The impact of social media: how Instagram & Snapchat are revolutionizing the fashion industry
Bibliographic record
Abstract
Social media has been revolutionizing our ways of learning, engaging, and sharing information on the latest trends within the fashion industry. This research project focuses on two social media platforms that are highly relevant and influential in the fashion industry. Instagram, a social media application that was originally created to share photos between intimate social networks, has now become a powerful marketing platform. Now, one can carefully curate one’s profile and sell an aesthetically pleasing image of oneself/item, all through strategic techniques such as framing, editing, or simply using the filters provided by both Instagram (e.g. Sepia, Valencia) and Snapchat. Snapchat, a social media application originally used to share selfies amongst one’s intimate social group, intended to “express yourself, and reflect individuals based on a moment” (Evan Spiegal, CEO of Snapchat, 2016). The company now also promotes their app as a marketing tool and an advertising platform that targets more than 100 million active users daily between the ages of 18 and 35 years old. Initially, these two apps were intended to be used as entertainment tools, however, now they are also used to help re-structure a brand’s business model and approach. The purpose of this research is to investigate the effects of both Instagram and Snapchat, on traditional practices within the fashion industry, with a primary focus on the runway and advertising.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".