# Knowledge Graph Representation for Political Information Sources

arXiv: https://arxiv.org/abs/2404.03437 · HTML: https://mlsanity.com/paper/2404.03437/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 79 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Yara Parser: A Fast and Accurate Dependency Parser](https://mlsanity.com/paper/1503.06733/) (2015)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [Semi-supervised sequence tagging with bidirectional language models](https://mlsanity.com/paper/1705.00108/) (2017)
- [Deep Reasoning with Knowledge Graph for Social Relationship Understanding](https://mlsanity.com/paper/1807.00504/) (2018)
- [The closed loop between opinion formation and personalised recommendations](https://mlsanity.com/paper/1809.04644/) (2018)
- [Relational Learning Analysis of Social Politics using Knowledge Graph Embedding](https://mlsanity.com/paper/2006.01626/) (2020)
- [A Generalized and Adaptive Method for Community Detection](https://mlsanity.com/paper/1406.2518/) (2014)
- [Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis](https://mlsanity.com/paper/1805.02823/) (2018)
- [Latent Poisson models for networks with heterogeneous density](https://mlsanity.com/paper/2002.07803/) (2020)
- [Network structure and patterns of information diversity on Twitter](https://mlsanity.com/paper/1607.06795/) (2016)
- [Grounding force-directed network layouts with latent space models](https://mlsanity.com/paper/2110.11772/) (2021)

## Abstract (arXiv)

With the rise of computational social science, many scholars utilize data analysis and natural language processing tools to analyze social media, news articles, and other accessible data sources for examining political and social discourse. Particularly, the study of the emergence of echo-chambers due to the dissemination of specific information has become a topic of interest in mixed methods research areas. In this paper, we analyze data collected from two news portals, Breitbart News (BN) and New York Times (NYT) to prove the hypothesis that the formation of echo-chambers can be partially explained on the level of an individual information consumption rather than a collective topology of individuals' social networks. Our research findings are presented through knowledge graphs, utilizing a dataset spanning 11.5 years gathered from BN and NYT media portals. We demonstrate that the application of knowledge representation techniques to the aforementioned news streams highlights, contrary to common assumptions, shows relative "internal" neutrality of both sources and polarizing attitude towards a small fraction of entities. Additionally, we argue that such characteristics in information sources lead to fundamental disparities in audience worldviews, potentially acting as a catalyst for the formation of echo-chambers.