Over the past decades, the complex phenomenon of volunteering has been mainly analyzed within the field of economic literature with respect to its “economic value added,” that is the capability of these activities to increase the productivity level of some specific goods or services. The paper adopts a different point of view, in that voluntary organizations are analyzed as places of innovation, where new jobs arise and people acquire new skills. Thus, volunteering can be understood as a “social innovation” factor. In order to gain a deeper insight into the types of voluntary works, we have used data coming from the Istat survey “Multiscopo, Aspetti della vita quotidiana” (Multipurposes survey, daily life aspects), released in the year 2013. In our textual analysis, we have utilized the information included in the open-ended question section provided by respondents regarding the description of the tasks performed individually as volunteers. After stemming, lemmatization, and cleaning, the data have been analyzed by means of community detection based on semantic network analysis, with the purpose of identifying job patterns, then by a clustering procedure on answers (Reinert approach to textual clustering), and lastly through correspondence analysis on generalized aggregated lexical tables (CA-GALT) with the aim to explore volunteers’ profiles. In particular, we have singled out differences in gender, age, educational level, region of residence, and type of voluntary association.
