Beyond Discrete Genres: Mapping News Items onto a Multidimensional Framework of Genre Cues

Abstract

In the contemporary media landscape, with the vast and diverse supply of news, it is increasingly challenging to study such an enormous amount of items without a standardized framework. Although attempts have been made to organize and compare news items on the basis of news values, news genres receive little attention, especially the genres in a news consumer’s perception. Yet, perceived news genres serve as an essential component in exploring how news has developed, as well as a precondition for understanding media effects. We approach this concept by conceptualizing and operationalizing a non-discrete framework for mapping news items in terms of genre cues. As a starting point, we propose a preliminary set of dimensions consisting of “factuality” and “formality”. To automatically analyze a large amount of news items, we deliver two computational models for predicting news sentences in terms of the said two dimensions. Such predictions could then be used for locating news items within our framework. This proposed approach that positions news items upon a multidimensional grid helps deepening our insight into the evolving nature of news genres.

Googling Politics? The Computational Identification of Political and News-related Searches from Web Browser Histories

“…To overcome drawbacks of dictionary and traditional SML, researchers have recently successfully applied sophisticated Large Language Models (LLMs), many of which are built on the Transformer architecture, such as BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al, 2019) and GPT-3 (Brown et al, 2020). LLM methods have shown to outperform other methods for classifying related concepts like news genres in news articles (Lin et al, 2023), sentiment in news headlines (van Atteveldt et al, 2021), and PNR content in web content (Makhortykh et al, 2022) and website titles (Wojcieszak et al, n.d.).…”