When Likes Turn to Loathing: A Critical Exploration of Toxicity of Influencer Culture on Instagram in Pakistan
Abstract: Social media has provided a space where individuals and groups can present their diverse opinions, but it can also promote prejudice, bias, fear, and hatred, spread misinformation, and provoke violence and harassment through its feedback mechanisms. Keeping in view the mentioned characteristics, the present study inspects the continuous hate speech against a Dubai-based Pakistani social media influencer, Adnan Zafar. The study was conducted on comments on his Instagram account (@ken_doll_dubaiii) comprising of offensive gender-based abuse, monetary condemnation and transgressive allegations using social media Critical Discourse which refers to the language and communication used in social media messages. It involves analyzing the words, phrases, and structure of these messages to comprehend the core beliefs, values, and attitudes being conveyed. The comments extracted using free-of-cost Instagram API, reflect established and firm patriarchal norms and the use of deliberate religious commentary to impose adherence. The findings of the study highlight that social media despite providing a platform for open discussions in conservative societies like Pakistan, also shows the disparity in authority and cultural partiality. The case of Adnan Zafar also reveals the multi-facet perspective of social media to be used to raise a voice against well-established practices in society as well as a tool for intensifying hate speech. The study also signifies the wider discussion regarding the role of social media in mirroring society's practices, along with the pressing requirement to tackle and counter hate speech on digital platforms. Keywords: Speech, Instagram, Pakistan, Patriarchal, Allegations.
Institutions
- University of Gujrat (PK)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-10-04
- DOI
- https://doi.org/10.5281/zenodo.13891658
- Primary Topic
- Hate Speech and Cyberbullying Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00