From Consumer Demand to User Engagement: Comparing the Popularity and Virality of Election Coverage on the Internet

Previous research has identified a strong consumer demand for sensationalized and conflict-oriented news coverage. With the rise of social network services as central spaces for encountering news, there is a need to move beyond the notion of consumer demand (measured by attention to news stories) to a broader conception of user engagement (encompassing attention as well as social interactions online). This article seeks to remedy this by analyzing which parts of election coverage tend to become popular and go viral. It develops a concept of user agendas that include popularity (news stories that receive most clicks on news Web sites) and virality (stories that users share most intensively on social network sites). The article then applies the concepts in a case study of online news coverage during the 2015 Danish parliamentary election. Through an analysis of frames, sentiments, and actors, it is shown that game-strategic and personalized coverage tend to attract large-scale attention on news Web sites, whereas issue-oriented coverage fares better on social network sites. This suggests that what users demand depend on where they encounter news. Users tend to engage with one kind of news in private settings and another in the public settings on the social Internet.

A tracking and summarization system for online Chinese news topics

Purpose – The purpose of this paper is to design and implement new tracking and summarization algorithms for Chinese news content. Based on the proposed methods and algorithms, the authors extract the important sentences that are contained in topic stories and list those sentences according to timestamp order to ensure ease of understanding and to visualize multiple news stories on a single screen. Design/methodology/approach – This paper encompasses an investigational approach that implements a new Dynamic Centroid Summarization algorithm in addition to a Term Frequency (TF)-Density algorithm to empirically compute three target parameters, i.e., recall, precision, and F-measure. Findings – The proposed TF-Density algorithm is implemented and compared with the well-known algorithms Term Frequency-Inverse Word Frequency (TF-IWF) and Term Frequency-Inverse Document Frequency (TF-IDF). Three test data sets are configured from Chinese news web sites for use during the investigation, and two important findings are obtained that help the authors provide more precision and efficiency when recognizing the important words in the text. First, the authors evaluate three topic tracking algorithms, i.e., TF-Density, TF-IDF, and TF-IWF, with the said target parameters and find that the recall, precision, and F-measure of the proposed TF-Density algorithm is better than those of the TF-IWF and TF-IDF algorithms. In the context of the second finding, the authors implement a blind test approach to obtain the results of topic summarizations and find that the proposed Dynamic Centroid Summarization process can more accurately select topic sentences than the LexRank process. Research limitations/implications – The results show that the tracking and summarization algorithms for news topics can provide more precise and convenient results for users tracking the news. The analysis and implications are limited to Chinese news content from Chinese news web sites such as Apple Library, UDN, and well-known portals like Yahoo and Google. Originality/value – The research provides an empirical analysis of Chinese news content through the proposed TF-Density and Dynamic Centroid Summarization algorithms. It focusses on improving the means of summarizing a set of news stories to appear for browsing on a single screen and carries implications for innovative word measurements in practice.