10.6084/m9.figshare.4010619.v1
Arkaitz Zubiaga
Arkaitz
Zubiaga
Geraldine Wong Sak Hoi
Geraldine
Wong Sak Hoi
Maria Liakata
Maria
Liakata
Rob Procter
Rob
Procter
PHEME dataset of rumours and non-rumours
figshare
2016
social media
rumours
rumors
journalism
veracity
verification
conversation
classification
tweets
twitter
rumour detection
rumor detection
Natural Language Processing
Pattern Recognition and Data Mining
Knowledge Representation and Machine Learning
Language, Communication and Culture not elsewhere classified
2016-10-24 10:15:23
Dataset
https://figshare.com/articles/dataset/PHEME_dataset_of_rumours_and_non-rumours/4010619
<div>This dataset contains a collection of Twitter rumours and non-rumours posted during breaking news. The five breaking news provided with the dataset are as follows:</div><div><br></div><div>* Charlie Hebdo: 458 rumours (22.0%) and 1,621 non-rumours (78.0%).</div><div>* Ferguson: 284 rumours (24.8%) and 859 non-rumours (75.2%).</div><div>* Germanwings Crash: 238 rumours (50.7%) and 231 non-rumours (49.3%).</div><div>* Ottawa Shooting: 470 rumours (52.8%) and 420 non-rumours (47.2%).</div><div>* Sydney Siege: 522 rumours (42.8%) and 699 non-rumours (57.2%).</div><div><br></div><div>The data is structured as follows. Each event has a directory, with two subfolders, rumours and non-rumours. These two folders have folders named with a tweet ID. The tweet itself can be found on the 'source-tweet' directory of the tweet in question, and the directory 'reactions' has the set of tweets responding to that source tweet.</div><div><br></div><div>This dataset was used in the paper 'Learning Reporting Dynamics during Breaking News for Rumour Detection in Social Media' for rumour detection. For more details, please refer to the paper.</div><div><br></div><div>License: The annotations are provided under a CC-BY license, while Twitter retains the ownership and rights of the content of the tweets.</div>