# Text and author-level political inference using heterogeneous knowledge representations

arXiv: https://arxiv.org/abs/2206.12293 · HTML: https://mlsanity.com/paper/2206.12293/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Semi-supervised sequence tagging with bidirectional language models](https://mlsanity.com/paper/1705.00108/) (2017)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Multi-view Models for Political Ideology Detection of News Articles](https://mlsanity.com/paper/1809.03485/) (2018)
- [Topic-Specific Sentiment Analysis Can Help Identify Political Ideology](https://mlsanity.com/paper/1810.12897/) (2018)
- [A Bias Aware News Recommendation System](https://mlsanity.com/paper/1803.03428/) (2018)
- [A Lightweight Regression Method to Infer Psycholinguistic Properties for Brazilian Portuguese](https://mlsanity.com/paper/1705.07008/) (2017)
- [Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector](https://mlsanity.com/paper/1905.01962/) (2019)

## Abstract (arXiv)

The inference of politically-charged information from text data is a popular research topic in Natural Language Processing (NLP) at both text- and author-level. In recent years, studies of this kind have been implemented with the aid of representations from transformers such as BERT. Despite considerable success, however, we may ask whether results may be improved even further by combining transformed-based models with additional knowledge representations. To shed light on this issue, the present work describes a series of experiments to compare alternative model configurations for political inference from text in both English and Portuguese languages. Results suggest that certain text representations - in particular, the combined use of BERT pre-trained language models with a syntactic dependency model - may outperform the alternatives across multiple experimental settings, making a potentially strong case for further research in the use of heterogeneous text representations in these and possibly other NLP tasks.