First founded in 2011, the live streaming video platform, Twitch, is one of the most popular ways in which viewers watch their favorite gamers play a variety of online video games. Streamer ibai (real name: Ibai Llanos) currently holds the record with the highest number of peak concurrent viewers in 2025 with 710,473.

In May 2025, the United States accounted for over 23 percent of Twitch's desktop traffic worldwide. Germany accounted for a further 8.69 percent of the streaming platform's desktop traffic. Twitch has gained massively in popularity during coronavirus-related lockdowns in 2020.

twitch.tv is ranked #37 in US with 1.09B Traffic. Categories: Computer and Video Games, Online Services. Learn more about website traffic, market share, and more!

Collection of chat log of 2,162 Twitch streaming videos by 52 streamers. Time period of target streaming video is from 2018-04-24 to 2018-06-24. Description of columns follows below: body: Actual text for user chat channel_id: Channel identifier (integer) commenter_id: User identifier (integer) commenter_type: User type (character) created_at: Time of when chat was entered (ISO 8601 date and time) fragments: Chat text including parsing information of Twitch emote (JSON list) offset: Time offset between start time of video stream and the time of when chat was entered (float) updated_at: Time of when chat was edited (ISO 8601 date and time) video_id: Video identifier (integer) File name indicates name of Twitch stream channel. This dataset is saved as python3 pandas.DataFrame with python pickle format. import pandas as pd pd.read_pickle('ninja.pkl')

We'll tailor a Twitch dataset to meet your unique needs, encompassing streamer profiles, viewer engagement metrics, streaming times, demographic data of viewers, follower counts, chat statistics, and other pertinent metrics.

Leverage our Twitch datasets for diverse applications to bolster strategic planning and market analysis. Scrutinizing these datasets enables organizations to grasp viewer preferences and streaming trends, facilitating nuanced content development and engagement initiatives. Customize your access to the entire dataset or specific subsets as per your business requisites.

Popular use cases involve optimizing content strategy based on streamer performance and viewer engagement, enhancing marketing strategies through targeted audience segmentation, and identifying and forecasting trends in the streaming community to stay ahead in the digital entertainment landscape.

Introduction

Twitch Statistics: Twitch is referred to as the premier platform for video game streaming, attracting millions of users daily who engage in conversation, interaction, and entertainment. By the year 2026, Twitch accounted for over 91% of all streaming content and commanded 76% of the market share in total viewing hours. At any given time, approximately 2.73 million viewers are present on the platform. Continue reading to uncover more interesting Twitch statistics.

Discover information regarding its partners, active streaming channels, popular categories, historical streaming data, and developing trends. For viewers, statistics concerning popular games or streamers assist them in finding content that aligns with their interests. Additionally, Twitch offers tools for performance analysis, while third-party websites provide even more complete data. Whether you are a content creator or simply nosy, understanding Twitch statistics can keep you updated on trends and the platform's expansion.

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

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.

As of May 2025, users between the ages of ************** accounted for the biggest share of Twitch.tv users worldwide. During the measured period, users in this age group amounted to over ** percent of Twitch online audiences. Users aged between *************** were the second largest demographic accessing with Twitch.

A complete list of live websites using the twitch.tv technology, compiled through global website indexing conducted by WebTechSurvey.

About this Dataset

Twitch stream data collected from ~2500 popular Twitch streamers over 4 months (9/24/2020 to 2/05/2021). Real time data for live streamers updated approx. every 5 minutes. Dataset includes current timestamp, streamer name, stream title, game_id, stream start time, and viewership count. Contains 7,936,251 live stream data instances.

Category

Machine Learning

Keywords

social network,twitch,social media,Machine Learning,viewership

Row Count

7936132

Price

$40.00

This dataset contains application and network measurements of 6,982 individual Twitch.tv streaming sessions of 222 different streamers summing up to more than 1,000h live streaming. The data are aggregated to uplink requests.

League of Legends (LOL) is the most popular game on PC, drawing 8 million concurrent players. A common activity of gamers, besides playing games, is to watch other players presenting tips and tricks. Streaming platforms allow some players to show gameplays and live games. Twitch.tv is the world´s leading live streaming platform. Considering that hate speech is a ubiquitous problem in online gaming, we collected 985,766 comments from five videos of the top 10 LOL streamers in Twitch.tv platform. The dataset is freely available in a single file, ensembling all videos/players; and divided by players as well. These comments are a rich data source for opinion mining, sentiment analysis, topic modeling, and hate speech detection (including sexism and racism).

Who are the source data producers?

[Alle im Stream Chat aktiven Zuschauer] Danke an alle die, die diesen Datensatz ermöglicht haben. Ebenso bedanke ich mich bei allen die den Stream finanziell unterstützt haben. Wie versprochen, hab ich das Geld für 'unnützes' rausgeworfen. (in diesem Falle für Rechenleistung :-D ) (Für eventuelle Interessierten: 'by me a coffee on Twitch')

  Dataset Details

Als 'input' wurde sich nach den Zuschauernachrichten gerichtet. 'instruct' und… See the full description on the dataset page: https://huggingface.co/datasets/gulaschnascher4000/stream-dataset-0-1.

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.

As of April 2025, **************************************** was the most followed live streamer on Twitch with **** million followers. ***** 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 and beginning of 2018. Spanish streamer ************************************* was ranked second with ***** million followers on Twitch. The top games on Twitch Despite being released way back in 2013, GTA V is still among the most popular games on Twitch. The action adventure classic accounted for *** percent of total hours watched on the platform in February 2025. In contrast, the most popular game on Twitch in terms of average number of channels was Fortnite - the battle royale title had ***** channels streaming the game in April 2025. Other top-ranked games include Valorant, Apex Legends, and Call of Duty: Modern Warfare II. Twitch - how it all began Founded in 2011, Twitch was initially launched as a spin-off of the general-interest streaming platform Justin.tv. The site primarily focuses on video gaming, including play through of video games, broadcasts of e-sports competitions, and more recently, creative content. Twitch Creative, the outlet for creative content, was launched on October 28, 2015. The launch included a marathon of Bob Ross ' "The Joy of Painting". Some of the most popular channels on Twitch are owned by YouTube gaming personalities, who are taking advantage of the website’s live-streaming capabilities to interact more with their audiences. Users are able to participate in a live chat during the video broadcast, with broadcasters frequently reacting to and replying to comments and questions from the chat in real time. In August 2014, Twitch was acquired by Amazon for *** million U.S. dollars. It was the company’s fifth most expensive acquisition of all time.

A subset of the chat logs dump from Twitch used in this work is provided, to help replicate the central findings of this work (https://doi.org/10.5281/zenodo.1182793). Data are aggregated and include the number of messages posted in each channel and the number of users posting them, sampled at intervals of 5 minutes. To protect the identity of the users in this data collection, message contents and user names are not included in this dataset. Stream names have been replaced with numeric IDs.
No additional filtering or data cleaning operation has been applied to this data. Replication code is available on Github (https://github.com/glciampaglia/twitch-overload-replication).

We are publishing a dataset we created for the HTTPS traffic classification.

Since the data were captured mainly in the real backbone network, we omitted IP addresses and ports. The datasets consist of calculated from bidirectional flows exported with flow probe Ipifixprobe. This exporter can export a sequence of packet lengths and times and a sequence of packet bursts and time. For more information, please visit ipfixprobe repository (Ipifixprobe).

During our research, we divided HTTPS into five categories: L -- Live Video Streaming, P -- Video Player, M -- Music Player, U -- File Upload, D -- File Download, W -- Website, and other traffic.

We have chosen the service representatives known for particular traffic types based on the Alexa Top 1M list and Moz's list of the most popular 500 websites for each category. We also used several popular websites that primarily focus on the audience in our country. The identified traffic classes and their representatives are provided below:

Live Video Stream Twitch, Czech TV, YouTube Live

Video Player DailyMotion, Stream.cz, Vimeo, YouTube

Music Player AppleMusic, Spotify, SoundCloud

File Upload/Download FileSender, OwnCloud, OneDrive, Google Drive

Website and Other Traffic Websites from Alexa Top 1M list

VOICE https://www.twitch.tv/mazellovvv rvc v2 300 epoch.

Twitch-FIFA is video-context, many-speaker dialogue dataset based on live-broadcast soccer game videos and chats from Twitch.tv. This dataset can be used to train visually-grounded dialogue models that generate relevant temporal and spatial event language from the live video, while also being relevant to the chat history.