Bibliographic record
Abstract
This article is devoted to the study of the phenomenon of trolling in the network, which is gaining more and more popularity among users. Based on the study of different stages of the manifestation of trolling, the author proposed to classify them according to psychological, social and political characteristics. The main purpose of the study is to determine the perception of provocative and aggressive behavior by network users, how closely they are familiar with the terms “troll” and “trolling”, and also to warn them from interacting with potential trolls on the network when consuming content. Research methods were based on deduction techniques, comparative analysis, retrospection. Moni- toring of foreign sites and scientific materials was used, including popular among young people social networks, namely Instagram and Tiktok. А historical analysis of the emergence and development of in- formation wars was carried out. The aim of the sociological survey was to identify understanding among network users: “What is trolling and why is it dangerous?”. The scientific novelty of the work lies in the fact that the author for the first time identified and pre- sented the absolute difference between trolling in certain countries like Russia and the United States, and trolling in the expanses of the kaznet. The results of the study showed that in the modern realities of the existence of trolling is an integral part of ethical ideas in the media space. The development of trolling in social networks has a destructive effect on the interpersonal relationships of users, gaining access to competent, expert opinions and dis- seminating unverified and false information. Key words: Internet, trolling, social networks, trends, kaznet, cyberbullying.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".