@InProceedings{dusmanu-cabrio-villata:2017:EMNLP2017,
  author    = {Dusmanu, Mihai  and  Cabrio, Elena  and  Villata, Serena},
  title     = {Argument Mining on Twitter: Arguments, Facts and Sources},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2317--2322},
  abstract  = {Social media collect and spread on the Web personal opinions, facts, fake news
	and all kind of information users may be interested in. Applying argument
	mining methods to such heterogeneous data sources is a challenging open
	research issue, in particular considering the peculiarities of the language
	used to write textual messages on social media. In addition, new issues emerge
	when dealing with arguments posted on such platforms, such as the need to make
	a distinction between personal opinions and actual facts, and to detect the
	source disseminating information about such facts to allow for provenance
	verification. In this paper, we apply supervised classification to identify
	arguments on Twitter, and we present two new tasks for argument mining, namely
	facts recognition and source identification. We study the feasibility of the
	approaches proposed to address these tasks on a set of tweets related to the
	Grexit and Brexit news topics.},
  url       = {https://www.aclweb.org/anthology/D17-1245}
}