The doctoral research empirically investigates language change and polarization tendencies in online political discourse using deep learning-based language models.

Deep learning-based language models are artificial neural networks with many layers and parameters that are capable of generating meaningful texts by learning syntactic and semantic features of the natural language. According to the internal, abstract representation of the input texts, these language models are able to generate text that best matches the input.

The textual traces of information diffusion patterns can be used to reconstruct the discursive structure of the online political space, identifying network centers and peripheries and ‘contagion patterns’ of information diffusion. By empirically scanning online political communication for language change and language polarization, and describing their characteristics and dynamics in detail, we can gain deeper insights into the ways in which public discourse operates and thus their impact on society.

The goal of the research is to make the models trained on the Hungarian data available together with an easy-to-use graphical user interface through which new tasks (e.g., classification and abstraction of texts) can be defined and answers gained without deep technical knowledge. The methodology developed in this research can help to pave the way for a wider sociological application of language models.

Understanding social theory is essential for navigating the complexities of our rapidly evolving world, particularly in the context of modernization and the emergence of late- and postmodern societies. This research delves into the intricacies of these theories and their relevance in today's digitalized and mediatized society. By applying these theoretical frameworks, we gain valuable insights into the dynamics of social change, cultural shifts, and the impact of technology on human interaction, ultimately contributing to a deeper understanding of contemporary society.

The doctoral research empirically investigates language change and polarization tendencies in online political discourse using deep learning-based language models.

Deep learning-based language models are artificial neural networks with many layers and parameters that are capable of generating meaningful texts by learning syntactic and semantic features of the natural language. According to the internal, abstract representation of the input texts, these language models are able to generate text that best matches the input.

The textual traces of information diffusion patterns can be used to reconstruct the discursive structure of the online political space, identifying network centers and peripheries and ‘contagion patterns’ of information diffusion. By empirically scanning online political communication for language change and language polarization, and describing their characteristics and dynamics in detail, we can gain deeper insights into the ways in which public discourse operates and thus their impact on society.

The goal of the research is to make the models trained on the Hungarian data available together with an easy-to-use graphical user interface through which new tasks (e.g., classification and abstraction of texts) can be defined and answers gained without deep technical knowledge. The methodology developed in this research can help to pave the way for a wider sociological application of language models.