This dataset has been gathered using the information from the website: twitchtracker.com. The motivation for collecting this dataset was a college assignment, but I thought the public might find it useful as well. The information was gathered using a python script and web-scraping software (selenium).

There is plenty of information to be found out in this dataset, having 17 columns, 7 categorical and 10 numerical column types.

The categorical columns are: name, language, type, 1st and 2nd most streamed game, most active day, and day with most followers gained.

The numerical columns are: rank, stream duration, followers gained, average viewers, average games played (per stream), time streamed, followers, views, games streamed (total), and active days per week.

A few suggestions for analysis: - Average beginner streamer metrics: Find out numerically and visually what goals should an aspiring streamer set for themselves as they embark on their streaming journey - Rising game trends: see which games have been popular lately and are being played/streamed by the most amount of popular streamers - When is the best day for streaming: Figure out the connection between streamers day with most followers gained with other factors in play such as: total followers, most active day, average stream duration and more. - Most efficient follower gaining method: Try to find out if there is any connecting tissues with streamers that are getting the most followers per stream, perhaps they all stream a certain day or most of them play certain games?

Overall, there is lots of potential so play around and have fun!

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.

This is a dataset of users consuming streaming content on Twitch. It contains all streamers, and all users connected in their respective chats, every 10 minutes for 43 days.

Basic statistics Users: 100k Streamers: 162.6k Interactions: 3M Time steps: 6148

Metadata Start and stop times are provided as integers and represent periods of 10 minutes. Stream ID could be used to retrieve a single broadcast segment from a streamer.

• User ID (anonymized)
• Stream ID
• Streamer username
• Time start
• Time stop

This is a dataset containing over 1,200,000 images of twitch real twitch emotes. Most emotes (99.99%) are 28 by 28 Could be used to create a GAN or for other applications. Examples:

GoldGloveTV Tweets Sentiment Analysis

Measuring Engagement and Popularity

By Twitter [source]

About this dataset

This dataset contains sentiment analysis and information on the tweets of GoldGloveTV, one of the most popular gaming Twitch streamers. It includes data such as timestamp, content of the tweet, number of likes and replies, retweet count, URL associated with each tweet, conversation id associated to the given tweet and various other metadata. This dataset offers invaluable insights about the engagement and popularity surrounding GoldGloveTV's tweets. Furthermore, precise analytical operations concerning different aspects can be performed using this data in order to understand user behaviour better. This is a valuable resource for identifying successful strategies employed by GoldGloveTV in terms of marketing his brand or understanding how users engage with his content on this social media platform

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How to use the dataset

1. Exploring the descriptor data: The first step to analyzing this dataset is to explore the descriptive information such as the tweet timestamp, text, likes, reply count and retweet count among others. This will enable you to look at the trend of GoldGloveTV’s engagement and gain an idea of their most popular posts.

2. Analyze sentiment: Another useful way to use this dataset is to analyze sentiment by looking at each individual tweets' polarities (positive/negative) or subjectivity (objective/subjective). This could provide valuable insight on what topics people are generally interested in or enthusiastic about when discussing GoldGloveTV on Twitter.

3. Compare conversations: You can also compare conversations between different tweets with same conversation id if you want a bigger picture of how people are discussing about specific topics related to GoldGloveTV. Additionally, you can use the URL data in order check out any videos that were released alongside certain Tweets for more context (if needed).

4. Visualizing results: Finally, once you have gained all the necessary insights from analysing this data then it's important to visualize them using charts like scatter plots or bar graphs so that it's easier for anyone else looking into your analysis can understand your findings easily and quickly based on what they see in these visuals rather than having them guess through your raw numbers

Research Ideas

• Analyzing the trends of customer feedback over time to determine the sentiment associated with a particular brand or product. This can be used to help companies adjust their promotional strategies and improve their customer experience.

• Use sentiment analysis on Twitter comments related to specific topics could be helpful for creating market research and gathering insights from user feedback.

• Analyzing the sentiment around different hashtags in order to track conversations about current events, products, services, and brands in real-time and measure how people are responding to them

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Twitter.

Live streaming is a unique form of media that creates a direct line of interaction between streamers and viewers. While previous research has explored the social motivations of those who stream and watch streams in the gaming community, there is a lack of research that investigates intimate self-disclosure in this context, such as discussing sensitive topics like mental health on platforms such as Twitch.tv. This study aims to explore discussions about mental health in gaming live streams to better understand how people perceive discussions of mental health in this new media context. The context of live streaming is particularly interesting as it facilitates social interactions that are masspersonal in nature: the streamer broadcasts to a larger, mostly unknown audience, but can also interact in a personal way with viewers. In this study, we interviewed Twitch viewers about the streamers they view, how and to what extent they discuss mental health on their channels in relation to gaming, how other viewers reacted to these discussions, and what they think about live streams, gaming-focused or otherwise, as a medium for mental health discussions. Through these interviews, our team was able to establish a baseline of user perception of mental health in gaming communities on Twitch that extends our understanding of how social media and live streaming can be used for mental health conversations. Our first research question unraveled that mental health discussions happen in a variety of ways on Twitch, including during gaming streams, Just Chatting talks, and through the stream chat. Our second research question showed that streamers handle mental health conversations on their channels in a variety of ways. These depend on how they have built their channel, which subsequently impacts how viewers perceive mental health. Lastly, we learned that viewers’ reactions to mental health discussions depend on their motivations for watching the stream such as learning about the game, being entertained, and more. We found that more discussions about mental health on Twitch led to some viewers being more cautious when talking about mental health to show understanding.

Greekgodx Tweets: Analyzing Conversation Interactions on Social Media

Retweets, Likes, and Mentions

By Twitter [source]

About this dataset

This dataset provides a unique opportunity to unravel the intricacies of a conversational exchange on social media platforms, by exploring the complex interplay between retweets, likes, mentions and replies. Greekgodx is an immensely popular Twitch streamer and YouTuber, whose tweets offer invaluable insights into how people interact with each other on social media networks. Through this data set we can gain an understanding of user engagement levels, the influence of certain topics or interests on conversations, as well as explore new techniques for measuring sentiment in social media conversations. With these tools in hand we will be better equipped to interpret popular conversations occurring online and more confidently make decisions based upon insights gleaned from our analysis

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How to use the dataset

How to use this dataset

This dataset is a useful resource for those wanting to explore and analyze the conversational dynamics that occur on social media platforms. It includes tweets from popular Twitch streamer and YouTuber, Greekgodx, whose content often inspires engagement from his followers as well as other online users. Here you will find various columns that provide an opportunity to investigate this data in a number of ways, such as investigating any retweets or likes he receives in response to his tweets or the mentions he gets from other users.

The data included here consists of four columns: id, tweet_text, timestamp, retweets_count, likes_count and mentions. All of these features help you gain insights into different elements of interaction between Greekgodx and other Twitter users by providing information about when particular tweets were published (timestamp), how many people have engaged with them (retweets count/likes count) or what kind of people are talking about him (mentions). Additionally the id column provides an identifier for each tweet which can be used for further analysis if needed.

To effectively work with this data set one could first use basic visualization techniques like histograms or bar plots to identify any initial trends related to how often Greekgodx is retweeted/liked within certain periods of time or which Twitter users mention him more frequently. Additionally more advanced analysis techniques suchas direct network analysis can be used too if one seeks more detailed insights into relationships between different members on the platform – these could suggest which individuals are most influential in terms replicating content posted by Greek god x or who are most active when engaging with him in conversations publicly on Twitter

Research Ideas

• Analyzing the Impact of Tweets on Popularity: This dataset can be used to analyze how Greekgodx’s tweets are affecting his popularity and viewership, by looking at engagement metrics such as retweets, likes and mentions over time.
• Exploring Network Dynamics: The dataset can be used to explore the network dynamics of conversations taking place on Twitter, by examining relationships between replies, retweets, likes and mentions over time.
• Investigating Sentiment Analysis of Tweets: This dataset provides a great opportunity to understand sentiment analysis on social media platforms by analyzing the sentiment associated with Greekgodx’s tweets using natural language processing techniques (NLP) and understanding how it affects his engagement levels with followers through retweets, likes, mention etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Twitter.

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

The dataset is a set of network traffic traces in pcap/csv format captured from a single user. The traffic is classified in 5 different activities (Video, Bulk, Idle, Web, and Interactive) and the label is shown in the filename. There is also a file (mapping.csv) with the mapping of the host's IP address, the csv/pcap filename and the activity label.

Activities:

Interactive: applications that perform real-time interactions in order to provide a suitable user experience, such as editing a file in google docs and remote CLI's sessions by SSH. Bulk data transfer: applications that perform a transfer of large data volume files over the network. Some examples are SCP/FTP applications and direct downloads of large files from web servers like Mediafire, Dropbox or the university repository among others. Web browsing: contains all the generated traffic while searching and consuming different web pages. Examples of those pages are several blogs and new sites and the moodle of the university. Vídeo playback: contains traffic from applications that consume video in streaming or pseudo-streaming. The most known server used are Twitch and Youtube but the university online classroom has also been used. Idle behaviour: is composed by the background traffic generated by the user computer when the user is idle. This traffic has been captured with every application closed and with some opened pages like google docs, YouTube and several web pages, but always without user interaction.

The capture is performed in a network probe, attached to the router that forwards the user network traffic, using a SPAN port. The traffic is stored in pcap format with all the packet payload. In the csv file, every non TCP/UDP packet is filtered out, as well as every packet with no payload. The fields in the csv files are the following (one line per packet): Timestamp, protocol, payload size, IP address source and destination, UDP/TCP port source and destination. The fields are also included as a header in every csv file.

The amount of data is stated as follows:

Bulk : 19 traces, 3599 s of total duration, 8704 MBytes of pcap files Video : 23 traces, 4496 s, 1405 MBytes Web : 23 traces, 4203 s, 148 MBytes Interactive : 42 traces, 8934 s, 30.5 MBytes Idle : 52 traces, 6341 s, 0.69 MBytes

IGDB.com is a video game database (acquired by Amazon-owned Twitch), intended for both game consumers and video game professionals alike. IGDB stands for Internet Game Database. data_200K.csv is the primary dataset that has video game ratings. A detailed notebook on how this dataset was used for mapping data from csv files and exploratory data analysis is here: https://www.kaggle.com/code/anudeepvanjavakam/exploring-video-game-ratings This dataset of more than 200K games is collected from IGDB API using igdb-api-v4 for python