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The spread of Internet and online social media has created a huge amount of data able to provide new insights to researchers in different disciplinary fields, but it also presents new challenges for data science. Data arising from online social networks can be naturally coded as relational data in affiliation and adjacency matrices, then analyzed with social network analysis. In this study, we apply an interdisciplinary approach (based on automatic visual content analysis, social network analysis, and exploratory statistical techniques) to define and derive a suitable indicator for characterizing places, along with the online activities of travelers, in terms of sharing images. We envisage a novel storytelling perspective where stories are related to places and the narrative activity is realized through posting images. Specifically, we use data extracted from an online social network (i.e., Instagram) to identify travelers' paths among sites of interests. Starting from a large collection of pictures geolocalized in a pre-specified set of locations (i.e., five locations in the Campania region of Italy during the 2018 Christmas season), we use automatic alternative text to produce an ex-post taxonomy of images on the most recurrent themes emerging from pictures posted on Instagram. Quantitative measures defined on the co-occurrence of locations and the emerging themes are used to build a statistical indicator able to characterize paths among different locations as narrated from travelers' posts. The proposed analysis, presented and discussed along with real data, can be useful for stakeholders interested in the fields of policy-making, communication design, and territory profiling strategies. [ABSTRACT FROM AUTHOR]