The use of visual icons and signs: investigating the punctuation of text by emoticons and communication clarity in online professional communication environments
Bibliographic record
Abstract
Computer mediated communication (CMC) is becoming increasingly prevalent and relied upon as the Internet facilitates the rapid growth of global networks and expands communication boarders. Today, many individuals rely on CMC for professional purposes, such as connecting long distance with co-workers to collaborate and advance workplace tasks. These individuals often rely on professional online collaborative programs that allow them to connect with colleagues across cities, provinces, and around the world. Relying on CMC for the transmittal of important electronic messages places it at the forefront for understanding how technical communication devices and networks function. This also requires an understanding of how ambiguity with online conversations can be decreased through the use of the Internet. However, what professional collaborative programs currently lack is a singular professional software that integrates both collaborative on-screen practices and online chatting capabilities with visual icons; or professional emoticons. The following research aims to investigate the communicative value of emoticons within a structured sentence via a study involving professional communication graduate students from Ryerson University and senior marketing communication professionals from a marketing agency in Toronto, Canada. Using concepts from critical visual methodology and a theoretical framework of visual semiotics, emoticons will be examined to see whether or not these pictorial symbols act in a similar fashion to punctuation symbols within a given sentence structure. The goal of this research was to investigate the use and meaning derived from emoticons in relation to grammatical punctuation for sentence structures in online communication environments. Specific emoticons were selected and used to measure participants‘ interpretation of each symbol within the particular context of a given sentence.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.005 |
| 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".