TF-IDF code and synthetic data for the paper "Who do you think you are? An experimental comparison of online and offline identities using NLP techniques"

Researchers have been debating whether our online identities are different from our offline identities since technology enabled us to operate in the digital space. This research advances the online-offline debate by utilizing Natural Language Processing (NLP) techniques to compare identity-related statements across offline and online contexts. Participants (N = 120) completed twenty “I am…” statements in two conditions: one reflecting offline identity and one reflecting online identity. Distinct linguistic patterns in textual data were identified using TF-IDF, odd ratios, and Fisher test. The results revealed that online identity statements emphasized themes of (anonymous) connectivity, whereas offline identity statements focused on physical and emotional states and personal relationships. These findings suggest that, despite blurring boundaries between online and offline worlds, individuals continue to present their identities in contextually distinct ways. This research provides insights into the conceptualization of identity and highlights the need to design digital spaces that better balance connectivity with opportunities for deeper emotional and relational engagement.