Abstract

The role of Non-Governmental Organizations (NGOs) in disseminating and protecting children’s rights is fundamental by increasing society’s knowledge about the reality that children face, thus mobilizing citizen attention. In this paper, we present an original study on social media data, specifically Twitter, to analyze childhood NGOs, evaluating the success of their content (through the likes obtained by publications) from the perspective of social marketing and prospective theory. In addition, it examines the positioning of organizations concerning the types of messages identified. The methodological approach is based on data mining, content analysis, and simple correspondence analysis through which the typology of the messages and positioning map are determined. The results suggest that these organizations generate predictable communication by publishing on specific topics and only increasing the number of tweets in emergencies when they are requiring urgent help. Some tweets show an immediate risk to which children are exposed if they do not receive help, which aligns with one of the premises of the Prospect Theory. Furthermore, a more significant number of posts does not necessarily imply a greater number of likes. Three types of messages have been determined: informative tweets that point out risks (type 1), impartial dialogue tweets (type 2), and action tweets that highlight benefits (type 3), confirmed through the Kruskal-Wallis test to have a relationship with impact. The positioning map shows that type 3 messages, which World Vision Spain opts for, are the most popular, followed by type 1, which Educo leans towards. Finally, there are those of type 2, with which UNICEF Spain is associated. The main implication is that our analysis validates the use of social media such as Twitter to analyze NGOs and proposes these social media platforms to be an important tool in mobilizing the community. In addition, this study offers parameters when constructing the messages for use in social marketing campaigns according to decisions that involve risk or certainty.

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