Uncovering Hidden Factions through Text-Network Representations: Unsupervised Public Opinion Mapping of Iran on Twitter in the 2022 Unrest

Sahar Omidi Shayegan, Jean-François Godbout, Reihaneh Rabbany

Keywords: Computational Social Science, Network Science, Social Networks, Natural Language Processing

Submission Type: Archival

Abstract: Ideological mapping on social media is typically framed as a supervised classification task that depends on stable party systems and abundant annotated data. These assumptions fail in contexts with weak political institutionalization, such as Iran. We recast ideology detection as a fully unsupervised mapping problem and introduce a text-network representation system, uncovering latent ideological factions on Persian Twitter during the 2022 Mahsa Amini protests. Using hundreds of millions of Persian tweets, we learn joint text–network embeddings by fine-tuning ParsBERT with a combined masked-language-modeling and contrastive objective and by passing the embeddings through a Graph Attention Network trained for link prediction on time-batched subgraphs. The pipeline integrates semantic and structural signals without observing labels. Density-based clustering reveals eight ideological blocs whose spatial relations mirror known political alliances. Alignment with 883 expert-labeled accounts yields 53% accuracy. This label-free framework scales to label-scarce contexts, offering new leverage for studying political debates online.

Submission Number: 22