Parth Joshi

Publication Date

Spring 2025

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Katerina Potika

Second Advisor

William Andreopoulos

Third Advisor

Maharshi Raval

Keywords

Graph Neural Networks (GNNs), Natural Language Processing (NLP), CLIP, Fake News Detection, Multimodal Learning, Neo4j, Reddit

Abstract

Social media platforms such as Reddit are widely used for sharing and consuming information. User-generated content poses a great risk for misinformation creation and dissemination on these platforms. “Fake news”, as it is commonly referred to, has far-reaching social implications, swaying public perception, making political viewpoints more radical, and adversely impacting health decisions. The covariable features that come with fake news make it even harder to detect because it is presented in the form of text, images, videos, and even social interactions. This paper describes a novel method for detecting fake news on Reddit: RIFT, short for Reddit Information Falsity Tagger. The proposed system models Reddit using a Neo4j graph database and employs a graph approach for fake news detection. Users, posts, and subreddits are mapped to nodes with edges like POSTED_BY and BELONGS_TO defining relationships. Each post node is augmented with multimodal features like text embeddings (BERT), image embeddings (CLIP), and upvote/comment engagement levels (upvotes, comments). RIFT is tested on two datasets: the original Fakeddit benchmark and FakedditEnhanced, an extended version created for this project. The enhanced dataset is enriched with more metadata and image embeddings obtained through Reddit’s API which allows for better multimodal detection. The results achieved with Graph Neural Networks (GCN, GraphSAGE) demonstrate the power of applying both content and structure in identifying misinformation.

Download

Available for download on Monday, May 25, 2026

Share

COinS