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.
Tabloid journalism is often criticized for its propensity for exaggeration, sensationalization, scare-mongering, and otherwise producing misleading and low quality news. As the news has moved online, a new form of tabloidization has emerged: ?clickbaiting.? ?Clickbait? refers to ?content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page? [?clickbait,? n.d.] and has been implicated in the rapid spread of rumor and misinformation online. This paper examines potential methods for the automatic detection of clickbait as a form of deception. Methods for recognizing both textual and non-textual clickbaiting cues are surveyed, leading to the suggestion that a hybrid approach may yield best results. This article explores the application of metaphors in news headlines with a view to interrogating their potential for coercion. Coercion in news discourse is understood as a strategic deployment of pragma-linguistic devices, including metaphors, to foreground the representations of socio-political reality that are compatible with the interests of the news outlet rather than those that inform public debate. It is argued that coercion can be exposed through systematic discourse analysis. Methodologically, the study aims to integrate the cognitive and pragmatic approaches to metaphor in regarding it as both a conceptual building block of news representations and a strategic framing device in news discourse. A quantitative and qualitative analysis of a sample of metaphors excerpted from a corpus of 400 most-read headlines from one of the most visited English-language newspaper sites The Daily Mail is conducted to illustrate such coercive applications of metaphor as simplification, imaging, animalization, confronta tion, (de)legitimization, emotionalization, and dramatization. In the course of the analysis it is demonstrated how certain ideologically-biased representations can be coerced through figurative language. This article sets out a framework for a language-oriented analysis of sensationalism in news media. Sensationalism is understood here as a discourse strategy of ‘packaging’ information in news headlines in such a way that news items are presented as more interesting, extraordinary and relevant than might be the case. Unlike previous content analyses of sensational coverage, this study demonstrates how sensationalism is instantiated through specific illocutions, semantic macrostructures, narrative formulas, evaluation parameters, and interpersonal and textual devices. Examples are drawn from a corpus of headlines of the ‘most read’ articles in the online outlet of the British mid-market tabloid Daily Mail compiled in early 2012. Sensationalized instances are identified through surveys and focus group discussions and subsequently analyzed quantitatively and qualitatively. The study is located within the context of media scholarship on news values and current trends in journalism.