Do We Criticise (and Laugh) in the Same Way? Automatic Detection of Multi-Lingual Satirical News in Twitter. Barbieri, F., Ronzano, F., & Saggion, H. In Twenty-Fourth International Joint Conference on Artificial Intelligence, June, 2015. ![Do We Criticise (and Laugh) in the Same Way? Automatic Detection of Multi-Lingual Satirical News in Twitter [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex During the last few years, the investigation of methodologies to automatically detect and characterise the figurative traits of textual contents has attracted a growing interest. Indeed, the capability to correctly deal with figurative language and more specifically with satire is fundamental to build robust approaches in several sub-fields of Artificial Intelligence including Sentiment Analysis and Affective Computing. In this paper we investigate the automatic detection of Tweets that advertise satirical news in English, Spanish and Italian. To this purpose we present a system that models Tweets from different languages by a set of language independent features that describe lexical, semantic and usage-related properties of the words of each Tweet. We approach the satire identification problem as binary classification of Tweets as satirical or not satirical messages. We test the performance of our system by performing experiments of both monolingual and cross-language classifications, evaluating the satire detection effectiveness of our features.Our system outperforms a word-based baseline and it is able to recognise if a news in Twitter is satirical or not with good accuracy. Moreover, we analyse the behaviour of the system across the different languages, obtaining interesting results.
@inproceedings{barbieri_we_2015,
title = {Do {We} {Criticise} (and {Laugh}) in the {Same} {Way}? {Automatic} {Detection} of {Multi}-{Lingual} {Satirical} {News} in {Twitter}},
copyright = {Authors who submit to this conference agree to the following terms: a) Authors transfer their copyrights in their paper to the International Joint Conferences on Artificial Intelligence, Inc. (IJCAI), in order to deal with future requests for reprints, translations, anthologies, reproductions, excerpts, and other publications. This grant will include, without limitation, the entire copyright in the paper in all countries of the world, including all renewals, extensions, and reversions thereof, whether such rights currently exist or hereafter come into effect, and also the exclusive right to create electronic versions of the paper, to the extent that such right is not subsumed under copyright. b) Every named author warrants that he/she is the sole author and owner of the copyright in the paper, except for those portions shown to be in quotations; that the paper is original throughout; and that their right to make the grants set forth above is complete and unencumbered. If anyone brings any claim or action alleging facts that, if true, constitute a breach of any of the foregoing warranties, each author, individually and collectively, will hold harmless and indemnify IJCAI, their grantees, their licensees, and their distributors against any liability, whether under judgment, decree, or compromise, and any legal fees and expenses arising out of that claim or actions, and the undersigned will cooperate fully in any defense IJCAI may make to such claim or action. Moreover, each author agrees to cooperate in any claim or other action seeking to protect or enforce any right the author has granted to IJCAI in the paper. If any such claim or action fails because of facts that constitute a breach of any of the foregoing warranties, each author agrees to reimburse whomever brings such claim or action for expenses and attorney\’s fees incurred therein. c) \ In return for these rights, IJCAI hereby grants to each author, and the employers for whom the work was performed, royalty-free permission to: 1. retain all proprietary rights (such as patent rights) other than copyright and the publication rights transferred to IJCAI; 2. personally reuse all or portions of the paper in other works of their own authorship; 3. make oral presentation of the material in any forum; 4. reproduce, or have reproduced, the paper for the author\’s personal use, or for company use provided that IJCAI copyright and the source are indicated, and that the copies are not used in a way that implies IJCAI endorsement of a product or service of an employer, and that the copies per se are not offered for sale. The foregoing right shall not permit the posting of the paper in electronic or digital form on any computer network, except by the author or the author\’s employer, and then only on the author\’s or the employer\’s own World Wide Web page or ftp site. Such Web page or ftp site, in addition to the aforementioned requirements of this Paragraph, must provide an electronic reference or link back to the IJCAI electronic server (http://www.ijcai.org), and shall not post other IJCAI copyrighted materials not of the author\’s or the employer\’s creation (including tables of contents with links to other papers) without IJCAI\’s written permission; \>5. make limited distribution of all or portions of the above paper prior to publication. 6. In the case of work performed under U.S. Government contract, IJCAI grants the U.S. Government royalty-free permission to reproduce all or portions of the above paper, and to authorize others to do so, for U.S. Government purposes. In the event the above paper is not accepted and published by IJCAI, or is withdrawn by the author(s) before acceptance by IJCAI, this agreement becomes null and void.},
shorttitle = {Do {We} {Criticise} (and {Laugh}) in the {Same} {Way}?},
url = {https://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/view/11364},
abstract = {During the last few years, the investigation of methodologies to automatically detect and characterise the figurative traits of textual contents has attracted a growing interest. Indeed, the capability to correctly deal with figurative language and more specifically with satire is fundamental to build robust approaches in several sub-fields of Artificial Intelligence including Sentiment Analysis and Affective Computing. In this paper we investigate the automatic detection of Tweets that advertise satirical news in English, Spanish and Italian. To this purpose we present a system that models Tweets from different languages by a set of language independent features that describe lexical, semantic and usage-related properties of the words of each Tweet. We approach the satire identification problem as binary classification of Tweets as satirical or not satirical messages. We test the performance of our system by performing experiments of both monolingual and cross-language classifications, evaluating the satire detection effectiveness of our features.Our system outperforms a word-based baseline and it is able to recognise if a news in Twitter is satirical or not with good accuracy. Moreover, we analyse the behaviour of the system across the different languages, obtaining interesting results.},
language = {en},
urldate = {2023-01-10},
booktitle = {Twenty-{Fourth} {International} {Joint} {Conference} on {Artificial} {Intelligence}},
author = {Barbieri, Francesco and Ronzano, Francesco and Saggion, Horacio},
month = jun,
year = {2015},
keywords = {notion},
}
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This grant will include, without limitation, the entire copyright in the paper in all countries of the world, including all renewals, extensions, and reversions thereof, whether such rights currently exist or hereafter come into effect, and also the exclusive right to create electronic versions of the paper, to the extent that such right is not subsumed under copyright. b) Every named author warrants that he/she is the sole author and owner of the copyright in the paper, except for those portions shown to be in quotations; that the paper is original throughout; and that their right to make the grants set forth above is complete and unencumbered. If anyone brings any claim or action alleging facts that, if true, constitute a breach of any of the foregoing warranties, each author, individually and collectively, will hold harmless and indemnify IJCAI, their grantees, their licensees, and their distributors against any liability, whether under judgment, decree, or compromise, and any legal fees and expenses arising out of that claim or actions, and the undersigned will cooperate fully in any defense IJCAI may make to such claim or action. Moreover, each author agrees to cooperate in any claim or other action seeking to protect or enforce any right the author has granted to IJCAI in the paper. If any such claim or action fails because of facts that constitute a breach of any of the foregoing warranties, each author agrees to reimburse whomever brings such claim or action for expenses and attorney’s fees incurred therein. c) In return for these rights, IJCAI hereby grants to each author, and the employers for whom the work was performed, royalty-free permission to: 1. retain all proprietary rights (such as patent rights) other than copyright and the publication rights transferred to IJCAI; 2. personally reuse all or portions of the paper in other works of their own authorship; 3. make oral presentation of the material in any forum; 4. reproduce, or have reproduced, the paper for the author’s personal use, or for company use provided that IJCAI copyright and the source are indicated, and that the copies are not used in a way that implies IJCAI endorsement of a product or service of an employer, and that the copies per se are not offered for sale. The foregoing right shall not permit the posting of the paper in electronic or digital form on any computer network, except by the author or the author’s employer, and then only on the author’s or the employer’s own World Wide Web page or ftp site. Such Web page or ftp site, in addition to the aforementioned requirements of this Paragraph, must provide an electronic reference or link back to the IJCAI electronic server (http://www.ijcai.org), and shall not post other IJCAI copyrighted materials not of the author’s or the employer’s creation (including tables of contents with links to other papers) without IJCAI’s written permission; >5. make limited distribution of all or portions of the above paper prior to publication. 6. In the case of work performed under U.S. Government contract, IJCAI grants the U.S. Government royalty-free permission to reproduce all or portions of the above paper, and to authorize others to do so, for U.S. Government purposes. 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