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Twitter Influencer and Engagement Metrics for Machine Learning
Social Media and Posts
Tags and Keywords
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Free
About
Twitter discourse surrounding Automated Machine Learning (#AutoML) throughout 2020 provides a window into the professional and academic interest in automated systems. Extracted specifically for an analysis related to a global data science survey, these records capture the pulse of the developer community as they share resources, learning paths, and platform updates. By focusing on a specific technological niche during a pivotal year, the information aids in understanding how technical trends propagate through social networks and which influencers lead the conversation in the machine learning ecosystem. Analysing these interactions is like observing a digital beehive; while each individual post is small, the collective movement reveals exactly where the most valuable "pollen" of knowledge is being gathered.
Columns
- S.No: A sequential index used to categorise and organise each record in the collection.
- username: The unique handle of the Twitter user, featuring prominent accounts such as 'TheSecretJunio1' and 'iPythonistaBot'.
- description: The biography or profile text provided by the user, offering context on their professional background or interests.
- location: The self-reported geographical origin of the user, with Mongolia being a frequently cited region.
- following: The total number of other accounts the user follows, indicating their level of engagement with the wider platform.
- followers: The count of users following the account, serving as a proxy for the user's influence within the community.
- totaltweets: The cumulative volume of posts made by the account since its inception.
- retweetcount: A metric showing how many times the specific #AutoML tweet was shared by other users.
- text: The actual content of the tweet, including technical discussions, links to learning paths, and various hashtags.
- hashtags: The specific tags used to categorise the post, such as #AI, #DataScience, and #Python.
Distribution
The information is delivered in a CSV file titled
tweets_data.csv with a file size of 263.99 kB. It consists of 553 valid records and 10 distinct columns, maintaining 100% data integrity for core fields with no mismatched entries. The dataset represents a static snapshot and is not scheduled for further updates.Usage
This resource is ideal for performing sentiment analysis and natural language processing on technical social media content. It is well-suited for identifying key influencers and automated bots that drive discourse in the machine learning field. Additionally, researchers can use these records to track the popularity of specific developer platforms and study how educational content is shared within the #AutoML community.
Coverage
The scope of the data is strictly temporal, covering the calendar year of 2020. Geographically, the records include a global audience, though approximately 38% of users did not specify a location. Demographically, the collection represents a diverse group of contributors, from automated bots to junior developers, with participants spanning from various regions including Mongolia.
License
CC0: Public Domain
Who Can Use It
Data Scientists and Analysts can leverage these records to study the evolution of automated machine learning trends. Social Media Researchers might utilise the collection to map the network of retweets and community interactions. Furthermore, Educational Content Creators can find this a valuable primary source for identifying the most common questions and shared resources within the developer ecosystem.
Dataset Name Suggestions
- 2020 #AutoML Twitter Discourse and Sentiment Archive
- Automated Machine Learning Social Media Trends Index
- Global Developer Conversations: The #AutoML 2020 Registry
- Twitter Influencer and Engagement Metrics for Machine Learning
- Historical #AutoML Community Interaction Dataset
Attributes
Original Data Source: Twitter Influencer and Engagement Metrics for Machine Learning
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