{"type":"ScholarlyArticle","title":"Learning to Identify Ambiguous and Misleading News Headlines","authors":[{"name":"Wei Wei","slug":"wei-wei","given":"Wei","family":"Wei","affiliation":"Institute of Computer Science and Technology , Peking University The MOE Key Laboratory of Computational Linguistics , Peking University"},{"name":"Xiaojun Wan","slug":"xiaojun-wan","given":"Xiaojun","family":"Wan","affiliation":"Institute of Computer Science and Technology , Peking University The MOE Key Laboratory of Computational Linguistics , Peking University"}],"abstract":"Accuracy is one of the basic principles of journalism. However, it is increasingly hard to manage due to the diversity of news media. Some editors of online news tend to use catchy headlines which trick readers into clicking. These headlines are either ambiguous or misleading, degrading the reading experience of the audience. Thus, identifying inaccurate news headlines is a task worth studying. Previous work names these headlines ``clickbaits'' and mainly focus on the features extracted from the headlines, which limits the performance since the consistency between headlines and news bodies is underappreciated. In this paper, we clearly redefine the problem and identify ambiguous and misleading headlines separately. We utilize class sequential rules to exploit structure information when detecting ambiguous headlines. For the identification of misleading headlines, we extract features based on the congruence between headlines and bodies. To make use of the large unlabeled data set, we apply a co-training method and gain an increase in performance. The experiment results show the effectiveness of our methods. Then we use our classifiers to detect inaccurate headlines crawled from different sources and conduct a data analysis.","doi":"10.24963/ijcai.2017/583","url":"https://ijcai.org/proceedings/2017/583","pdf":"https://ijcai.org/proceedings/2017/0583.pdf","markdown":"https://ijcai.org/proceedings/2017/583.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/583","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Natural Language Processing","track":"Main track","number":583,"pages":{"first":4172,"last":4178},"keywords":["Natural Language Processing: Text Classification","Natural Language Processing: NLP Applications and Tools","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Anand et al., 2016] Ankesh Anand, Tanmoy Chakraborty, and Noseong Park. 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