The last decade has seen a huge expansion in the use of social media to extract data about human behaviour. While metadata and textual information have taken the lion's share as data sources for social media analysis, geotagged image-based platforms represent an unprecedented and as yet almost untapped source of data to analyse human behaviour and characterise the physical space we live in. In this paper we investigate the use of Instagram photos to analyse tourism consumption. We take the city of Venice (Italy) as a case study and we collect a dataset of about 90k photos taken between January 2014 and December 2015. Using computer vision techniques, we build a supervised classifier which assigns each photo to one of six different categories. We then observe how the frequency and spatial distribution of these categories varies with time. This in turn allows us to confirm the existence of a number of touristic hotspots associated with different events, such as Venice Carnival and Biennale. Our analysis also uncovers the existence of touristic flows associated with these events, such as the Folklore Line that marks the path of tourists from "Santa Lucia" railway station to "San Marco" square during the Carnival period. Overall, our findings confirm the effectiveness of the proposed framework to investigate tourism consumption using Instagram data.
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Venice through the Lens of Instagram: A Visual Narrative of Tourism in Venice
Semantic Scholar · Geography · 2018
Abstract
The last decade has seen a huge expansion in the use of social media to extract data about human behaviour. While metadata and textual information have taken the lion's share as data sources for social media analysis, geotagged image-based platforms represent an unprecedented and as yet almost untapped source of data to analyse human behaviour and characterise the physical space we live in. In this paper we investigate the use of Instagram photos to analyse tourism consumption. We take the city of Venice (Italy) as a case study and we collect a dataset of about 90k photos taken between January 2014 and December 2015. Using computer vision techniques, we build a supervised classifier which assigns each photo to one of six different categories. We then observe how the frequency and spatial distribution of these categories varies with time. This in turn allows us to confirm the existence of a number of touristic hotspots associated with different events, such as Venice Carnival and Biennale. Our analysis also uncovers the existence of touristic flows associated with these events, such as the Folklore Line that marks the path of tourists from "Santa Lucia" railway station to "San Marco" square during the Carnival period. Overall, our findings confirm the effectiveness of the proposed framework to investigate tourism consumption using Instagram data.