More than Meets the Tie: Examining the Role of Interpersonal Relationships in Social Networks

TL;DR

This study analyzes how different interpersonal relationships like friendship, kinship, or romance influence communication patterns on Twitter, revealing their impact on language, topics, and information spread, with implications for understanding social dynamics.

Contribution

It introduces a large-scale analysis of relationship types in social networks and demonstrates their predictive power for communication behaviors and information diffusion.

Findings

Relationship types significantly affect language and topic diversity.

A predictive model achieves a macro F1 score of 0.70 for relationship classification.

Including relationship features improves retweet prediction performance.

Abstract

Topics in conversations depend in part on the type of interpersonal relationship between speakers, such as friendship, kinship, or romance. Identifying these relationships can provide a rich description of how individuals communicate and reveal how relationships influence the way people share information. Using a dataset of more than 9.6M dyads of Twitter users, we show how relationship types influence language use, topic diversity, communication frequencies, and diurnal patterns of conversations. These differences can be used to predict the relationship between two users, with the best predictive model achieving a macro F1 score of 0.70. We also demonstrate how relationship types influence communication dynamics through the task of predicting future retweets. Adding relationships as a feature to a strong baseline model increases the F1 and recall by 1% and 2%. The results of this study suggest relationship types have the potential to provide new insights into how communication and information diffusion occur in social networks.