This project applies machine learning to study a) discursive practices of politicians and journalists on Twitter, and b) to what extent institutional differences between agents still matter, or even exist, now they have similar publishing opportunities on social media. While automated analysis of the content of tweets is intensively studied, the project’s focus on behaviour is innovative. It aims to develop a tool for large-scale automated content analysis of latent categories of behavior that should be scalable in terms of big data sets and various social media platforms.
The project builds upon previous work by the research team in which manual content analysis was applied to study discursive practices of politicians and journalists. A detailed coding scheme was designed to code latent categories of online behaviour (or: discursive practices) such as broadcasting, promoting, criticizing, branding, requesting input etc. These annotated data sets will be used to train the computer.
Our work suggests that although journalists and politicians have different roles and goals, their behaviour on social media is surprisingly similar. This hypothesized redistribution of power in the so-called “triangle of political communication” calls for a revision of classic theoretical insights that are key to both political communication and journalism studies.
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