The impact of family violence incidents on personality changes: An examination of social media users’ messages in <scp>C</scp>hina
Bibliographic record
Abstract
Changes in personality tend to be intertwined with life events (e.g., family violence [FV]). This study aimed to examine the personality changes before and after an FV incident using Weibo data. Samples were selected from 1.16 million Weibo users in China who had posted their own FV experience as victims. We used Linguistic Inquiry and Word Count (LIWC) to extract the linguistic features of these unstructured texts as the scores of participants' personality. We built prediction models to measure and compare personality differences between the victim group and control group in Sample 1; and personality changes between the victim group and control group before and after an FV incident in Sample 2. Results showed that the victims' neuroticism increased and conscientiousness decreased after experiencing FV. At the same time, their agreeableness and openness levels were lower than those of the control group. Implications and limitations are also discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".