COVID-19 and the decline of active social media engagement
Bibliographic record
Abstract
Purpose The COVID-19 pandemic triggered an increase in online traffic, with many assuming that this technology would facilitate coping through active social connections. This study aims to interrogate the nature of this traffic-engagement relationship by distinguishing between passive (e.g. browsing) and active (e.g. reacting, commenting and sharing) engagement, and examining behavioral shifts across platforms. Design/methodology/approach Three field studies assessed changes in social media engagement during the COVID-19 pandemic. These studies included social media engagement with the most followed accounts (Twitter), discussion board commenting (Reddit) and news content sharing (Facebook). Findings Even though people spent more time online during the pandemic, the current research finds people were actively engaging less. Users were reacting less to popular social media accounts, commenting less on discussion boards and even sharing less news content. Research limitations/implications While the current work provides a systematic observation of engagement during a global crisis, it does not claim causality based on its correlational nature. Future research should test potential mechanisms (e.g. anxiety, threat and privacy) to draw causal inference and identify possible interventions. Practical implications The pandemic shed light on a complex systemic issue: the misunderstanding and oversimplification of how online platforms facilitate social cohesion. It encourages thoughtful consideration of online social dynamics, emphasizing that not all engagement is equal and that the benefits of connection may not always be realized as expected. Originality/value This research provides a postmortem on the traffic-engagement relationship, highlighting that increased online presence does not necessarily translate to active social connection, which might help explain the rise in mental health issues that emerged from the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".