Title: A comparative linguistic analysis of English news headlines in China, USA, UK, and ASEAN countries
Authors: Yusha Zhang; Xiaoming Lu; Yingwen Fu; Shengyi Jiang
Addresses: School of Electronic Information, Hunan Institute of Information Technology, Changsha, 410151, China ' School of Interpreting and Translation Studies, Guangdong University of Foreign Studies, Guangzhou Guangdong, 510420, China ' School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou Guangdong, 510420, China ' School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou Guangdong, 510420, China
Abstract: This paper aims to conduct a comparative study on English news headlines in the USA, China, the UK, and ASEAN countries, mainly investigating how the composing of news headlines is interrelated with its linguistic factors such as the part of speech, length and the frequency of most common words depending on which country the news is published in. In this paper, the linguistic comparison is performed based on merely the headlines without going through whole articles. For this purpose, 13 sets of data are collected from major online news sites within above-mentioned countries. The comparison results reveal that headlines in different countries comply with news writing rules in slightly different ways as well as boast distinctive features. These differences are attributed to the consideration of the target audience's multi-faceted states such as knowledge states, beliefs, or interests. To better exemplify the results, the headlines in this article were read with care. The proposed method begins with data collection and pre-processing. News headlines are then fetched from different news sources using crawler and processed in natural language processing tool (NLTK).
Keywords: headline; part of speech; length of headlines; cluster; China; USA; UK.
International Journal of Computational Science and Engineering, 2020 Vol.23 No.3, pp.271 - 285
Received: 04 Apr 2020
Accepted: 15 May 2020
Published online: 24 Nov 2020 *