Assessing the Impact of Granular Privacy Controls on Content Sharing and Disclosure on Facebook
Bibliographic record
Abstract
We examine the role of granular privacy controls on dynamic content-sharing activities and disclosure patterns of Facebook users based on the exogenous policy change in December 2009. Using a unique panel data set, we first conduct regression discontinuity analyses to verify a discontinuous jump in context generation activities and disclosure patterns around the time of the policy change. We next estimate unobserved effects models to assess the short-run and long-run effects of the change. Results show that Facebook users, on average, increase use of wall posts and decrease use of private messages after the introduction of granular privacy controls. Also, users’ disclosure patterns change to reflect the increased openness in content sharing. These effects are realized immediately and over time. More importantly, we show that user-specific factors play crucial roles in shaping users’ varying reactions to the policy change. While more privacy sensitive users (those who do not reveal their gender and/or those who have exclusive disclosure patterns ex ante) share more content openly and less content secretly than before, less privacy sensitive users (those who reveal their gender and/or those who have inclusive disclosure patterns ex ante) share less content openly and more content secretly after the change. Hence, the policy change effectively diminishes variation among Facebook users in terms of content-generation activities and disclosure patterns. Therefore, characterizing the privacy change as a way to foster openness across all user categories does not reveal the change’s true influence. Although an average Facebook user seems to favor increased openness, the policy change has different impacts on various groups of users based on their sensitivity to privacy, and this impact is not necessarily toward increased openness. To our knowledge, this is the first study that relies on observational data to assess the impact of a major privacy change on dynamic content-sharing activities and the resulting disclosure patterns of Facebook users.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".