Social-media big data can expose dynamics that cross-sectional studies miss.

Evidence

In terms of future research directions, the increasing use of large language models and big data methodologies in social psychological research holds the potential to offer more nuanced and ecologically valid insights into questions that, to date, have been primarily investigated through cross-sectional studies with simple experimental designs. For example, future research could examine how events such as a group apology affect group members’ behaviors on social network platforms (e.g., through expressions of acceptance or rejection of the apology in tweets and posts) and how these behaviors unfold over time (e.g., see research on how political events influence the frequency of victimhood narratives in Telegram communities; Boussalis et al. 2024). — Nurit Shnabel and Johannes Ullrich, Identity Needs in Intergroup Relations, 149

Categorization

• XREF: Links the apology-rejection sequence and victimhood-narrative methods to longitudinal, ecologically valid study designs. — connects to: Social modeling through media can bypass trial-and-error limits and spread behavior change worldwide; Social media content lacks the spatial and temporal context real understanding requires. — Jenny Odell, How to Do Nothing: Resisting the…, 527
• QUESTION: Could revisit how social-platform data might trace the temporal arc of moral- and agentic-identity needs after an apology event.

Broader Connections

• Social media content lacks the spatial and temporal context real understanding requires. #complicates (0.78) — Jenny Odell, How to Do Nothing, 527
• Social modeling through media can bypass trial-and-error limits and spread behavior change worldwide #expands (0.72)