Identifying anger in digital spaces
Bibliographic record
Abstract
This thesis examines expressions and interpretations of anger in computer-mediated-communication (CMC) and identifies specific tactics and important factors in this expression and interpretation. This research is based on online data collection and subsequent online survey. The data collection portion of the research consisted of gathering samples of expressions of anger from social media platforms Reddit, YouTube, and TikTok. The samples were all gathered from discussions regarding the Canadian Housing Crisis. These samples were categorized and co-validated into different types of anger, and then analyzed for trends in specific textisms (linguistic features characteristic of CMC). Patterns in linguistic features found from this process were then used to create an online survey. The survey provided more detailed information for how CMC users interpret and express anger and yielded a total of 90 responses. The survey consisted of four major sections: data validation, where participants were asked to categorize samples from the previous data collection, roleplay, where participants were asked to type out what the angry message would be sent in response to a specific prompt, ranking, where participants ranked the emotional expression of a message with varying textisms, and analysis, where participants were provided texts asked to identify why this would be interpreted as angry or not. The data collection and survey both found that specific features, specifically using a period for the final sentence in a message and quotation marks, are the features that are most interpreted as indicating anger. However, the features of expressions of anger across both the data collection and the survey also included questions, but this did not affect the interpretation of anger. This research finds that the most critical feature to interpreting and expressing anger is context. Overall, this thesis discusses the ways that CMC users interpret and express anger and the ways in which gender interact with this expression and interpretation, as well as highlights the large role context plays in interpretation of anger in CMC.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".