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
More Datasets
For more datasets, click here.
Featured Notebooks
- 🚨 Your notebook can be here! 🚨!
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.