Trolling as a Disruptive Tool for Human Rights Violations: An Exploration of the Challenges Faced by Performance Artists
Bibliographic record
Abstract
The proliferation of online platforms and digital tools has introduced both opportunities and challenges for individuals, particularly within the realm of social media. While platforms like Facebook, Twitter, Instagram, TikTok, etc. have served as avenues for cultural, social, economic, educational, state-run, and political discourse, they have also facilitated the emergence of paranoiac phenomena, such as trolling. This paper delves into the portrayal of trolling as a disruptive technological tool and cultural phenomenon in Bangladesh, specifically targeting TV actors, theatre performers, and local rural artists. The study highlights the pervasive use of trolling as a means to harass, criticize, and intimidate artists online. It reveals how trolling not only violates the human rights of these artists but also undermines their creativity, credibility, and sense of belonging within society. For methodological justification, it has employed a mixed-method research approach incorporating questionnaire surveys administered to 38 participants, interviews with two aspiring artists, three theatre artists, and three students studying theatre and performance studies, and analysis of social media comments. The paper unravelled that trolling is used exclusively as an online harassment tool to embarrass performance artists in Bangladesh. It underscores the detrimental impact of trolling, leading to psychological distress, depression, and alienation among targeted artists. In response to these challenges, the paper offers recommendations aimed at empowering artists to confront and combat online harassment, thereby safeguarding their well-being and fostering a more supportive digital environment conducive to artistic expression and innovation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".