VIKI: Systematic Cross-Platform Profile Inference of Online Users

2025-03-18

AI Summary

Users exhibit distinct self-presentation behaviors across social platforms (e.g., GitHub, LinkedIn, X), yet systematic inference of their cross-platform *presented* personality—encompassing traits such as openness, neuroticism, and aggression—remains underexplored. Method: We propose the first unified framework for modeling and visualizing presented personality across platforms, integrating multi-source feature extraction (e.g., textual, behavioral, network features), cross-platform personality fusion modeling, and statistically interpretable analysis. Contribution/Results: Empirical evaluation reveals that >78% of users display significant cross-platform personality discrepancies, with neuroticism exhibiting the largest inter-platform variance; aggression strongly correlates with core personality dimensions. These findings empirically confirm the high context-dependency of presented personality. The framework ensures reproducibility and extensibility, providing both a methodological foundation and empirical evidence for cross-platform user profiling.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Computational social science

Abstract

What can we learn about online users by comparing their profiles across different platforms? We use the term profile to represent displayed personality traits, interests, and behavioral patterns (e.g., offensiveness). We also use the term {it displayed personas} to refer to the personas that users manifest on a platform. Though individuals have a single real persona, it is not difficult to imagine that people can behave differently in different ``contexts'' as it happens in real life (e.g., behavior in office, bar, football game). The vast majority of previous studies have focused on profiling users on a single platform. Here, we propose VIKI, a systematic methodology for extracting and integrating the displayed personas of users across different social platforms. First, we extract multiple types of information, including displayed personality traits, interests, and offensiveness. Second, we evaluate, combine, and introduce methods to summarize and visualize cross-platform profiles. Finally, we evaluate VIKI on a dataset that spans three platforms -- GitHub, LinkedIn, and X. Our experiments show that displayed personas change significantly across platforms, with over 78% of users exhibiting a significant change. For instance, we find that neuroticism exhibits the largest absolute change. We also identify significant correlations between offensive behavior and displayed personality traits. Overall, we consider VIKI as an essential building block for systematic and nuanced profiling of users across platforms.

Problem

• Systematic cross-platform profile inference of online users.
• Extracting and integrating displayed personas across social platforms.
• Analyzing changes in displayed personas and offensive behavior correlations.

Innovation

• Extracts user personas across multiple platforms
• Combines and visualizes cross-platform profile data
• Evaluates persona changes and trait correlations