arXiv Open Access 2021

Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity

Valentin Hofmann Xiaowen Dong Janet B. Pierrehumbert Hinrich Schütze

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Abstrak

The increasing polarization of online political discourse calls for computational tools that automatically detect and monitor ideological divides in social media. We introduce a minimally supervised method that leverages the network structure of online discussion forums, specifically Reddit, to detect polarized concepts. We model polarization along the dimensions of salience and framing, drawing upon insights from moral psychology. Our architecture combines graph neural networks with structured sparsity learning and results in representations for concepts and subreddits that capture temporal ideological dynamics such as right-wing and left-wing radicalization.

Topik & Kata Kunci

cs.CL cs.AI cs.SI

Penulis (4)

Valentin Hofmann

Xiaowen Dong

Janet B. Pierrehumbert

Hinrich Schütze

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APA MLA BibTeX

Hofmann, V., Dong, X., Pierrehumbert, J.B., Schütze, H. (2021). Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity. https://arxiv.org/abs/2104.08829

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Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓