Grok in the Wild: Characterizing the Roles and Uses of Large Language Models on Social Media

TL;DR

This study analyzes how the large language model Grok is used on social media platform X, revealing diverse roles, user engagement patterns, and the social dynamics of AI interactions in public online spaces.

Contribution

It provides the first systematic characterization of LLM use in a public social media environment, identifying 10 interaction roles and analyzing user and engagement patterns.

Findings

Grok responds to 62% of requests with low engagement levels.

Identified 10 roles including information provider, dispute mediator, and advocate.

User interests correlate with the roles Grok assumes in interactions.

Abstract

xAI's large language model, Grok, is called by millions of people each week on the social media platform X. Prior work characterizing how large language models are used has focused on private, one-on-one interactions. Grok's deployment on X represents a major departure from this setting, with interactions occurring in a public social space. In this paper, we systematically sample three months of interaction data to investigate how, when, and to what effect Grok is used on X. At the platform level, we find that Grok responds to 62% of requests, that the majority (51%) are in English, and that engagement is low, with half of Grok's responses receiving 20 or fewer views after 48 hours. We also inductively build a taxonomy of 10 roles that LLMs play in mediating social interactions and use these roles to analyze 41,735 interactions with Grok on X. We find that Grok most often serves as an information provider but, in contrast to LLM use in private one-on-one settings, also takes on roles related to dispute management, such as truth arbiter, advocate, and adversary. Finally, we characterize the population of X users who prompted Grok and find that their self-expressed interests are closely related to the roles the model assumes in the corresponding interactions. Our findings provide an initial quantitative description of human-AI interactions on X, and a broader understanding of the diverse roles that large language models might play in our online social spaces.