How to Engage Followers: Classifying Fashion Brands According to Their Instagram Profiles, Posts and Comments
- In this article we show how fashion brands communicate with their follower on Instagram. We use a continuously update dataset of 68 brands, more than 300,000 posts and more than 40,000,000 comments. Starting with descriptive statistics, we uncover different behavior and success of the various brands. It turns out that there are patterns specific to luxury, mass-market and sportswear brands. Posting volume is extremely brand dependent as is the number of comments and the engagement of the community. Having understood the statistics, we turn to machine learning techniques to measure the response of the community via comments. Topic models help us understand the structure of their respective community and uncover insights regarding the response to campaigns. Having up-to-date content is essential for this kind of analysis, as the market is highly volatile. Furthermore, automatic data analysis is crucial to measure the success of campaigns and adjust them accordingly for maximum effect.
Author: | Stefanie Scholz, Christian Winkler |
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DOI: | https://doi.org/10.5121/csit.2020.101704 |
Parent Title (English): | Computer Science & Information Technology (CS & IT) |
Publisher: | AIRCC Publishing Corporation |
Document Type: | conference proceeding (article) |
Language: | English |
Reviewed: | Begutachtet/Reviewed |
Release Date: | 2024/08/09 |
Tag: | Instagram, Fashion Brands, Data Extraction, Marketing, Analysis, Artificial Intelligence, Netnography, Descriptive Statistics, Visualization, Community Engagement, Artificial Intelligence, Unsupervised Learning, Topic Modelling |
Pagenumber: | 22 |
First Page: | 223 |
Last Page: | 234 |
Konferenzangabe: | 8th International Conference on Computational Science and Engineering (CSE 2020) |
institutes: | Fakultät Betriebswirtschaft |
Research Themes: | Soziale & ökonomische Transformation |