Understanding the Interaction between Interests, Conversations and Friendships in Facebook

TL;DR

This paper introduces a novel latent space model that integrates text, network links, and categorical data to analyze user interests, conversations, and friendships on Facebook, revealing new insights into social dynamics.

Contribution

The paper presents a new latent space model that combines multiple data modalities to study complex social interactions on Facebook.

Findings

Revealed surprising patterns in user interests and friendships.

Confirmed the significance of social trends with statistical evidence.

Provided insights into the interplay between conversations and social structure.

Abstract

In this paper, we explore salient questions about user interests, conversations and friendships in the Facebook social network, using a novel latent space model that integrates several data types. A key challenge of studying Facebook's data is the wide range of data modalities such as text, network links, and categorical labels. Our latent space model seamlessly combines all three data modalities over millions of users, allowing us to study the interplay between user friendships, interests, and higher-order network-wide social trends on Facebook. The recovered insights not only answer our initial questions, but also reveal surprising facts about user interests in the context of Facebook's ecosystem. We also confirm that our results are significant with respect to evidential information from the study subjects.