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DeepSeek AI Model Global Social Reaction Log
Social Media and Posts
Tags and Keywords
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Free
About
This collection captures public discourse and engagement on Twitter, now X, surrounding the Chinese AI startup DeepSeek and its revolutionary large language models. DeepSeek gained global notoriety for releasing models claimed to match or exceed industry leaders, such as those from OpenAI and Meta, while requiring substantially less computational investment for training. The data provides insight into market sentiment, tracking reactions related to DeepSeek as well as closely associated industry keywords including NVIDIA, ANTHROPIC, and LLAMA. It serves as a vital resource for understanding the immediate social media impact of major technology disruptions within the AI sector. Users should be aware that due to the nature of keyword collection, some minimally relevant tweets may be present.
Columns
The dataset comprises 20 distinct columns, providing granular details about each social media entry and its author:
- pseudo_id: A numerical identifier for the tweet.
- text: The content of the tweet itself.
- retweetCount, replyCount, likeCount, quoteCount, bookmarkCount, viewCount: Numerical metrics tracking engagement levels for the tweet. For instance, the maximum number of views recorded is 232 million, while the maximum likes reached 133 thousand.
- createdAt: The timestamp indicating when the tweet was posted.
- lang: The dominant language identified in the tweet content (English is the most frequent).
- isReply: A boolean flag indicating whether the entry is a direct reply to another tweet.
- pseudo_inReplyToId, pseudo_conversationId, pseudo_inReplyToUserId: Identifiers used to map conversational context and thread structure.
- pseudo_author_id: A numerical identifier for the user who authored the tweet.
- author_location: The location specified by the author (Note: 40% of records are missing this detail).
- author_followers, author_following: Numerical counts for the author’s follower and following totals.
- author_isVerified, author_isBlueVerified: Boolean indicators showing verification status (e.g., approximately 31% of authors have a blue verification status).
Distribution
The data is delivered in a standard format typically utilized on data platforms, such as CSV. The file, named
for_export_deepseek.csv, has a file size of 140.08 MB. It contains 20 columns and a total of 364 thousand valid records. The dataset is currently static and not scheduled for future updates.Usage
This data product is perfectly suited for several analytical applications, including:
- Market Sentiment Analysis: Gauging immediate public perception and emotional response towards a disruptive technology firm.
- Competitive Intelligence: Comparing the level of public interest DeepSeek generates relative to established industry rivals (OpenAI, Meta).
- Trend Prediction: Analysing the spread and virality of new technological announcements based on retweet and view counts.
- Natural Language Processing (NLP): Utilising the text column for advanced language modelling and topic extraction related to AI advancements.
Coverage
The temporal scope of the records runs from April 2023 through January 2025. Geographically, the data is global, reflecting conversations across various locations, although location data is heavily fragmented. Regarding language, 56% of the entries are in English, with Spanish accounting for 16%, and the remainder split across 67 other languages, providing a multi-lingual view of the discourse.
License
CC0: Public Domain
Who Can Use It
This data resource is essential for professionals focused on the evolving landscape of artificial intelligence:
- Data Scientists and NLP Researchers: For training models that detect sentiment or identify key themes in tech discussions.
- Competitive Intelligence Analysts: To monitor social media buzz and public perception of challenger firms versus market leaders.
- Tech Journalists and Analysts: To quantify the impact and reaction to new AI model releases.
- Social Media Marketing Teams: To understand the dynamics of high-stakes technology communication.
Dataset Name Suggestions
- DeepSeek AI Model Global Social Reaction Log
- DeepSeek & LLM Twitter/X Conversation Metrics
- AI Industry Disruptor Social Media Engagement Data
- DeepSeek Public Sentiment and Reaction Dataset
Attributes
Original Data Source: DeepSeek AI Model Global Social Reaction Log
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