--- annotations_creators: - expert-generated language_creators: - crowdsourced language: - de license: - cc-by-nc-sa-4.0 multilinguality: - monolingual size_categories: - 10K

Brief von BP, Total, Shell, Statoil, BG Group und Eni unterzeichnet

Paris/London/La Defense - Sechs große Öl- und Gaskonzerne haben mit Blick auf die Verhandlungen über einen neuen Welt-Klimavertrag ein globales Preissystem für CO2-Emissionen gefordert. Wenn der Ausstoß von CO2 Geld kostet, sei dies ein Anreiz für die Nutzung von Erdgas statt Kohle, mehr Energieeffizienz und Investitionen zur Vermeidung des Treibhausgases, heißt es in einem am Montag veröffentlichten Brief.

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Das Schreiben ist unterzeichnet von BP, Total, Shell, Statoil, BG Group und Eni. Die Unternehmen versicherten, sie seien bereit, ihren Teil zum Kampf gegen den Klimawandel beizutragen. Dafür sei aber ein klarer und verlässlicher Politik-Rahmen nötig. (APA, 1.6.2015)

' } ``` ### Data Fields The data set contains the following data for each post: * **ID_Post**: Post ID * **ID_Parent_Post**: Parent post (replies give rise to tree-like discussion thread structures) * **ID_Article**: Article ID * **ID_User**: User ID (the user names used by the website have been re-mapped to new numeric IDs) * **Headline**: Headline (max. 250 characters) * **Body**: Main Body (max. 750 characters) * **CreatedAt**: Time stamp * **Status**: Status (online or deleted by a moderator) * **PositiveVotes**: Number of positive votes by other community members * **NegativeVotes**: Number of negative votes by other community members Labeled posts also contain: * **Category**: The category of the annotation, one of: ArgumentsUsed, Discriminating, Inappropriate, OffTopic, PersonalStories, PossiblyFeedback, SentimentNegative, SentimentNeutral, SentimentPositive * **Value**: either 0 or 1, explicitly indicating whether or not the post has the specified category as a label (i.e. a category of `ArgumentsUsed` with value of `0` means that an annotator explicitly labeled that this post doesn't use arguments, as opposed to the mere absence of a positive label). * **Fold**: a number between [0-9] from a 10-fold split by the authors For each article, the data set contains the following data: * **ID_Article**: Article ID * **publishingDate**: Publishing date * **Path**: Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1) * **Title**: Title * **Body**: Body ### Data Splits Training split only. | name | train | |-----------------|--------:| | posts_labeled | 40567 | | posts_unlabeled | 1000000 | | articles | 12087 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information This data set is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. ### Citation Information ``` @InProceedings{Schabus2018, author = {Dietmar Schabus and Marcin Skowron}, title = {Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website}, booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC)}, year = {2018}, address = {Miyazaki, Japan}, month = may, pages = {1602-1605}, abstract = {This paper describes an approach and our experiences from the development, deployment and usability testing of a Natural Language Processing (NLP) and Information Retrieval system that supports the moderation of user comments on a large newspaper website. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods. We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial aspects. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting.}, url = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/8885.html}, } ``` ### Contributions Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset.