Submitted:
18 December 2025
Posted:
22 December 2025
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Abstract
Keywords:
1. Introduction
- What types of vulgar, obscene, and offensive language most commonly used by Gen Z on social media in Bangladesh?
- What motivations—psychological, social, or cultural—underlie the use of such language?
- How do platform features such as anonymity, algorithmic visibility, and peer interaction influence linguistic behaviour?
- What are the broader implications of these practices for digital civility, youth culture, and media ethics in Bangladesh?
2. Literature Review
3. Theoretical Framework
- Micro-level (Psychological): Online disinhibition reduces self-censorship.
- Meso-level (Cultural): Sociolinguistic norm shifts legitimize vulgar language.
- Macro-level (Technological): Media ecology and algorithms amplify provocative speech.
- Structural-level (Economic): Platform capitalism rewards engagement-driven vulgarity.
4. Research Methodology
- Bangladeshi nationality or long-term residency
- Active use of at least one social media platform (e.g., Facebook, Instagram, TikTok, YouTube, X)
- Minimum social media usage of one hour per day
- Demographic Information (age, gender, education, residence)
- Social Media Usage Patterns (platforms used, time spent, purposes)
- Frequency and Types of Vulgar Language Use
- Motivations and Psychological Factors
- Attitudes toward Digital Ethics and Language Norms
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Vulgar Language Use (Dependent Variable):Measured through self-reported frequency of using obscene, indecent, or offensive words in posts, comments, and private messages.
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Online Disinhibition:Measured using adapted items from prior studies on online disinhibition, focusing on anonymity, lack of accountability, and emotional release (Suler, 2004).
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Platform Influence:Assessed through items related to likes, shares, algorithmic visibility, and peer reactions.
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Normative Attitudes:Measured by respondents’ perceptions of acceptability and normalization of vulgar language on social media.
- Informed Consent: Participants were clearly informed about the study’s purpose, procedures, and their right to withdraw at any time.
- Confidentiality: Responses were anonymized and stored securely.
- Minimization of Harm: Survey items were framed to avoid explicit reproduction of obscene terms, focusing instead on categories and self-assessment.
- Descriptive Statistics: Frequencies, means, and standard deviations were used to summarize demographic variables and language-use patterns.
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Inferential Statistics:
- o
- Pearson correlation analysis examined relationships between online disinhibition, platform influence, and vulgar language use.
- o
- Multiple regression analysis assessed the predictive power of psychological and platform-related factors.
- Exploratory Factor Analysis (EFA): Used to identify underlying dimensions of attitudes toward vulgar language and digital ethics.
- Reliance on self-reported data may introduce social desirability bias.
- Non-probability sampling limits full generalizability.
- Cross-sectional design restricts causal inference.
5. Data Analysis and Findings
- Slang-based vulgarity (Bangla-English hybrid terms)
- Sexually suggestive language
- Derogatory insults targeting peers or public figures
- Emotion-driven profanity (anger, frustration, humor)
- Normalization of Vulgarity—viewing vulgar language as “normal” or “unavoidable” online
- Moral Discomfort—feelings of guilt or concern regarding language degradation
- Contextual Justification—acceptance of vulgar language in humor, satire, or political critique
- Vulgar and indecent language use is widespread and increasingly normalized among Bangladeshi Gen Z social media users.
- Online disinhibition significantly predicts vulgar language use.
- Algorithmic visibility and engagement incentives reinforce transgressive language.
- Gen Z users’ exhibit ambivalent attitudes, balancing normalization with moral concern.
- Gender and urban–rural differences shape linguistic practices and perceptions.
6. Discussion
7. Conclusion and Policy Recommendations
- The psychological effects of online disinhibition
- The role of algorithms in amplifying provocative content
- The difference between expressive language and harmful abuse
- Ethical responsibility in digital communication
- Context-aware moderation in Bangla and regional dialects
- Distinguishing between casual vulgarity and targeted harassment
- Reducing algorithmic amplification of abusive content
- Transparency in content ranking and moderation practices
- Platform compliance with transparency and accountability standards
- Protection against targeted harassment and gender-based abuse
- Collaboration with educators and civil society rather than solely law enforcement
- Strengthening reporting mechanisms for harassment
- Ensuring swift and fair responses to gender-based abuse
- Supporting digital mental health resources for affected users
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