• https://pearl-laboratory.github.io/mirage-mc/

Attribution

The current MIRAGE-MetaCorpus is available under a CC4 license. Users may cite the dataset here:

Sears, David R.W. “Music Informatics for Radio Across the Globe (MIRAGE) Metacorpus -- 2024”. Zenodo, July 19, 2024. https://doi.org/10.5281/zenodo.12786202.

Users accessing the MIRAGE-MetaCorpus using the online dashboard should also cite the following ISMIR paper:

Ngan V.T. Nguyen, Elizabeth A.M. Acosta, Tommy Dang, and David R.W. Sears. "Exploring Internet Radio Across the Globe with the MIRAGE Online Dashboard," in Proceedings of the 25th International Society for Music Information Retrieval Conference (San Francisco, CA, 2024).

Data Sources

This repository of the MIRAGE-MetaCorpus contains 81 metadata variables from the following open-access sources:

• Radio Garden (RG) -- https://radio.garden
• Natural Earth map data set (NE) -- https://www.naturalearthdata.com/
• Internet Radio Station Stream Encoder (SE)
• Annotator Review (AR)
• Monitoring/Matching Algorithm (MA)
• WikiData (WD) -- https://www.wikidata.org
• MusicBrainz (MB) -- https://musicbrainz.org/

Each event also includes attribution metadata from the following commercial sources:

• Spotify (SP) -- https://open.spotify.com/
  • Note that users may examine an additional 19 metadata variables on the MIRAGE online dashboard that were obtained from the Spotify API.
• Musixmatch (MX) -- https://www.musixmatch.com/
• YouTube (YT) -- https://www.youtube.com/
• Genius (GE) -- https://genius.com/
• AZlyrics (AZ) -- https://www.azlyrics.com/

Data Sets

The metadata reflect information about each event's location (e.g., city, country), station (name, format, url), event (id, local time at station, etc.), artist (name, voice type, etc.), and track (e.g., title, year of release, etc.). For that reason, the MIRAGE-MetaCorpus includes the following datasets:

• MIRAGE.csv -- the complete metacorpus (1 million)
• events.csv -- all event-level metadata (1 million)
• tracks.csv -- all track-level metadata (414,886)
• artists.csv -- all artist-level metadata (259,783)
• stations.csv -- all station-level metadata (10,000)
• locations.csv -- all location-level metadata (4,324)

A subset of the MIRAGE-MetaCorpus is also available for events with metadata from online music libraries that reliably matched the event's description in the radio station's stream encoder:

• MIRAGE_reliable.csv (473,850)
• events_reliable.csv (473,850)
• tracks_reliable.csv (204,969)
• artists_reliable.csv (80,005)
• stations_reliable.csv (9,284)
• locations_reliable.csv (4,142)

Contact

If you are a copyright owner for any of the metadata that appears in the MIRAGE-MetaCorpus and would like us to remove your metadata, please contact the developer team at the following email address: miragedashboard@gmail.com

Description

The datasets demonstrate the malware economy and the value chain published in our paper, Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access, at the 12th International Workshop on Cyber Crime (IWCC 2023), part of the ARES Conference, published by the International Conference Proceedings Series of the ACM ICPS.

Using the well-documented scripts, it is straightforward to reproduce our findings. It takes an estimated 1 hour of human time and 3 hours of computing time to duplicate our key findings from MalwareInfectionSet; around one hour with VictimAccessSet; and minutes to replicate the price calculations using AccountAccessSet. See the included README.md files and Python scripts.

We choose to represent each victim by a single JavaScript Object Notation (JSON) data file. Data sources provide sets of victim JSON data files from which we've extracted the essential information and omitted Personally Identifiable Information (PII). We collected, curated, and modelled three datasets, which we publish under the Creative Commons Attribution 4.0 International License.

1. MalwareInfectionSet We discover (and, to the best of our knowledge, document scientifically for the first time) that malware networks appear to dump their data collections online. We collected these infostealer malware logs available for free. We utilise 245 malware log dumps from 2019 and 2020 originating from 14 malware networks. The dataset contains 1.8 million victim files, with a dataset size of 15 GB.

2. VictimAccessSet We demonstrate how Infostealer malware networks sell access to infected victims. Genesis Market focuses on user-friendliness and continuous supply of compromised data. Marketplace listings include everything necessary to gain access to the victim's online accounts, including passwords and usernames, but also detailed collection of information which provides a clone of the victim's browser session. Indeed, Genesis Market simplifies the import of compromised victim authentication data into a web browser session. We measure the prices on Genesis Market and how compromised device prices are determined. We crawled the website between April 2019 and May 2022, collecting the web pages offering the resources for sale. The dataset contains 0.5 million victim files, with a dataset size of 3.5 GB.

3. AccountAccessSet The Database marketplace operates inside the anonymous Tor network. Vendors offer their goods for sale, and customers can purchase them with Bitcoins. The marketplace sells online accounts, such as PayPal and Spotify, as well as private datasets, such as driver's licence photographs and tax forms. We then collect data from Database Market, where vendors sell online credentials, and investigate similarly. To build our dataset, we crawled the website between November 2021 and June 2022, collecting the web pages offering the credentials for sale. The dataset contains 33,896 victim files, with a dataset size of 400 MB.

Credits Authors

Billy Bob Brumley (Tampere University, Tampere, Finland)

Juha Nurmi (Tampere University, Tampere, Finland)

Mikko Niemelä (Cyber Intelligence House, Singapore)

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under project numbers 804476 (SCARE) and 952622 (SPIRS).

Alternative links to download: AccountAccessSet, MalwareInfectionSet, and VictimAccessSet.