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

arXiv: https://arxiv.org/abs/2105.06038 · HTML: https://mlsanity.com/paper/2105.06038/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2021.

## Built on by (most cited first, 4 of 4)

- [The Call for Socially Aware Language Technologies](https://mlsanity.com/paper/2405.02411/) (2024)
- [Overcoming Language Disparity in Online Content Classification with Multimodal Learning](https://mlsanity.com/paper/2205.09744/) (2022)
- [Your spouse needs professional help: Determining the Contextual Appropriateness of Messages through Modeling Social Relationships](https://mlsanity.com/paper/2307.02763/) (2023)
- [Does Geo-co-location Matter? A Case Study of Public Health Conversations during COVID-19](https://mlsanity.com/paper/2405.17710/) (2024)

## Abstract (arXiv)

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.