18 datasets found
  1. Active streamers on Twitch worldwide 2025

    • statista.com
    • tokrwards.com
    Updated Sep 4, 2025
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    Statista (2025). Active streamers on Twitch worldwide 2025 [Dataset]. https://www.statista.com/statistics/746173/monthly-active-streamers-on-twitch/
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    Dataset updated
    Sep 4, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2018 - Mar 2025
    Area covered
    Worldwide
    Description

    In March 2025, video streaming platform Twitch had approximately *** million active streamers, down from a peak of **** million in January 2021. The platform experienced a boom during the COVID-19 pandemic, when many new users used the platform to connect with friends or try their hand at livestreaming. However, this trend normalized again towards the end of the year, and the streaming space has also grown more competitive as platforms apart from Twitch have evolved to attract streamers and viewers. Popular content categories on Twitch In 2024, most of the leading content categories on Twitch were all gaming-related – except for the top spot: Just Chatting. The general conversation category accumulated *** billion hours of viewing time in the measured period. In March 2025, global Twitch audiences spent around *** million hours watching Just Chatting content on Twitch, with the average viewer count of such content reaching *** thousand. HasanAbi was the most popular Just Chatting streamer on Twitch in the most recently measured month. Game streamers Twitch is very popular with gamers and gaming audiences, and the ranking of the most popular Twitch streamers reflects this. Ninja (real name: Richard Tyler Blevins), the top-ranked streamer on Twitch, had **** million followers in April 2025. Ninja saw a meteoric rise to fame when he was one of the first top-ranked players to stream the then-newly released Fortnite Battle Royale at the end of 2017. Second-ranked ibai (real name: Ibai Llanos Garatea) was ranked second with ***** million followers on Twitch. With more than **** million followers, Imane Anys, better known as Pokimane, was the only woman among the most-followed Twitch streamers worldwide. Overall, women only accounted for **** percent of the top-ranked Twitch channels.

  2. s

    Twitch Social Networks

    • marketplace.sshopencloud.eu
    Updated Apr 24, 2020
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    (2020). Twitch Social Networks [Dataset]. https://marketplace.sshopencloud.eu/dataset/3mIMx7
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    Dataset updated
    Apr 24, 2020
    Description

    These datasets used for node classification and transfer learning are Twitch user-user networks of gamers who stream in a certain language. Nodes are the users themselves and the links are mutual friendships between them. Vertex features are extracted based on the games played and liked, location and streaming habits. Datasets share the same set of node features, this makes transfer learning across networks possible. These social networks were collected in May 2018. The supervised task related to these networks is binary node classification - one has to predict whether a streamer uses explicit language.

  3. Twitch Social Networks

    • kaggle.com
    Updated Nov 12, 2019
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    Andrea Garritano (2019). Twitch Social Networks [Dataset]. https://www.kaggle.com/andreagarritano/twitch-social-networks/notebooks
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 12, 2019
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Andrea Garritano
    Description

    Twitch Social Networks

    Description

    These datasets used for node classification and transfer learning are Twitch user-user networks of gamers who stream in a certain language. Nodes are the users themselves and the links are mutual friendships between them. Vertex features are extracted based on the games played and liked, location and streaming habits. Datasets share the same set of node features, this makes transfer learning across networks possible. These social networks were collected in May 2018. The supervised task related to these networks is binary node classification - one has to predict whether a streamer uses explicit language.

    Links

    Properties

    DEENESFRPTRU
    Nodes9,4987,1264,6486,5491,9124,385
    Edges153,13835,32459,382112,66631,29937,304
    Density0.0030.0020.0060.0050.0170.004
    Transitvity0.0470.0420.0840.0540.1310.049

    Possible tasks

    • Binary node classification
    • Link prediction
    • Community detection
    • Network visualization

    Paper: Multi-scale Attributed Node Embedding. Benedek Rozemberczki, Carl Allen, and Rik Sarkar. arXiv, 2019. https://arxiv.org/abs/1909.13021

  4. Twitch Small Panel Results

    • kaggle.com
    Updated Feb 3, 2019
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    Keiichi C (2019). Twitch Small Panel Results [Dataset]. https://www.kaggle.com/keiichicomplex/twitch-small-panel-results/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 3, 2019
    Dataset provided by
    Kaggle
    Authors
    Keiichi C
    Description

    Twitch.tv boasts over 2 million unique user views per day, and more than 100 thousand channels that entertain the users. How should new streamers stand out from more established names and gather a larger audience?

    Using viewership data from Twitch.tv, I develop a model to help streamers make informed choices on choice of time, game and target language audience. I specifically consider the interaction between these choices, answering such as "When is the best time to stream League of Legends for a given language?" or "I am a Russian language streamer, what game attracts most audience?"

    Additionally, I describe the whether streamers should stream when avoids time slots with more existing channels. This involves studying whether streamers has synergy with each other, despite acting as competitors by choosing to streaming similar content, together they might attract more viewers than when they stream different types of content.

    Final project target is an application which is trained using historical twitch data, powered by immediate data from the Twitch API. The application offers the best selection of streaming choices under current twitch environment. Answering the questions "I want to gather the most viewships. What game in what language and when should i stream?"

    Original datasource : https://clivecast.github.io

    Content:

    twitch_panel_fixedeffect.py : Panel Regression Model. Data Source 250 MB> 25MB limit, not included. creates regression data results 'twitch_small_panel_results.txt'

    twitch_plot.py : Plots graphs using 'twitch_small_panel_results.txt'

    twitch_small_panel_results.tx : contains regression results generated from twitch_panel_fixedeffect.py

  5. Z

    Twitch Plays Pokemon Dataset

    • data.niaid.nih.gov
    Updated Jul 8, 2020
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    Haque, Albert (2020). Twitch Plays Pokemon Dataset [Dataset]. https://data.niaid.nih.gov/resources?id=ZENODO_3932956
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    Dataset updated
    Jul 8, 2020
    Dataset authored and provided by
    Haque, Albert
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The dataset, titled the Twitch Plays Pokemon Dataset, contains 37.8 million IRC chat messages. It contains IRC chat log data for messages made between February 2, 2014 and April 23, 2014 (68 days). Each line denotes a single IRC chat message.

    Sample of the dataset:

    2014-02-1408:17:32medicbluea 2014-02-1408:17:32murderousburgerrare candy, RARE CANDY 2014-02-1408:17:32milk2978B 2014-02-1408:17:32mrtiktalikb 2014-02-1408:17:32dualhammersb 2014-02-1408:17:32shares5YES 2014-02-1408:17:32orangeruststart 2014-02-1408:17:32snowieea 2014-02-1408:17:33duroatedown 2014-02-1408:17:33crypticcraigup 2014-02-1408:17:33doug2725LOL HELIX FOSSIL WENT BACK THAT FAR

    Abstract

    With the increasing importance of online communities, discussion forums, and customer reviews, Internet “trolls” have proliferated thereby making it difficult for information seekers to find relevant and correct information. In this paper, we consider the problem of detecting and identifying Internet trolls, almost all of which are human agents. Identifying a human agent among a human population presents significant challenges compared to detecting automated spam or computerized robots. To learn a troll’s behavior, we use contextual anomaly detection to profile each chat user. Using clustering and distance-based methods, we use contextual data such as the group’s current goal, the current time, and the username to classify each point as an anomaly. A user whose features significantly differ from the norm will be classified as a troll. We collected 38 million data points from the viral Internet fad, Twitch Plays Pokemon. Using clustering and distance-based methods, we develop heuristics for identifying trolls. Using MapReduce techniques for preprocessing and user profiling, we are able to classify trolls based on 10 features extracted from a user’s lifetime history.

    You can view the full technical paper here: https://arxiv.org/abs/1902.06208

    Source Code

    Code related to this dataset can be found at: https://github.com/ahaque/twitch-troll-detection

  6. Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data

    • zenodo.org
    • data.niaid.nih.gov
    bin, tsv, txt
    Updated Jun 15, 2023
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    Korosh Moosavi; Korosh Moosavi; Muhammad Aurangzeb Ahmad; Muhammad Aurangzeb Ahmad; Afra Mashhadi; Afra Mashhadi (2023). Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data [Dataset]. http://doi.org/10.5281/zenodo.8012284
    Explore at:
    bin, txt, tsvAvailable download formats
    Dataset updated
    Jun 15, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Korosh Moosavi; Korosh Moosavi; Muhammad Aurangzeb Ahmad; Muhammad Aurangzeb Ahmad; Afra Mashhadi; Afra Mashhadi
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    These are the data and resources used for a Twitch Emote recommendation system using a Word2Vec model. The nature and exploration of the data is described in Emotes-2-Vec: A Large Scale Embedding of Twitch Chat Data. To protect the privacy of the users whose messages were scraped to build this corpus, names and timestamps have been removed and only the message bodies are included. However, a tutorial for this project is included on the project GitHub: https://github.com/KoroshM/Emote-Recommender.

    embeddings.tsv and labeled_metadata.tsv may be used in TensorFlow's embedding projector to visualize the embedding space.

    Note: Model files are the following:
    embeddings.tsv
    labeled_metadata.tsv
    model
    model.model**
    model.wv.vectors.npy

    **Located here: https://drive.google.com/drive/folders/1RZC4JA4CpAcwoo6dOwq_jobTd6dNi_n2?usp=sharing

  7. u

    Goodreads Book Reviews

    • cseweb.ucsd.edu
    json
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    UCSD CSE Research Project, Goodreads Book Reviews [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain reviews from the Goodreads book review website, and a variety of attributes describing the items. Critically, these datasets have multiple levels of user interaction, raging from adding to a shelf, rating, and reading.

    Metadata includes

    • reviews

    • add-to-shelf, read, review actions

    • book attributes: title, isbn

    • graph of similar books

    Basic Statistics:

    • Items: 1,561,465

    • Users: 808,749

    • Interactions: 225,394,930

  8. u

    Steam Video Game and Bundle Data

    • cseweb.ucsd.edu
    json
    + more versions
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    UCSD CSE Research Project, Steam Video Game and Bundle Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain reviews from the Steam video game platform, and information about which games were bundled together.

    Metadata includes

    • reviews

    • purchases, plays, recommends (likes)

    • product bundles

    • pricing information

    Basic Statistics:

    • Reviews: 7,793,069

    • Users: 2,567,538

    • Items: 15,474

    • Bundles: 615

  9. u

    Amazon Question and Answer Data

    • cseweb.ucsd.edu
    json
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    UCSD CSE Research Project, Amazon Question and Answer Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
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    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain 1.48 million question and answer pairs about products from Amazon.

    Metadata includes

    • question and answer text

    • is the question binary (yes/no), and if so does it have a yes/no answer?

    • timestamps

    • product ID (to reference the review dataset)

    Basic Statistics:

    • Questions: 1.48 million

    • Answers: 4,019,744

    • Labeled yes/no questions: 309,419

    • Number of unique products with questions: 191,185

  10. u

    Google Restaurants dataset

    • cseweb.ucsd.edu
    csv
    + more versions
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    UCSD CSE Research Project, Google Restaurants dataset [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    csvAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    This is a mutli-modal dataset for restaurants from Google Local (Google Maps). Data includes images and reviews posted by users, as well as metadata for each restaurant.

  11. u

    Behance Community Art Data

    • cseweb.ucsd.edu
    json
    + more versions
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    UCSD CSE Research Project, Behance Community Art Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    Likes and image data from the community art website Behance. This is a small, anonymized, version of a larger proprietary dataset.

    Metadata includes

    • appreciates (likes)

    • timestamps

    • extracted image features

    Basic Statistics:

    • Users: 63,497

    • Items: 178,788

    • Appreciates (likes): 1,000,000

  12. u

    PDMX

    • cseweb.ucsd.edu
    json
    + more versions
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    UCSD CSE Research Project, PDMX [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    We introduce PDMX: a Public Domain MusicXML dataset for symbolic music processing, including over 250k musical scores in MusicXML format. PDMX is the largest publicly available, copyright-free MusicXML dataset in existence. PDMX includes genre, tag, description, and popularity metadata for every file.

  13. u

    Marketing Bias data

    • cseweb.ucsd.edu
    json
    + more versions
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    UCSD CSE Research Project, Marketing Bias data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
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    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain attributes about products sold on ModCloth and Amazon which may be sources of bias in recommendations (in particular, attributes about how the products are marketed). Data also includes user/item interactions for recommendation.

    Metadata includes

    • ratings

    • product images

    • user identities

    • item sizes, user genders

  14. u

    Social Recommendation Data

    • cseweb.ucsd.edu
    • berd-platform.de
    json
    + more versions
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    UCSD CSE Research Project, Social Recommendation Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
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    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets include ratings as well as social (or trust) relationships between users. Data are from LibraryThing (a book review website) and epinions (general consumer reviews).

    Metadata includes

    • reviews

    • price paid (epinions)

    • helpfulness votes (librarything)

    • flags (librarything)

  15. u

    Product Exchange/Bartering Data

    • cseweb.ucsd.edu
    json
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    UCSD CSE Research Project, Product Exchange/Bartering Data [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
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    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    These datasets contain peer-to-peer trades from various recommendation platforms.

    Metadata includes

    • peer-to-peer trades

    • have and want lists

    • image data (tradesy)

  16. Dota 2 - Pro Players Matches Results 2019 ~ 2021

    • kaggle.com
    Updated Jun 21, 2021
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    Teo Calvo (2021). Dota 2 - Pro Players Matches Results 2019 ~ 2021 [Dataset]. https://www.kaggle.com/teocalvo/dota2-pro-players-matches-2019-202106/tasks
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 21, 2021
    Dataset provided by
    Kaggle
    Authors
    Teo Calvo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    [pt-br]

    Contexto

    Como jogador, estudante de Estatística e curioso, sempre que posso busco maneiras de aplicar meus conhecimentos em projetos práticos. Mais do que isso, tenho muita paixão em compartilhar minhas descobertas e aprendizados com a comunidade por meio de minhas lives na Twitch e vídeos no YouTube.

    Tendo em vista recapitular um projeto que desenvolvi durante minha graduação, resolvemos fazer em live a obtenção dos dados de Partidas profissionais de Dota 2 a partir da API Open Dota. Os dados foram salvos em bancos de dados NoSQL (MongoDB) e também processados em diversas camadas de dados usando o conceito de Data Lake com a engine de processamento Apache Spark.

    Você pode conferir nosso projeto em seu repositório no GitHub.

    Conteúdo

    Este dataset está longe de ser um dado crú, uma vez que passou por diversas etapas de transformações, cruzamentos e agregações. As informações presentes são estatísticas de cada time um dia antes da partida em questão ter início. Tais estatísticas são calculadas a partir das informações das partidas de cada jogador no 6 meses anteriores à partida em questão.

    Assim, cada linha deste dataset possui a informação de qual time ganhou a partida, bem como estatísticas sumarizadas e 'não normalizadas' de cada time.

    Agradecimentos

    Muito obrigado a todos que acompanharam o desenvolvimento deste projeto em nossas lives e nos apoiaram com as inscrições na Twitch. O apoio de voc6es possibilita que levemos Data Science adiante, como por exemplo, compartilhando este dataset com mais pessoas que têm interesse em se desenvolver na área.

    Inspiração

    Nosso desejo enquanto comunidade é fazer com que o ensino chegue cada dia mais próximo das pessoas. E entendo que isso começa no Brasil. Por isso a descrição em pt-br, dando maior foco ao nosso público nacional.

    Se tiver interesse em conhecer mais sobre nosso trabalho, nos acompanhe na Twitch: Téo Me Why .

    [en - Google Translate]

    Context

    As a player, Statistics student and a curious person, I am always looking for ways to apply my skills in real time problems. I also am passionate about sharing my findings and learnings with others through my streaming sessions on Twitch or my Youtube channel.

    With the goal of reusing a project that I worked on during my undergrad, we decided to stream the data acquisition of professional matches of Dota 2 through the Open Dota API. The dataset has been stored in a NoSQL (MongoDB) and it has been processed in several data layers using the Data Lake concept with the Apache Spark processing engine.

    You can check out the project in this repository on GitHub.

    Content

    This dataset is far from being raw data, since it went through several stages of transformations, crossings and aggregations. The information present is each team's statistics one day before the match in question starts. Such statistics are calculated from each player's match information in the 6 months preceding the match in question.

    Thus, each row of this dataset contains information on which team won the match, as well as summarized and 'non-normalized' statistics for each team.

    Acknowledge

    Many thanks to everyone who followed the development of this project in our lives and supported us with registration at Twitch. Your support enables us to take Data Science forward, such as sharing this dataset with more people who are interested in developing in the area.

    Inspiration

    Our desire as a community is to bring teaching closer to people every day. And I understand that this starts in Brazil. That's why the description in pt-br, giving greater focus to our national audience.

    If you are interested in learning more about our work, follow us on Twitch: Téo Me Why .

  17. u

    Pinterest Fashion Compatibility

    • cseweb.ucsd.edu
    json
    + more versions
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    UCSD CSE Research Project, Pinterest Fashion Compatibility [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
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    jsonAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

    Metadata includes

    • product IDs

    • bounding boxes

    Basic Statistics:

    • Scenes: 47,739

    • Products: 38,111

    • Scene-Product Pairs: 93,274

  18. u

    Recipe Pairs

    • cseweb.ucsd.edu
    csv
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    UCSD CSE Research Project, Recipe Pairs [Dataset]. https://cseweb.ucsd.edu/~jmcauley/datasets.html
    Explore at:
    csvAvailable download formats
    Dataset authored and provided by
    UCSD CSE Research Project
    Description

    This is a collection recipes paired with variants, e.g. a recipe matched with a vegan version of the same recipe.

  19. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Statista (2025). Active streamers on Twitch worldwide 2025 [Dataset]. https://www.statista.com/statistics/746173/monthly-active-streamers-on-twitch/
Organization logo

Active streamers on Twitch worldwide 2025

Explore at:
20 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Sep 4, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Jan 2018 - Mar 2025
Area covered
Worldwide
Description

In March 2025, video streaming platform Twitch had approximately *** million active streamers, down from a peak of **** million in January 2021. The platform experienced a boom during the COVID-19 pandemic, when many new users used the platform to connect with friends or try their hand at livestreaming. However, this trend normalized again towards the end of the year, and the streaming space has also grown more competitive as platforms apart from Twitch have evolved to attract streamers and viewers. Popular content categories on Twitch In 2024, most of the leading content categories on Twitch were all gaming-related – except for the top spot: Just Chatting. The general conversation category accumulated *** billion hours of viewing time in the measured period. In March 2025, global Twitch audiences spent around *** million hours watching Just Chatting content on Twitch, with the average viewer count of such content reaching *** thousand. HasanAbi was the most popular Just Chatting streamer on Twitch in the most recently measured month. Game streamers Twitch is very popular with gamers and gaming audiences, and the ranking of the most popular Twitch streamers reflects this. Ninja (real name: Richard Tyler Blevins), the top-ranked streamer on Twitch, had **** million followers in April 2025. Ninja saw a meteoric rise to fame when he was one of the first top-ranked players to stream the then-newly released Fortnite Battle Royale at the end of 2017. Second-ranked ibai (real name: Ibai Llanos Garatea) was ranked second with ***** million followers on Twitch. With more than **** million followers, Imane Anys, better known as Pokimane, was the only woman among the most-followed Twitch streamers worldwide. Overall, women only accounted for **** percent of the top-ranked Twitch channels.

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