A Decade of News Forum Interactions: Threaded Conversations, Signed Votes, and Topical Tags

2025-06-27

AI Summary

This study addresses the scarcity of long-term, large-scale, privacy-compliant online user interaction data for medium-resource languages—specifically German. To this end, we construct an anonymized dataset from a German news forum spanning ten years, comprising 75 million comments and 400 million votes. It is the first publicly released resource to unify decade-long structured conversation trees, explicit upvote/downvote annotations, edit histories, and topic tags. User and comment identifiers are persistently anonymized via salted hashing, and comment embeddings are generated using state-of-the-art transformer-based models to preclude raw text disclosure. Designed to meet core computational social science requirements—including analysis of discussion dynamics, social network evolution, and semantic change—the dataset adheres strictly to GDPR privacy principles. It constitutes the first high-quality, reusable, longitudinal benchmark for studying German-language online public discourse.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

We present a large-scale, longitudinal dataset capturing user activity on the online platform of DerStandard, a major Austrian newspaper. The dataset spans ten years (2013-2022) and includes over 75 million user comments, more than 400 million votes, and detailed metadata on articles and user interactions. It provides structured conversation threads, explicit up- and downvotes of user comments and editorial topic labels, enabling rich analyses of online discourse while preserving user privacy. To ensure this privacy, all persistent identifiers are anonymized using salted hash functions, and the raw comment texts are not publicly shared. Instead, we release pre-computed vector representations derived from a state-of-the-art embedding model. The dataset supports research on discussion dynamics, network structures, and semantic analyses in the mid-resourced language German, offering a reusable resource across computational social science and related fields.

Problem

• Analyze online discourse dynamics using threaded conversations and votes
• Study network structures and semantic analyses in German language
• Provide anonymized dataset for computational social science research

Innovation

• Anonymized data using salted hash functions
• Pre-computed vector representations from embeddings
• Structured threads with votes and topic labels