The practice of phubbing has become an emerging phenomenon of worldwide interest to researchers. The cause is due to the fact that smartphones are ubiquitous and are often used in co-present interactions. This behavior is generally considered inappropriate and is called “phubbing”. Phubbing, as described by Chotpitayasunondh and Douglas (2018), is the act of snubbing someone in a social setting by looking at one's phone instead of paying attention to the other person. The aim of this article is to provide an overview of research studies on phubbing through a review of the current literature. To do this, a search was carried out in an international database, finding 84 relevant articles in English that appeared in peer-reviewed journals published between 2012, the year in which the term ‘phubbing’ appears, and January 2020. The review covers the main fields of research studies on phubbing behaviors. Likewise, the results of the study show the distribution of published articles on phubbing by year that detail the type of study and the methodological approach and, finally, the research journals that have published articles on phubbing. The results of this review are expected to stimulate and guide future research in this field.

Online social networks have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, it is vital to have an accurate understanding of the contextual content of social users, thus establishing grounds for measuring their social influence accordingly. In particular, there is the need for a better understanding of domain-based social trust to improve and expand the analysis process and determining the credibility of Social Big Data. The aim of this paper is to determine domain-based social influencers by means of a framework that incorporates semantic analysis and machine learning modules to measure and predict users’ credibility in numerous domains at different time periods. The evaluation of the experiment conducted herein validates the applicability of semantic analysis and machine learning techniques in detecting highly trustworthy domain-based influencers.