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Record W4387397688 · doi:10.1111/jbfa.12758

The evolution of corporate twitter usage

2023· article· en· W4387397688 on OpenAlexaff
M. Al Guindy, James P. Naughton, Ryan Riordan

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsQueen's UniversityCarleton University
Fundersnot available
KeywordsSocial mediaBusinessStock (firearms)Context (archaeology)MicrobloggingSet (abstract data type)Stock marketMarketingAdvertisingAccountingWorld Wide WebComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract We study the evolution of corporate Twitter usage from 2006 to 2021 using a comprehensive dataset of over 19 million tweets covering publicly listed US firms. Overall, we find that Twitter usage has changed substantially over the past 15 years, with a broader set of firms using Twitter and with more firms using Twitter to communicate financial information. The stock market response to tweets, measured using abnormal returns and trading volume, is positive and significant in all periods but has declined over time. Retweeting and “liking” behavior has increased substantially in recent years, suggesting broader dissemination. In addition, although firms continue to use press releases for the most economically important information, our results suggest that firms are increasingly substituting tweets for press releases. Moreover, we find that firms continue to be strategic with both the content and timing of their Twitter usage. Collectively, our findings provide useful context for the interpretation of prior studies and valuable information for the design of future studies on the corporate use of 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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.226
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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