Emojified communication: How do news organizations use emojis on social media?
Bibliographic record
Abstract
In this paper, a largely under-researched area of news study is explored which is the use of emojis by news organizations. Though emoticons and then emojis have been part of social media since the beginning, there is little empirical and theoretical research on this aspect of news. A new conceptualization of this digital phenomenon is introduced which is called “emojified communication”. Situated within Semiotics theory, it is defined as a means of communicating nonverbal meaning to complement the main media content, and it serves in offering contextual insight and enriching the message’s cognitive and emotional cues . Using a mixed method approach, the results show that when it comes to emojified news, news organizations predominately use various people’s icons and positive sentiments, creating relatable and lively posts that can enhance the news items’ emotional appeals and cognitive cues. To target certain audiences, news organizations used emojis that showcased women slightly more than those that referenced men, while light skin colors were used much more than darker ones. The paper concludes with a discussion on the possible drawbacks of using emojis in news branding.
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 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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".