Displayed Depression Symptoms on Facebook at Two Time Points: Content Analysis
Bibliographic record
Abstract
BACKGROUND: Depression is a prevalent and problematic mental disorder that often has an onset in adolescence. Previous studies have illustrated that depression disclosures on social media are common and may be linked to an individual's experiences of depression. However, most studies have examined depression displays on social media at a single time point. OBJECTIVE: This study aims to investigate displayed depression symptoms on Facebook at 2 developmental time points based on symptom type and gender. METHODS: Participants were recruited from an ongoing longitudinal cohort study. The content analysis of text-based Facebook data over 1 year was conducted at 2 time points: time 1 (adolescence; age 17-18 years) and time 2 (young adulthood; ages 20-22 years). Diagnostic criteria for depression were applied to each post to identify the displayed depression symptoms. Data were extracted verbatim. The analysis included nonparametric tests for comparisons. RESULTS: A total of 78 participants' Facebook profiles were examined, of which 40 (51%) were male. At time 1, 62% (48/78) of the adolescents had a Facebook profile, and 54% (26/78) displayed depression symptom references with an average of 9.4 (SD 3.1) references and 3.3 (SD 2.3) symptom types. Of the 78 participants, 15 (19%) females and 12 (15%) males displayed depression symptom references; these prevalence estimates were not significantly different by gender (P=.59). At time 2, 35 young adults displayed symptoms of depression with an average of 4.6 (SD 2.3) references and 2.4 (SD 1.3) symptom types. There were no differences in the prevalence of symptoms of depression displayed between males (n=19) and females (n=16; P=.63). CONCLUSIONS: This content analysis study within an ongoing cohort study illustrates the differences in depression displays on Facebook by developmental stage and symptom. This study contributes to a growing body of literature by showing that using social media to observe and understand depression during the emerging adult developmental period may be a valuable approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".