Keyword – dynamic topic model

Katona, Eszter – Kmetty, Zoltán – Németh, Renáta:

Applying natural language processing to analyise the representation of corruption in the Hungarian online media

This paper presents a thematic analysis of the representation of corruption in the Hungarian online media, using a text mining tool called dynamic topic modeling. The text corpus was provided by K-Monitor and includes online articles on corruption and issues related to the misuse of public funds. Our study is exploratory in nature: it is aimed at identifying the main topics of the articles and the dynamics of thematic changes in the period 2007–2018, including the meaning, the background and the changes of each corruption topic. The causal links revealed by this research lie in whether the medium is of an oppositional or of a pro-government position, and how election campaign periods affect the thematic structure of the representation of corruption. Owing to the fact that the ownership of the news portal Origó changed during the analysed period, a natural experiment has also been possible in an attempt to reveal the impact of this change on the thematic structure of the corruption discourse on the portal in question.

Keywords: automated text analytics, corruption, dynamic topic model, NLP, text mining