Evolution of the Web of Social Machines: A Systematic Review and Research Challenges

Social machines (SMs) are the term used to define processes in which the people do the creative work and the machine does the administration. The concept was scarcely studied until 2013, when the series of workshops on SMs was created, and the topic began to receive more attention. However, it is not clear how research has evolved since then. This article aims to investigate and summarize how the research field of SM has evolved since 2013, to outline the state of the art and practice, and identify research opportunities within this field. We performed a systematic literature review analyzing the quantity and quality of publications, the main topics addressed, the current classifications of SMs, the context in which the concepts are used, and the main perceived challenges. We identified and analyzed 56 relevant studies addressing 12 topics, representing the current practical landscape of research regarding SM. Our findings suggest that: 1) research interest in SM is increasing, but is still concentrated into two research clusters; 2) topics are grouped under two main headings: a) human behavior and b) software development; 3) there is still a need for a common taxonomy to define and classify SM; 4) the main contexts are crowdsourcing and social networks, and the majority of studies are small-scale studies in an academic setup; and 5) more empirical rigor and evidence is needed regarding their use, benefits and challenges, despite some evidence regarding challenges related to user engagement, trust, scalability, and a better human–machine collaboration. Finally, a vision of the future of SMs, with the integration of web of people, artificial intelligence, and things, is also presented and discussed.