@inproceedings{e724cfad5f184a4e8ffd35241cacfec5,
title = "Using sentiment to detect bots on Twitter: Are humans more opinionated than bots?",
abstract = "In many Twitter applications, developers collect only a limited sample of tweets and a local portion of the Twitter network. Given such Twitter applications with limited data, how can we classify Twitter users as either bots or humans? We develop a collection of network-, linguistic-, and application-oriented variables that could be used as possible features, and identify specific features that distinguish well between humans and bots. In particular, by analyzing a large dataset relating to the 2014 Indian election, we show that a number of sentimentrelated factors are key to the identification of bots, significantly increasing the Area under the ROC Curve (AUROC). The same method may be used for other applications as well.",
author = "Dickerson, {John P.} and Vadim Kagan and Subrahmanian, {V. S.}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 ; Conference date: 17-08-2014 Through 20-08-2014",
year = "2014",
month = oct,
day = "10",
doi = "10.1109/ASONAM.2014.6921650",
language = "English (US)",
series = "ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "620--627",
editor = "Xindong Wu and Xindong Wu and Martin Ester and Guandong Xu",
booktitle = "ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining",
address = "United States",
}