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

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.

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Publication Details

Journal
Scientific Data
Published
2026-04-27
DOI
https://doi.org/10.1038/s41597-026-07267-3
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Andreas Kaltenbrunner, Vicenç Gómez, Emma Fraxanet, Max Pellert
Scientific Data
Computational and Text Analysis Methods
article

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

Andreas Kaltenbrunner, Vicenç Gómez, Emma Fraxanet, Max Pellert
article en

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.

Scientific Data
Universitat Oberta de Catalunya (ES), Universitat Pompeu Fabra (ES), Barcelona Supercomputing Center (ES), Universitat Politècnica de Catalunya (ES)
European Commission, Agencia Estatal de Investigación
Openalex Percentile: Top 99%
Computational and Text Analysis Methods
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