In recent years, the growth of social media platforms has led to an increase in the number of influencers who work in/for the fashion industry - these individuals have become attractive marketing partners for fashion brands. As a result, understanding how to measure the performance of fashion influencers (and what kinds of behavior lead to better performance) is paramount. This paper presents a methodology for characterizing fashion influencers' behavior on Instagram. Using a preexisting dataset, we analyze Instagram fashion influencers exploring behavioral patterns, engagement metrics, and content dynamics. Our analyses uncover insights about influencer behavior, gender-based performance, and correlations between engagement metrics and posts' metadata (such as caption sentiment, length, and hashtag usage). Additionally, our research highlights the impact of early engagement and post comments on overall engagement rates, highlighting the role of community interaction. Though focused on Instagram influencers, our proposed methodology could be adapted to diverse datasets and social media platforms. In sum, we present here a preliminary study for understanding and decoding the complex dynamics of fashion influencer culture on social media, offering actionable insights for marketers, brands, aspiring influencers, scholars, and regular social media users.
Characterizing fashion influencers’ behavior on instagram
Lucia C. Passaro;
2024-01-01
Abstract
In recent years, the growth of social media platforms has led to an increase in the number of influencers who work in/for the fashion industry - these individuals have become attractive marketing partners for fashion brands. As a result, understanding how to measure the performance of fashion influencers (and what kinds of behavior lead to better performance) is paramount. This paper presents a methodology for characterizing fashion influencers' behavior on Instagram. Using a preexisting dataset, we analyze Instagram fashion influencers exploring behavioral patterns, engagement metrics, and content dynamics. Our analyses uncover insights about influencer behavior, gender-based performance, and correlations between engagement metrics and posts' metadata (such as caption sentiment, length, and hashtag usage). Additionally, our research highlights the impact of early engagement and post comments on overall engagement rates, highlighting the role of community interaction. Though focused on Instagram influencers, our proposed methodology could be adapted to diverse datasets and social media platforms. In sum, we present here a preliminary study for understanding and decoding the complex dynamics of fashion influencer culture on social media, offering actionable insights for marketers, brands, aspiring influencers, scholars, and regular social media users.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.