# Modeling Political Orientation of Social Media Posts: An Extended Analysis

arXiv: https://arxiv.org/abs/2311.12323 · HTML: https://mlsanity.com/paper/2311.12323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding](https://mlsanity.com/paper/2006.01321/) (2020)
- [Multi-view Models for Political Ideology Detection of News Articles](https://mlsanity.com/paper/1809.03485/) (2018)
- [Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media](https://mlsanity.com/paper/1904.00542/) (2019)
- [Text Similarity in Vector Space Models: A Comparative Study](https://mlsanity.com/paper/1810.00664/) (2018)
- [Examining the consumption of radical content on YouTube](https://mlsanity.com/paper/2011.12843/) (2020)
- [Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks](https://mlsanity.com/paper/2207.08349/) (2022)
- [Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine](https://mlsanity.com/paper/2212.00419/) (2022)
- [Political Polarization in Online News Consumption](https://mlsanity.com/paper/2104.06481/) (2021)
- [Understanding Political Polarization via Jointly Modeling Users, Connections and Multimodal Contents on Heterogeneous Graphs](https://mlsanity.com/paper/2201.05946/) (2022)
- [Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning](https://mlsanity.com/paper/2202.12382/) (2022)
- [KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction](https://mlsanity.com/paper/2302.12126/) (2023)
- [Detecting Political Biases of Named Entities and Hashtags on Twitter](https://mlsanity.com/paper/2209.08110/) (2022)
- [Examining Untempered Social Media: Analyzing Cascades of Polarized Conversations](https://mlsanity.com/paper/1906.04261/) (2019)
- [Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction](https://mlsanity.com/paper/2209.10292/) (2022)

## Abstract (arXiv)

Developing machine learning models to characterize political polarization on online social media presents significant challenges. These challenges mainly stem from various factors such as the lack of annotated data, presence of noise in social media datasets, and the sheer volume of data. The common research practice typically examines the biased structure of online user communities for a given topic or qualitatively measuring the impacts of polarized topics on social media. However, there is limited work focusing on analyzing polarization at the ground-level, specifically in the social media posts themselves. Such existing analysis heavily relies on annotated data, which often requires laborious human labeling, offers labels only to specific problems, and lacks the ability to determine the near-future bias state of a social media conversations. Understanding the degree of political orientation conveyed in social media posts is crucial for quantifying the bias of online user communities and investigating the spread of polarized content. In this work, we first introduce two heuristic methods that leverage on news media bias and post content to label social media posts. Next, we compare the efficacy and quality of heuristically labeled dataset with a randomly sampled human-annotated dataset. Additionally, we demonstrate that current machine learning models can exhibit improved performance in predicting political orientation of social media posts, employing both traditional supervised learning and few-shot learning setups. We conduct experiments using the proposed heuristic methods and machine learning approaches to predict the political orientation of posts collected from two social media forums with diverse political ideologies: Gab and Twitter.