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An analysis of more than 183,000 American adults surveyed over an 11-year span indicates that higher daily social media use is consistently linked to an elevated likelihood of reporting depression. Using machine learning models to evaluate demographic and media habits from 2014 through 2024, researchers found that social media use ranked among the strongest predictors of self-reported depression, with its contribution to predicted risk rising once use exceeded about two and a half hours per day. The findings, published in npj Mental Health Research, suggest that the connection between heavy social media use and self-reported depression is not limited to teenagers and has remained stable across a decade of shifting digital trends.
Social media encompasses online platforms built for interpersonal communication, content sharing, and public commentary on community life, services, or products. Over the past decade, the rapid adoption of platforms such as Instagram, Snapchat, and TikTok has reshaped how people socialize, express themselves, and compare their lives to the lives of others. Alongside this expansion, public health experts have raised questions about whether heavy engagement with these apps contributes to depression, a mood condition characterized by persistent sadness, low energy, and a reduced interest in everyday activities.
Some early studies suggested that heavy use of social platforms might take an emotional toll. In a 2016 experiment involving 1,095 adults in Denmark, participants who took a one-week break from Facebook reported higher life satisfaction and more positive emotions than those who kept using the site, particularly if they were heavy users or prone to envying others online. Similarly, a study covered by PsyPost in 2018 found that teenagers who spent more free time on screen-based activities reported lower happiness than peers who spent more time on in-person activities, with the happiest teens using digital media for less than an hour a day.
Other studies complicated this picture by questioning whether social media actually triggers mental distress or whether people who are already struggling simply turn to their screens more often. For instance, a 2018 study of university students tracked 125 undergraduates seven times a day for two weeks and found that feeling lonely or tired predicted more passive scrolling later on, whereas scrolling did not predict subsequent increases in depression symptoms or stress.
Meanwhile, some researchers analyzing large time-use diaries argued that overall screen time had little negative impact on youth well-being, while critics countered that combining television, gaming, and social media into a single screen-time measure can hide risks specific to interactive social platforms, particularly among women and girls.
Most of the debate surrounding digital media and mental health has centered on teenagers and young adults or relied on data from a single point in time. That narrow focus has left open whether the association between social media time and depression extends across the broader adult lifespan and whether it has held steady as smartphone habits evolved. Martin Block of Northwestern University, Vikram Suresh and Hans Breiter of the University of Cincinnati, and their colleagues examined 11 years of national survey data from American adults aged 18 to 70, building on Block’s earlier analysis of the 2014 survey data, which linked media use to depression.
The researchers analyzed data from the Media Behavior and Influence Study, an annual survey conducted each January and February from 2014 through 2024 by the market research firm Prosper Analytics. Participants were recruited through corporate customer databases, social media, and direct mail, with each yearly sample balanced to match U.S. Census benchmarks for age and gender. After screening out incomplete responses and people who finished the questionnaire in under two minutes, the analytic sample included 183,300 adults across the 11 annual waves, with between 15,410 and 17,869 participants surveyed each year.
To measure self-reported depression, the survey presented participants with a list of 31 health conditions and asked them to indicate with a yes-or-no answer which conditions they experienced. Participants also estimated the likelihood, from 0 to 100 percent, that they engaged in different media activities during each hour of a typical weekday and weekend day. From these hourly ratings, the authors calculated each person’s average daily minutes spent on social media, general internet browsing, television viewing, video gaming, email, and news consumption.
The team first used logistic regression, a statistical technique that estimates how strongly each type of media use relates to the odds of reporting depression while holding demographic factors constant. They then trained machine learning models known as balanced random forests for each survey year, combining 15 demographic and media-use variables: personal income, age range, gender, ethnicity, marital status, education level, occupation, household size, number of children under 18, and daily minutes spent on social media, internet use, television, gaming, email, and news.
A balanced random forest builds many decision trees using equal-sized samples of depressed and non-depressed individuals, which keeps the larger non-depressed group from overwhelming the computer’s predictions. To understand how the models reached their classifications, the researchers calculated Shapley Additive Explanations, a method grounded in economic game theory that measures how much each individual variable pushes a person’s predicted probability of depression up or down.
Across the 11-year study period, both daily social media time and the share of adults reporting depression climbed steadily. The proportion of respondents reporting depression rose from 15.1 percent in 2014 to 22.0 percent in 2024, following an upward trajectory both before and after the onset of the COVID-19 pandemic. After adjusting for demographic characteristics, every additional minute of daily social media use was associated with a 0.26 percent increase in the odds of reporting depression, which translates to roughly 17 percent higher odds (not the same as a 17 percent higher chance) for each additional hour spent on social media per day.
Comparable positive links emerged across all 11 years for daily time spent playing video games, browsing the internet, watching television, and using email, with gaming and internet use showing per-minute associations as strong as or slightly stronger than social media, whereas news consumption showed a weak relationship in only two of the 11 survey waves.
In the machine learning analyses, the balanced random forest models showed moderate ability to distinguish adults with and without self-reported depression. On a standard measure of this ability, where 0.5 reflects chance and 1.0 reflects perfect sorting, scores ranged from 0.66 to 0.74 across the 11 annual datasets, and the models correctly flagged roughly 40 to 53 percent of adults who reported depression. To test whether patterns learned in earlier years remained relevant later on, the researchers trained a model exclusively on surveys from 2014 through 2018 and used it to predict depression in the 2019 through 2024 datasets. The model performed similarly in those later years, with scores of 0.68 to 0.72, indicating that the relationship between media habits, demographics, and depression remained stable over time.
Additional sensitivity tests showed that removing the 2020 and 2021 survey waves or accounting for the COVID-19 pandemic as a separate factor did not alter the link between social media use and depression. When the authors ranked the 15 predictors across three different importance measures over the entire decade, personal income had the strongest overall influence on predicted depression, followed by social media use, general internet use, and age range.
Social media use grew more prominent as a predictor over the course of the decade, rising from ninth place in the game-theory rankings in 2014 to consistently place among the top four predictors from 2019 through 2024. While social media use is a daily habit that people can change more easily than demographic traits like income or age, the authors caution that high importance rankings can reflect how much a behavior varies across a population rather than showing that changing the behavior will reduce depression.
Examining how different amounts of daily social media time affected the machine learning predictions pointed to a consistent pattern across multiple survey years. Below roughly 150 minutes per day, time spent on social media had a nearly neutral effect on whether the model classified a participant as depressed, but beyond two and a half hours per day, additional social media time increasingly pushed predicted depression risk upward. The authors describe this 150-minute mark as a hypothesis for future studies to test rather than an established safe limit.
Subgroup analyses also highlighted notable demographic patterns. Among young adults aged 18 to 25, self-reported depression prevalence ranged between 24 percent and 40 percent for women across the decade compared to between 15 percent and 25 percent for men, while the machine learning models performed better among adults aged 45 and older than among the youngest age group.
The link between heavier social media engagement and depression aligns with research covered by PsyPost in 2019, which tracked symptom severity longitudinally in adolescents rather than self-reported depression across the adult lifespan and similarly found that higher social media and television use predicted more severe depression symptoms over four years, although video gaming was not linked to depression in that teen sample. The results also echo a meta-analysis covered by PsyPost earlier in 2026, which examined longitudinal data in children and teens rather than repeated surveys of adults and found that higher social media use was consistently linked to increased depression and anxiety.
At the same time, the new findings stand in tension with a longitudinal study covered by PsyPost in 2020, which tracked individual adolescents over an eight-year period into young adulthood rather than analyzing population-level data in adults aged 18 to 70 and found that increases in daily social media time were not associated with increases in depression or anxiety. They also contrast with a study covered by PsyPost in 2025, which used objective smartphone screen-time tracking in a small sample of mostly young adults rather than self-reported usage across a broad adult population and found no meaningful association between social media use and depression.
There are a few things to keep in mind when interpreting these findings. Because the study relied on repeated cross-sectional surveys that sampled a new group of adults each year rather than following the same individuals over time, the analysis cannot establish whether heavy social media use causes depression. It is possible that adults who are already experiencing depressive symptoms spend more time online seeking distraction, social connection, or relief from low mood when in-person interactions feel difficult. The relationship could also work in both directions, with heavy social media exposure contributing to distress through social comparison or disrupted sleep while depressive feelings simultaneously encourage more time online.
Depression was assessed through a single self-reported yes-or-no question rather than a clinical interview or a multi-item diagnostic scale. That approach may overlook milder symptoms or include inaccurate self-diagnoses. Daily media time was also estimated from self-reported hourly probabilities collected exclusively in January and February. The sample also came from a commercial survey panel recruited through company customer lists, social media, and direct mail rather than a random national sample, and it was balanced only on age and gender, so it may not fully represent American adults.
In addition, the survey did not separate passive scrolling from active interactions such as messaging friends or sharing original posts, leaving open which specific online habits account for the link with depression. The authors suggest that future longitudinal and experimental studies should track individuals over time, incorporate psychological measures of how people weigh rewards and negative experiences, and test whether keeping daily social media use below the 150-minute mark offers a meaningful benefit for mental health. Recruiting partly through social media could also tilt the sample toward heavier users.
The study, “Social media use and depression across 11 years of U.S. adult data,” was authored by Martin Block, Vikram Suresh, Johnathan Avant, Sumra Bari, Nicole Vike, Felix Zhan, and Hans Breiter.