How Facebook Photo Post’s Text Impacts User Engagement in Fashion – A Machine Learning Approach
(A2021-94644)

AUTHORS

Dimitris Gkikas, University of Patras; Prokopis Theodoridis, University of Patras; Maro Vlachopoulou, University of Macedonia

ABSTRACT

Fashion industry has become increasingly popular aiming to increase social media users’ engagement, brand awareness, and revenues. The aim of this study is to calculate the organic fashion photo posts’ text characteristics such as text readability, hashtags number and characters number. Using data mining classification models try to expose whether these characteristics affect organic post user engagement for lifetime post engaged users and lifetime people who have liked your page and engaged with your post. Post text readability score, characters number, and hashtags number are the independent variables. Post’s performances were measured by seven Facebook performance metrics, the depended variables. Data, content characteristics, and performance metrics were extracted from a business Facebook page. Finally, user engagement was calculated, and posts’ performance classification was represented through decision tree graphs. The findings reveal how post texts content characteristics impact performance metrics helping marketers to better form their Facebook organic image post strategies.