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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".