JOURNAL ARTICLE

Barthes meets vlogging: A semiotic analysis of a top Filipino influencer's videos.

  • Published In: Journal of Asian Pacific Communication (John Benjamins Publishing Co.), 2025, v. 35, n. 1. P. 29 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Jose, Paul John C.; Velasco, John Errol; Liwanag, Leslie Anne L.; Liwanag, Lois Mauri Anne L. 3 of 3

Abstract

This article applies Roland Barthes' semiology in analyzing the content of the 15 most popular vlogs by Cong TV, a top Filipino vlogger. Using Barthes' framework, the study uncovers hidden myths and ideological discourses embedded in Cong TV's works that often reinforce the perspectives of the ruling class, whether consciously or unconsciously. The analysis identifies rhetorical strategies, including identification, erasure of history, inoculation, and intertextuality, while also identifying beneficiaries and victims within these narratives. The findings show the subtle ways in which humor and relatable content perpetuate social inequalities, discrimination, and colonial mentalities. The study highlights the significant role influencers play in shaping societal norms and calls for greater awareness and responsibility among content creators. It challenges the audience and scholars alike to scrutinize seemingly harmless content for deeper, often overlooked, cultural implications. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Asian Pacific Communication (John Benjamins Publishing Co.). 2025/01, Vol. 35, Issue 1, p29
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2025
  • ISSN:0957-6851
  • DOI:10.1075/japc.00115.jos
  • Accession Number:187726009
  • Copyright Statement:Copyright of Journal of Asian Pacific Communication (John Benjamins Publishing Co.) is the property of John Benjamins Publishing Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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