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>