Papers › Classifying the Ideological Orientation of User-Submitted Texts in Social Media

Classifying the Ideological Orientation of User-Submitted Texts in Social Media

12 Dec 2022IEEE International Conference on Machine Learning and Applications (ICMLA) 2022 12archive 2025-07-28

Kamalakkannan Ravi, Adan Ernesto Vela, Rickard Ewetz

With the long-term goal of understanding how language is used and evolves within online communities, this work explores the application of natural language processing techniques to classify text articles according to their ideological orientation (i.e., conservative or liberal). We first collect a balanced corpus of text articles posted to the online communities r/Liberal and r/Conservative from the social media website Reddit. Using the corpus, we develop and apply three classifiers. The baseline classifier is a Bayes model that accounts for each text article’s web domain, as such, classification is independent of content. Next, we develop a support vector machine (SVM) model with term frequency-inverse document frequency (TF-IDF) features; this approach highlight differences in language using a count-based feature-space to differentiate text articles. Last, we evaluate the context-based transformer (RoBERTa) model and discuss its under-performance relative to the baseline and SVM models.

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Code

ADCLab/RedditIdeologyDB official report

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Tasks

Articles Classification News Classification

Datasets

Introduced by this paper, per the archive.

Reddit Ideology Database

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Classification	Reddit Ideology Database	SVM	F1-score (Weighted)	86.19	#1 of 2	Archive leaderboard	report
Classification	Reddit Ideology Database	RoBERTa	F1-score (Weighted)	78.13	#2 of 2	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection RoBERTa SVM Softmax Weight Decay WordPiece

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