Researchers at the University of Washington showed how people enter a dissociative state when they are scrolling through social media. The study forms a part of a project presented at the CHI 2022 conference in New Orleans.
Dissociative State During Social Media-use
The researchers characterized “normative dissociation” as “total cognitive absorption, characterized by diminished self-awareness and reduced sense of agency”. This is different from trauma-based dissociation, as it involves everyday dissociation associated with spacing out or focusing intently on a task.
For the study, the researchers deployed Chirp—a custom Twitter client—to 43 American participants for a month. Through Chirp, the users’ likes and tweets appeared on the real social media platform, but researchers could control people’s experience—such as by adding new features or quick pop-up surveys.
The researchers also designed intervention strategies that the participants could use to retain more control over their online experience. Such interventions included custom lists, reading history labels, time limit dialogs, and usage statistics—strategies that reduced normative dissociation.
The results revealed that a majority of the participants experienced a state of dissociation while using social media platforms. Many even contended that the changes in the app design and the intervention strategies allowed them to be mindful of their social media usage and online experience.
One of the lead researchers, Amanda Baughan, said: “We all naturally dissociate in many ways throughout our day—whether it’s daydreaming or scrolling through Instagram, we stop paying attention to what’s happening around us.”
In fact, the researchers saw normative dissociation as a positive and beneficial break, much better than social media addiction.
To Know More You May Refer To
Baughan, A., Zhang, M. R., Rao, R., Lukoff, K., Schaadhardt, A., Butler, L. D., & Hiniker, A. (2022). “I don’t even remember what I read”: How design influences dissociation on social media. CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3501899