Socially Responsible Investing: Is Social Media an Influencer?
Bibliographic record
Abstract
As digital connectivity transforms financial decision-making, this study offers one of the first empirical investigations into the relationship between social media use and socially responsible investing (SRI). Using data from the 2021 National Financial Capability Study, multinomial regression analysis was used to explore whether people who rely on social media for investment decisions were more likely to invest in ways that reflect their values. The results show that investors who use social media for investment information are more likely to value being socially responsible as an important reason for investing. Younger, less experienced, and more risk-tolerant investors were especially likely to follow SRI strategies, and certain platforms like Twitter were more associated with SRI interest than others. These findings suggest that social media is not just a platform for sharing information; it may also shape how people think about investing and the role their money can play in making a societal difference. As online platforms continue to influence financial behavior, understanding their impact on values-based investing becomes increasingly important. This research contributes novel insights to the emerging intersection of social media, behavioral finance, and values-driven investing.
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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 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".