tianlinzhang668 / hmc2019

Twitter Dataset for "Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter"

Data for "Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter" paper presented at The Web Conference '20.

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Data file contains three columns:

TweetID : The tweet_id needed to query the Twitter API to download the tweet

Label : Class Label

Label	Label Description	Example
0	Figurative Mentions	he is such a headache
1	Other Mentions	try this home headache remedy now
2	Health mentions	i have a headache

Health Keyword : The health keyword mentioned in the tweet. (i.e, cough, cancer, etc)

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If you use this dataset in your work please cite

@inproceedings{10.1145/3366423.3380198,
author = {Biddle, Rhys and Joshi, Aditya and Liu, Shaowu and Paris, Cecile and Xu, Guandong},
title = {Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter},
year = {2020},
isbn = {9781450370233},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3366423.3380198},
doi = {10.1145/3366423.3380198},
booktitle = {Proceedings of The Web Conference 2020},
pages = {1217–1227},
numpages = {11},
keywords = {Figurative Language, Public Health Surveillance, Health Mention Classification, Twitter},
location = {Taipei, Taiwan},
series = {WWW ’20}
}

Please see https://github.com/emory-irlab/PHM2017/ for the original version of our extended dataset provided here.