Social Media Data Toolkit: Standardization and Anonymization of Social Network Datasets

2026-04-30

AI Summary

This study addresses the challenges of cross-platform social media data analysis—namely data heterogeneity, API restrictions, and privacy compliance—stemming from the absence of standardized, reproducible workflows. To overcome these limitations, the authors propose an open-source Python framework featuring a unified data model that harmonizes multi-source social data across five core dimensions: communities, accounts, posts, behaviors, and entities. The framework incorporates a configurable personally identifiable information (PII) anonymization module to ensure regulatory compliance and integrates an LLM-driven analytical layer that enables semantic enrichment without requiring code modifications. Evaluated through four case studies, the framework demonstrates significant improvements in fairness, reproducibility, and scalability for cross-platform textual and network analyses.

Abstract

The rapid diversification of social media platforms and the increasing restrictions on official APIs have significantly complicated cross-platform analysis. Researchers are often forced to rely on heterogeneous datasets obtained through web scraping and historical archives; however they often lack structural consistency. Prior to conducting cross-platform social media analyses, one needs to answer three critical questions: (1) What makes platforms different and similar? (2) How were the datasets collected? (3) How can we align the datasets of different platforms to conduct fair analyses? To address these questions, we introduce the Social Media Data Toolkit (\projectname{}), a comprehensive Python framework designed for the standardization, anonymization, and enrichment of social network datasets. \projectname{} unifies diverse data structures into a generic schema comprising Communities, Accounts, Posts, Actions, and Entities to facilitate multi-platform research. The framework features a configurable anonymization module to secure Personally Identifiable Information (PII) and an extendable enrichment layer that integrates Large Language Models (LLMs) and network analysis tools for downstream tasks such as stance detection and toxicity scoring without creating codebase for different datasets. We demonstrate the versatility of \projectname{} through four case studies spanning from textual analysis of the content to network analysis across platforms. To offer reproducible social media research, \projectname{} is released as an open-source tool featuring detailed documentation and practical guides for researchers at any skill-level. It can be accessed at github.com/ViralLab/SMDT and varollab.com/SMDT.

Problem

• cross-platform analysis
• social media datasets
• data standardization
• structural heterogeneity
• reproducible research

Innovation

• standardization
• anonymization
• cross-platform analysis
• large language models
• social network datasets