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One Million Posts Corpus

A Data Set of German Online Discussions

This is the home of the “One Million Posts” corpus, an annotated data set consisting of user comments posted to an Austrian newspaper website (in German language).

This data set was presented as a short paper at the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017). See the Citation section below.

Data Set Description

DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website, there is a discussion section below each news article where readers engage in online discussions. The data set contains a selection of user posts from the 12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and 1,000,000 unlabeled posts in the data set. The labeled posts were annotated by professional forum moderators employed by the newspaper.

The data set contains the following data for each post:

For each article, the data set contains the following data:

The data is provided in the form of an SQLite database file. See also the commented database schema.

Detailed descriptions of the post selection and annotation procedures are given in the paper.

Annotated Categories

Potentially undesirable content:

Neutral content that requires a reaction:

Potentially desirable content:

Several of these categories are based on respective rules in the community guidelines of the website.


The following table contains some relevant statistics for the data set.

Total number of posts1,011,773
Number of unlabeled posts1,000,000
Number of labeled posts11,773
Number of category annotation decisions58,568
Number of posts taken offline by moderators62,320
Min/Median/Max post length (words)0 / 21 / 500
Vocabulary size (≥ 5 occurrences)129,070
Number of articles12,087
Number of article topics1,229
Number of users31,413
Min/Median/Max number of posts per article1 / 22 / 3,656
Min/Median/Max number of posts per topic1 / 142 / 44,329
Min/Median/Max number of posts per user1 / 5 / 4,682
Min/Median/Max number of users per article1 / 15 / 1,371
Min/Median/Max number of users per topic1 / 78 / 6,874
Number of pos./neg. community votes3,824,806 / 1,096,300

The following table gives the number of labeled examples per category.

Category Does Apply Does Not Apply Total Percentage
Sentiment Negative 1691 1908 3599 47 %
Sentiment Neutral 1865 1734 3599 52 %
Sentiment Positive 43 3556 3599 1 %
Off-Topic 580 3019 3599 16 %
Inappropriate 303 3296 3599 8 %
Discriminating 282 3317 3599 8 %
Possibly Feedback 1301 4737 6038 22 %
Personal Stories 1625 7711 9336 17 %
Arguments Used 1022 2577 3599 28 %

In the first annotation round, three moderators annotated 1,000 randomly selected posts. From this subset of the data, we can estimate the category distributions. The following bar chart shows the distribution for the three annotators (A, B, C) and after a majority vote per post.

category distribution chart

Furthermore, this subset can be used to compute Cohen’s Kappa values to quantify the inter-rater agreement. The following bar chart shows the agreement for each pair of moderators and the average across all pairs for each category.

inter-rater agreement chart

The distribution of posts to topic paths is illustrated below (regular scale on the left, log scale on the right).

The 10 topic paths with the most posts are listed below (see the top 100 here). Topics from the selected time window (2015-06-01 to 2016-05-31) with broad general interest show up, like the European migrant crisis, the 2016 Austrian presidential elections and the Syrian Civil War.

PostsTopic Path

The top 10 category paths with respect to category prevalence are given in


This data set is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Creative Commons License


The data set was first presented at SIGIR 2017:

Dietmar Schabus, Marcin Skowron, Martin Trapp One Million Posts: A Data Set of German Online Discussions Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) pp. 1241-1244 Tokyo, Japan, August 2017

Please cite this paper if you use the data set (BibTeX below). You can download a preprint version of the paper here. The definitive version of the paper is available under DOI 10.1145/3077136.3080711.

  Author    = {Dietmar Schabus and Marcin Skowron and Martin Trapp},
  Title     = {One Million Posts: A Data Set of German Online Discussions},
  Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},
  Pages     = {1241--1244},
  Year      = {2017},
  Address   = {Tokyo, Japan},
  Doi       = {10.1145/3077136.3080711},
  Month     = aug


The code and instructions to reproduce the experimental results presented in the paper can be found in the experiments folder in the GitHub repository.


This research was partially funded by the Google Digital News Initiative. We thank DER STANDARD and their moderators for the interesting collaboration and the annotation of the presented corpus. The GPU used for this research was donated by NVIDIA.