User-Centric Modeling of Online Hate Through the Lens of Psycholinguistic Patterns and Behaviors in Social Media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hate speech in social media is a growing problem that reinforces racial discrimination and mistrust between people, leading to physical crimes, violence, and fragmentation in world communities. Although previous studies showed the potential of user profiling in hate speech detection in social media, there has not been a thorough analysis of users’ characteristics and dispositions to understand the development of hate attitudes among users. To bridge this gap, we investigate the role of a wide range of psycholinguistic and behavioral traits in characterizing and distinguishing users prone to post hate speech on social media. Considering anti-Asian hate during the COVID-19 pandemic as a case study, we curate a dataset of 5 417 041 tweets from 3001 Twitter users prone to publish hate content (aka hateful-to-be users) and a corresponding matched set of 3001 control users. Our findings reveal significant statistical differences in most dimensions of psycholinguistic attributes and online activities of hateful-to-be users compared to control users. We further develop a classifier and demonstrate that features derived from user timelines are strong indicators for automatically predicting the onset of hateful behavior.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it