COVID-19 news and audience aggressiveness: Analysis of news content and audience reaction during the state of emergency in Latvia (2020–2021)

Anda Rozukalne, Vineta Kleinberga, Normunds Grūzītis


Last modified: 27.04.2021

Abstract

This research focuses on the interrelation between news content on COVID-19 of three largest online news sites in Latvia (delfi.lv, apollo.lv, tvnet.lv) and the audience reaction to the news in the Latvian and Russian channels during the state of emergency. By using a tool for audience behaviour analysis, the Index of the Internet Aggressiveness (IIA), for analysis of audience comments, the study aims to uncover how and whether news about COVID-19 affect the level of audience aggressiveness. The study employs two data collection methods: news content analysis and IIA data analysis, in which ten index peaks are selected in each of the two emergency periods (spring 2020, fall and winter 2020/21). The study data consists of content analysis of 400 news items and analysis of ~80,000 comments, identifying the level of aggressiveness, the number and structure of comment keywords. The results show that the level of public aggressiveness is only partially formed by the attitude towards COVID-19 news: less than half of the most aggressively commented news is devoted to information about COVID-19. An increase in the level of aggressiveness of the audience of online news sites can be observed at the end of 2020 and at the beginning of 2021 when it is higher than over the course of 2020.

IIA is an online comment analysis platform, which analyses user-generated comments on news on online news sites according to pre-selected keywords, allowing to grasp the dynamics of commenters’ verbal aggressiveness. In addition, IIA exploits a machine learned classifier to recognize not only potentially aggressive keywords but also to analyse the entire comments. In January 2021, the IIA data set consists of ~24.89 million comments (~611.97 million words) added to ~1.34 million news articles.

 


Keywords


big data; COVID – 19; internet users’ comments; online news; verbal aggressiveness

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