An Advanced Deep Learning Framework for Discerning Relevant and Irrelevant Content in Image Analysis for Tourism Studies

2025 · doi:10.22541/au.174143720.08508341/v1
preprint OA: closed
Full text JSON View at publisher

Abstract

The rise of social media has transformed tourism research, providing new ways to understand travelers’ perspectives. Picture analysis, in particular, offers valuable insights into tourist preferences, popular attractions, and emotions conveyed through images. This analysis can be performed manually or with artificial intelligence. However, a significant challenge arises from the presence of memes and advertisements related to informal markets, which complicate data usability. Manually filtering such content is labor-intensive and inefficient. To address this, we propose a robust analytical methodology that combines traditional and modern learning techniques. Our approach achieves over 89% accuracy in its classification task, streamlining data processing for tourism research. By automating image filtering, this method enhances dataset quality and improves the reliability of tourism analyses.
Full text 2,044 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

The rise of social media has transformed tourism research, providing new ways to understand travelers’ perspectives. Picture analysis, in particular, offers valuable insights into tourist preferences, popular attractions, and emotions conveyed through images. This analysis can be performed manually or with artificial intelligence. However, a significant challenge arises from the presence of memes and advertisements related to informal markets, which complicate data usability. Manually filtering such content is labor-intensive and inefficient. To address this, we propose a robust analytical methodology that combines traditional and modern learning techniques. Our approach achieves over 89% accuracy in its classification task, streamlining data processing for tourism research. By automating image filtering, this method enhances dataset quality and improves the reliability of tourism analyses. Supplementary Material File (content_disc__comp_int_.pdf) - Download - 2.84 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Keywords

Authors Metrics & Citations Metrics Article Usage 265views 147downloads Citations Download citation Angel Díaz-Pacheco, Miguel Ángel Alvarez Carmona, Ramón Aranda, et al. An Advanced Deep Learning Framework for Discerning Relevant and Irrelevant Content in Image Analysis for Tourism Studies. Authorea. 08 March 2025. DOI: https://doi.org/10.22541/au.174143720.08508341/v1 DOI: https://doi.org/10.22541/au.174143720.08508341/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00