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Dataset Card for "dkstance / DAST"
Dataset Summary
This is an SDQC stance-annotated Reddit dataset for the Danish language generated within a thesis project. The dataset consists of over 5000 comments structured as comment trees and linked to 33 source posts.
The dataset is applicable for supervised stance classification and rumour veracity prediction.
Supported Tasks and Leaderboards
- Stance prediction
Languages
Dataset Structure
Data Instances
DAST / dkstance
- Size of downloaded dataset files: 4.72 MiB
- Size of the generated dataset: 3.69 MiB
- Total amount of disk used: 8.41 MiB
An example of 'train' looks as follows.
{
'id': '1',
'native_id': 'ebwjq5z',
'text': 'Med de udfordringer som daginstitutionerne har med normeringer, og økonomi i det hele taget, synes jeg det er en vanvittig beslutning at prioritere skattebetalt vegansk kost i daginstitutionerne. Brug dog pengene på noget mere personale, og lad folk selv betale for deres individuelle kostønsker.',
'parent_id': 'a6o3us',
'parent_text': 'Mai Mercado om mad i daginstitutioner: Sund kost rimer ikke på veganer-mad',
'parent_stance': 0,
'source_id': 'a6o3us',
'source_text': 'Mai Mercado om mad i daginstitutioner: Sund kost rimer ikke på veganer-mad',
'source_stance': 0
}
Data Fields
id
: astring
feature.native_id
: astring
feature representing the native ID of the entry.text
: astring
of the comment text in which stance is annotated.parent_id
: thenative_id
of this comment's parent.parent_text
: astring
of the parent comment's text.parent_stance
: the label of the stance in the comment towards its parent comment.
0: "Supporting",
1: "Denying",
2: "Querying",
3: "Commenting",
source_id
: thenative_id
of this comment's source / post.source_text
: astring
of the source / post text.source_stance
: the label of the stance in the comment towards the original source post.
0: "Supporting",
1: "Denying",
2: "Querying",
3: "Commenting",
Data Splits
name | size |
---|---|
train | 3122 |
validation | 1066 |
test | 1060 |
These splits are specified after the original reserach was reported. The splits add an extra level of rigour, in that no source posts' comment tree is spread over more than one partition.
Dataset Creation
Curation Rationale
Comments around rumourous claims to enable rumour and stance analysis in Danish
Source Data
Initial Data Collection and Normalization
The data is from Reddit posts that relate to one of a specific set of news stories; these stories are enumerated in the paper.
Who are the source language producers?
Danish-speaking Twitter users.
Annotations
Annotation process
There was multi-user annotation process mediated through a purpose-built interface for annotating stance in Reddit threads.
Who are the annotators?
- Age: 20-30.
- Gender: male.
- Race/ethnicity: white northern European.
- Native language: Danish.
- Socioeconomic status: higher education student.
Personal and Sensitive Information
The data was public at the time of collection. User names are not preserved.
Considerations for Using the Data
Social Impact of Dataset
There's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.
Discussion of Biases
The source of the text has a strong demographic bias, being mostly young white men who are vocal their opinions. This constrains both the styles of language and discussion contained in the data, as well as the topics discussed and viewpoints held.
Other Known Limitations
The above limitations apply.
Additional Information
Dataset Curators
The dataset is curated by the paper's authors.
Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0. An NLP data statement is included in the paper describing the work, https://aclanthology.org/W19-6122.pdf
Citation Information
@inproceedings{lillie-etal-2019-joint,
title = "Joint Rumour Stance and Veracity Prediction",
author = "Lillie, Anders Edelbo and
Middelboe, Emil Refsgaard and
Derczynski, Leon",
booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
month = sep # "{--}" # oct,
year = "2019",
address = "Turku, Finland",
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/W19-6122",
pages = "208--221",
}
Contributions
Author-added dataset @leondz
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