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 paper advances the online-offline debate by utilizing Natural Language Processing (NLP) techniques to compare identity-related statements across online and offline contexts. Participants (N = 120) completed twenty “I am…” statements in two conditions: one reflecting online identity and one reflecting offline identity. Distinct linguistic patterns in textual data were identified using TF-IDF and LIWC, with statistical differences determined using Fisher test and t- tests, respectively. The results revealed that online identity statements emphasized themes of (anonymous) connectivity and knowledge, whereas offline identity statements focused on personal relationships and physical and emotional states. 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.