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Medium ML and AI Content Repository
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Gain insights into the world of tech writing with this collection of over 100,000 data science articles published on Medium throughout 2020. Originally compiled to help beginner writers optimise their text for data science topics, this resource aggregates content featuring tags such as Data Science, Machine Learning, Artificial Intelligence, and Big Data. It provides a granular look at article performance metrics, author details, and publication timing, offering a valuable snapshot of the industry's discourse during a pivotal year.
Columns
- url: The direct URL link to the article.
- title: The headline or title of the published article.
- author: The Medium username or handle of the writer.
- author_page: The URL linking to the author's profile page.
- subtitle: The article's subtitle or brief description (note: approximately 64% of records may be null).
- claps: The number of 'claps' (likes/upvotes) the article received, serving as an indicator of popularity.
- responses: The count of comments or responses posted on the article.
- reading_time: The estimated time required to read the article, measured in minutes.
- tag: The primary category tag associated with the article (e.g., Data Science, Machine Learning).
- date: The specific date the article was published in 2020.
Distribution
- Format: CSV
- Size: 28.05 MB
- Structure: 10 columns, approximately 108,000 rows (records).
Usage
- Content Strategy: Analyse titles and subtitles to determine which keywords drive higher engagement (claps and responses).
- Trend Analysis: Track the popularity of specific topics like 'Deep Learning' or 'Big Data' over the course of 2020.
- Author Performance: Evaluate the relationship between author productivity and reader engagement.
- Optimal Publishing Times: Investigate correlations between publication dates and article performance metrics.
- Sentiment Analysis: Perform text mining on titles to gauge the sentiment of data science discourse.
Coverage
- Geographic Scope: Global (Online Platform).
- Time Range: 1st January 2020 to 31st December 2020.
- Demographic: Content creators and readers interested in Data Science, AI, and Technology on Medium.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For practising natural language processing (NLP) and regression analysis on engagement metrics.
- Content Marketers: To research successful content structures and headline formulas in the tech sector.
- Social Media Analysts: To understand community engagement dynamics on professional blogging platforms.
- Academic Researchers: Studying the evolution of data science terminology and trends.
Dataset Name Suggestions
- Medium Data Science Articles 2020
- 2020 Tech Article Metrics
- Medium ML and AI Content Repository
- Data Science Writing Benchmarks 2020
Attributes
Original Data Source: Medium ML and AI Content Repository
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