Fake News Detection through Ego Network Analysis

Medium blog post walking through my entire analysis: https://medium.com/@briansrebrenik/ego-network-analysis-for-the-detection-of-fake-news-da6b2dfc7c7e

Data Sources:

• "Fake News" data from Credbank

• Twitter Network Edges from Luca Hammer

Tools Used:

• Neo4j Graph Database

• Gephi for graph visualizations

• Node2Vec from Stanford Network Analysis Project

• Node2Vec Algorithm Implementation

• NetworkX

• Keras

• Paperspace Gradient

Eigenvector centrality is a measure of the influence of a node in a network. Relative scores are assigned to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes. A high eigenvector score means that a node is connected to many nodes who themselves have high scores.

PageRank is widely recognized as a way of detecting influential nodes in a graph. It is different to other centrality algorithms because the influence of a node depends on the influence of its neighbours.

The Louvain method of community detection is an algorithm for detecting communities in networks. It maximizes a modularity score for each community, where the modularity quantifies the quality of an assignment of nodes to communities by evaluating how much more densely connected the nodes within a community are compared to how connected they would be in a random network.

The node2vec framework learns low-dimensional representations for nodes in a graph by optimizing a neighborhood preserving objective. The objective is flexible, and the algorithm accomodates for various definitions of network neighborhoods by simulating biased random walks. Specifically, it provides a way of balancing the exploration-exploitation tradeoff that in turn leads to representations obeying a spectrum of equivalences from homophily to structural equivalence.

Using profile descriptions for classification in a recurrent neural network. The Embedding Layer inside the network computes word embedding vectors. Word Embeddings are a type of vectorization strategy that computes word vectors from a text corpus by training a neural network, which results in a high-dimensional embedding space, where each word is in the corpus is a unique vector in that space. In this embedding space, the position of the vector relative to the other vectors captures semantic meaning.

Combining features from Node2Vec and probabilities from word embeddings in an XGBoost and SVM Classifier.