The Problem
Doomscrolling, the compulsive consumption of distressing online content, is consistently linked to anxiety, depression, sleep disruption, and lost productivity. Existing research treats it as a single, undifferentiated behavior measured by frequency or duration of use, which obscures meaningful variation in how and why people engage with it.
Some users actively chase stimulation and novelty; others fall into passive scrolling as a response to burnout and emotional depletion. Without distinguishing between these patterns, interventions and platform design changes risk being mismatched to the users they are meant to help.
Key Findings
Doomscrolling is not a single behavior. The data revealed two psychologically and behaviorally distinct subtypes that differ systematically in their triggers, emotional consequences, and the strategies users adopt to manage them.
Active Doomscrollers
Primary triggers
- Dopamine seeking and addiction like patterns (105)
- Fear of missing out / FOMO (59)
- Boredom as a launching point for stimulation (36)
- Social motivations and staying current (30)
Internal states
- Anxiety while scrolling (34)
- Lowered self esteem and self loathing (33)
- Envy and social comparison (26)
- Loss of control and powerlessness (19)
Self regulation
- Asking others for advice (70)
- Deleting or deactivating apps (60)
- Cold turkey attempts (18, often abandoned)
Passive Doomscrollers
Primary triggers
- Overwhelm, burnout, and coping (28)
- Low motivation and low effort default (27)
- Boredom co occurring with avoidance (30)
- ADHD and executive function difficulty (14)
Internal states
- Blocking of hobbies and meaningful activity (15)
- Loss of motivation (14)
- Depression and emotional numbness (13)
- Diffuse impact rather than acute distress
Self regulation
- Asking others for advice (23)
- Narrower range of strategies overall
- Lower active engagement with change
"I doomscroll TikTok and reels like I need it to survive. It's mainly short videos for that quick dopamine boost."
— Active doomscroller
"Every hobby I've burned out of. I can barely sit down to read a book even when I want to. I just doomscroll all day. Even when I do not want to."
— Passive doomscroller
Quitting outcomes
Of the 183 users who explicitly discussed trying to quit doomscrolling, only 25 reported sustained success, a 14% success rate. The data revealed five distinct outcome groups.
Users who quit but later regressed matched successful quitters on Technology Abstinence (85% vs. 88%) and Hobby Replacement (60% vs. 60%). The strategies were present but did not hold. Strategy adoption alone does not determine sustained success.
Research Questions
These three questions structured the coding framework and guided interpretation of the data.
- RQ1 What triggers do active and passive doomscrollers describe as leading to doomscrolling?
- RQ2 How do active and passive doomscrollers describe the effects of doomscrolling on their mental and physical states?
- RQ3 What actions do active and passive doomscrollers describe taking after these experiences?
Methods
I analyzed 243 posts drawn from 48 Reddit communities covering doomscrolling, digital minimalism, screen time, social media addiction, attention regulation, and self improvement (including r/doomscrollHQ, r/nosurf, r/digitalminimalism, r/getdisciplined, r/ADHD, and r/productivity, among others). Posts spanned January 2020 through July 2025. The initial corpus of over 3,000 posts was narrowed through a multi stage filtering process: keyword filtering, an LLM assisted relevancy pass, and a manually annotated 100 post calibration sample used to set inclusion criteria for a second LLM pass.
All 243 posts were inductively coded across the three dimensions of the Stimulus Organism Response (SOR) model: triggers, internal states, and self regulation responses. Each post was additionally classified as active or passive doomscrolling.
The SOR model was chosen because it cleanly separates the external triggers of a behavior from the internal psychological states it produces and the responses users take afterward, a structure well suited to a phenomenon where the same trigger can lead to very different behavioral patterns depending on the user's underlying state.
Implications for Design
The distinction between active and passive doomscrollers has a direct consequence for product design. A single intervention will fail one of the two groups. Strategies that disrupt active doomscrolling do not address the depletion that drives passive use. Strategies that support passive users do little to interrupt the reward seeking that pulls active users back in.
→ 01 Friction based interventions for active users
Active users are pulled into scrolling by reward seeking, FOMO, and algorithmically optimized stimulation. Effective interventions for this group should introduce deliberate friction: scroll limits with active reconfirmation, visible usage dashboards, and feeds that break the variable reward loop.
→ 02 Restoration oriented design for passive users
Passive users are pushed into scrolling by burnout and motivational depletion. Friction alone will not help. What they need is reduced cognitive demand and redirection toward restorative alternatives: calming content surfacing, prompts that suggest offline activity, and reduced algorithmic intensity during long sessions.
→ 03 Reducing social comparison affordances
Envy and social comparison co occurred with FOMO among active doomscrollers, forming a self reinforcing loop. Design choices that mute this dynamic, including hidden like counts, curated social feeds, and reduced visibility into peer activity, may help break the cycle.
→ 04 Support gradual disengagement over abrupt cessation
Gradual reduction was the only strategy meaningfully associated with success across both groups. Product features should make incremental reduction visible and rewarding rather than asking users to delete apps and quit outright, an approach the data suggests fails for most.
→ 05 Platform accountability as a design constraint
The psychological vulnerabilities doomscrolling exploits, including negativity bias, reward sensitivity, and social comparison, are not incidental to platform design but in many cases deliberately targeted by it. Treating user wellbeing as a first class design constraint, rather than an afterthought measured only by retention metrics, is a baseline ethical responsibility.
Artifacts & Code
Selected figures and excerpts from the study are shown below. The full paper is available on request. The data collection and analysis pipeline, including Reddit scraping, keyword filtering, the custom annotation tool, and statistical analysis, is available on GitHub.
Quitting strategy rates by outcome group