Introduction

Data mining and big data analysis have been crucial to decode the user's activities in online platforms. Additionally, social listening methodologies have managed to break the 'question and answer' scheme of research, using a contextual reading of the social media conversation through artificial intelligence (AI) programs. Nonetheless, the latter methodologies lack profundity in terms of conversational quality (Duarte, Llansó & Loup, 2018) and deep understanding of the motives hidden behind the user's behaviours. Now, the proliferation of social networks has facilitated the creation of content-based research. This ensures dynamic, fast and cost-efficient practices that step away from the traditional methods.

Content-based research demands the creation and distribution of powerful content stimuli. Content is capable of instantly provoking a wide volume of users in social media, who organically decide to share their perspectives about a specific topic. These methodologies enhance the creation and extraction of profound insights, which stem from a qualitative analysis of the conversation and a quantitative analysis of social media metrics. This approach proposes that the best way to discover users' perspectives in social media would be building digital laboratories through an anonymous open field for informants to actively participate in and/or on.