Linguistic Formality and Audience Engagement: Investors' Reactions to Characteristics of Social Media Disclosures*
Bibliographic record
Abstract
ABSTRACT As firms increasingly use social media to provide disclosures to investors, it is important to understand whether the characteristics that are associated with these disclosures lead to different reactions from investors than disclosures provided via more traditional channels. In this paper, we use an experiment to examine whether linguistic formality in positive news disclosures, and engagement of social media users surrounding the disclosures (e.g., “likes” and “retweets”), affect investors' judgments about a firm and its management. Results suggest that, as predicted, investors are more sensitive to signals of audience engagement when disclosures use informal rather than formal language. Specifically, when associated with signals of high audience engagement, the use of informal language leads to greater willingness to invest than the use of formal language in a disclosure. However, also as predicted, the use of informal language hurts willingness to invest when associated with signals of low audience engagement. In two follow‐up experiments, we investigate how news valence and linguistic formality are expected to affect the level of audience engagement in the first place, and we investigate whether managers strategically vary their use of linguistic formality based on characteristics of the setting. Overall, our results provide evidence on how firms might use social media disclosures to better connect with investors. This study contributes to the growing literature on linguistic attributes of disclosures, and the emerging literature investigating the consequences of issuing financial disclosures through social media.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".