Datasets:
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
pretty_name: lrs_17
size_categories:
- 1K<n<10K
Dataset Details
The Longitudinal Rumour Stance (LRS) is a longitudinal version of the RumourEval-2017 dataset. It consists of Twitter conversations around newsworthy events. The source tweet of the conversation conveys a rumourous claim, discussed by tweets in the stream. In 325 conversations, a total of 5,568 posts are labelled based on their stance towards the rumourous claim in the corresponding source tweet as either Supporting, Denying, Questioning or Commenting. We convert conversation structure and labels into a Longitudinal Stance Switch Detection task.
Specifically, conversations are converted from tree-structured into linear timelines to obtain chronologically ordered lists. Then the original stance labels from the RumourEval-2017 dataset are converted into a binary label of Switch and No Switch categories based on the numbers of supporting tweets versus denying and questioning ones at each point in time.
More precisely this longitudinal task captures switches in overall user stance:
- Switch: switch between the total number of oppositions (query/deny) and supports or vv
- No Switch: absence of a switch or balance between the numbers of supporting and opposing posts
Dataset Structure
id: a type float64 unique id for each tweet
timeline_id: a string type unique id for each timeline. A timeline is a linear chronologically ordered conversation converted from the tree-structured conversation.
datetime: date and time of the tweet
text: a string type text of the tweet
label_binary: a type float64 binary label of either Switch (1) or No Switch (0)
label: a type float64 binary label of either No Switch (0), a switch in stance from supporting to opposing the claim starts (-1), a switch in stance from opposing to supporting the claim starts (1), a switch in stance from supporting to opposing the claim continues (-2), a switch in stance from opposing to supporting the claim continues (2).
Data Splits
Each split has unique timelines.
| set | samples | timelines |
|---|---|---|
| train | 4,238 | 272 |
| dev | 281 | 25 |
| test | 1,049 | 28 |
Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0.
Models
Code for a number of models ran on this dataset can be found at the Git Repository of the Sig-Networks Demo Paper
Citation Information
@article{tseriotou2023sig,
title={Sig-networks toolkit: Signature networks for longitudinal language modelling},
author={Tseriotou, Talia and Chan, Ryan Sze-Yin and Tsakalidis, Adam and Bilal, Iman Munire and Kochkina, Elena and Lyons, Terry and Liakata, Maria},
journal={arXiv preprint arXiv:2312.03523},
year={2023}
}
@article{derczynski2017semeval,
title={SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours},
author={Derczynski, Leon and Bontcheva, Kalina and Liakata, Maria and Procter, Rob and Hoi, Geraldine Wong Sak and Zubiaga, Arkaitz},
journal={arXiv preprint arXiv:1704.05972},
year={2017}
}
Acknowledgement
This dataset was curated by Iman Munire Bilal