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.

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.

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.

Context

Gaming is a very big industry now. Every year there are millions of Dollars invested in Esports and many new companies want to invest in the Esports scene now. One of bigegest ever deals was when Mixer opened up and brought Ninja and Shroud to their platform from twitch. But Twitch has been a home to streamers since day 1 and now that Mixer has been shut down, streamers are returning to the platform again.Millions, if not billions, watch twitch streams everyday and i myself like to watch twitch streams. So i put together Top 1000 Streamers from past one year who were streaming on twitch.

Content

This data consists of different things like number of viewers, number of active viewers, followers gained and many other relevant columns regarding a particular streamer. It has 11 different columns with all the necessary information that is needed.

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.

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

This dataset contains data for the entire collection of cards for Hearthstone, the popular online card game by Blizzard. Launching to the public on March 11, 2011 after being under development for almost 5 years, Hearthstone has gained popularity as a freemium game, launching into eSports across the globe, and the source of many Twitch channels.

The data in this dataset was extracted from hearthstonejson.com, and the documentation for all the data can be found on the cards.json documentation page.

The original data was extracted from the actual card data files used in the game, so all of the data should be here, enabling explorations like:

• Card strengths and weaknesses
• Card strengths relative to cost and rarity
• Comparisons across player classes, bosses, and sets
• Whether a set of optimal cards can be determined per class

The cards can be explored in one of four ways:

• cards.json: The raw JSON pulled from hearthstonejson.com
• cards_flat.csv: A flat CSV containing a row for each card, and any n:m data stored as arrays in single fields
• database.sqlite: A SQLite database containing relational data of the cards
• cards.csv, mechanics.csv, dust_costs.csv, play_requirements.csv, and entourages.csv: the normalized data in CSV format.

This dataset will be updated as new releases and expansions are made to Hearthstone.

Currently, any localized string values are in en-us, but I may look into adding other languages if the demand seems to be there.