Social Media Post Categorization

Project Overview:

Objective

Our “Social Media Post Categorization” project aims to create a valuable dataset for training machine learning models to automatically categorize and classify social media posts across various platforms. This dataset will facilitate content analysis, user engagement tracking, and targeted marketing efforts.

Scope

This project involves collecting social media posts from multiple platforms, such as Facebook, Twitter, Instagram, and more, and annotating them with relevant categories.

Social Media Post Categorization
Social Media Post Categorization
Social Media Post Categorization
Social Media Post Categorization

Sources

  • Social Media APIs: Utilize APIs provided by social media platforms to collect publicly available posts.
  • Web Scraping: Extract data from publicly accessible social media profiles, pages, and hashtags.
  • User Contributions: Encourage user contributions by allowing individuals to submit their posts for categorization.
Social Media Post Categorization
Social Media Post Categorization

Data Collection Metrics

  • Total Social Media Posts: 20,000 posts
  • Social Media APIs: 10,000
  • Web Scraping: 5,000
  • User Contributions: 5,000

Annotation Process

Stages

  1. Categorization: Annotate each social media post with one or more relevant categories, such as “Entertainment,” “Technology,” “Health,” and “Sports.”
  2. User Data Privacy: Ensure compliance with privacy regulations by anonymizing any personally identifiable information in the posts.
  3. Metadata Logging: Log metadata, including the platform, post date, and any relevant hashtags or keywords.

Annotation Metrics

  • Social Media Posts with Categories: 20,000
  • Privacy Compliance: Ongoing to ensure compliance
  • Metadata Logging: 20,000
Social Media Post Categorization
Social Media Post Categorization
Social Media Post Categorization
Social Media Post Categorization

Quality Assurance

Stages

Annotation Verification: Implement a validation process involving experts to review and verify the accuracy of post categorizations.
Data Integrity: Employ data cleansing techniques to remove duplicates, spam, or irrelevant posts.
Data Security: Protect sensitive information and maintain the integrity of the dataset.

QA Metrics

  • Annotation Validation Cases: 2,000 (10% of total)
  • Data Cleansing: Remove irrelevant posts and duplicates

Conclusion

The “Social Media Post Categorization” dataset is a valuable resource for understanding and classifying social media content. With a diverse collection of posts, accurate categorizations, and robust privacy and security measures, this dataset empowers businesses and researchers to gain insights, enhance user experiences, and tailor their marketing strategies based on real-world social media data. It contributes to improved content analysis and user engagement tracking in the dynamic landscape of social media.

quality dataset

Quality Data Creation

Guaranteed TAT​

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified​

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance​

HIPAA Compliance

GDPR Compliance​

GDPR Compliance

Compliance and Security​

Compliance and Security

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