Online Popularity and Topical Interests through the Lens of Instagram

Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagging and media sharing. The network of social interactions among users models various dynamics including follower/followee relations and users' communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can "like" and comment each piece of information on the platform. In this work we investigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous interactions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they devote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users' interactions in online environments and how collective trends emerge from individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201

On the Potential of Social Media Data in Urban Planning: Findings from the Beer Street in Curitiba, Brazil

Social media makes available vast amounts of data for various types of analyses. Cities have the opportunity to explore this new data source to study urban dynamics and complement traditional data used for urban planning. We investigate Untappd social media data in the context of urban planning in Curitiba, Brazil. We analyze the project to create a Craft Beer Street, recently announced by the municipality to promote local beers in Curitiba, in order to study the potential of exploring social media data to support the planning of this project. Our results indicate that social media data could have helped to guide the decision of the Beer Street creation and can potentially become a strategic urban planning tool

Digital social interactions in the city: reflecting on location-based social media

In this paper we discuss how digital interactions are increasingly interwoven with spaces and places in urban settings and how such interactions are mediated by and in turn shape the technologies that facilitate them. We will focus on the understanding of interactions using location based social media (particularly Foursquare) as a way to reflect on issues of technological support to human activities, and on the relationship between code, digital agency and the physical world

How to study the city on instagram

We introduce Instagram as a data source for use by scholars in urban studies and neighboring disciplines and propose ways to operationalize key concepts in the study of cities. These data can help shed light on segregation, the formation of subcultures, strategies of distinction, and status hierarchies in the city. Drawing on two datasets of geotagged Instagram posts from Amsterdam and Copenhagen collected over a twelve-week period, we present a proof of concept for how to explore and visualize sociospatial patterns and divisions in these two cities. We take advantage of both the social and the geographic aspects of the data, using network analysis to identify distinct groups of users and metrics of unevenness and diversity to identify socio-spatial divisions. We also discuss some of the limitations of these data and methods and suggest ways in which they can complement established quantitative and qualitative approaches in urban scholarship

Understanding city dynamics: using geolocated social media in a problem-based activity as an investigative tool to enhance student learning

Research that tackles the pedagogical use of geolocated social media as an investigative tool for understanding cities in Geography and Urban Studies higher education programs has not been fully exploited. This study contributes by addressing the transferability of these sources as a research medium for enhancing student knowledge of urban phenomena. A collaborative problem-based learning activity was conducted in a third-year compulsory Urban Studies module of the Fundamentals in Architecture Degree at the University of Alicante. Two groups – Spanish (25 students) and English (34 students) language, participated in the activity. Foursquare and Twitter datasets were used as sources of information, and scaffolding in QGIS software, data analysis, and visualization tools were provided. Pre- and post- activity questionnaires as well as the work submitted by students gave an indication of the extent to which the activity was useful for achieving the set objective. Recurring approaches adopted by students and their “how-to” make sense of social media information enabled them to align spatiotemporal and social phenomena to the use and perception of city spaces. Students developed critical thinking and interpretative skills that are key transversal competencies for understanding the huge volume of data available in today’s digitalized world.This research was cofounded by the Vice-rectorate of Research and Knowledge Transfer of the University of Alicante, Spain (GRE18-19) and the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital. Generalitat Valenciana, Spain (GV/2021/177)

Predicting the temporal activity patterns of new venues

Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse-grained measures such as observing numbers in local venues or venues at similar places (e.g., coffee shops around another station in the same city). The advent of crowdsourced data from devices and services carried by individuals on a daily basis has opened up the possibility of performing better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from Foursquare, a location-centric platform, we treat venue categories as proxies for urban activities and analyze how they become popular over time. The main contribution of this work is a prediction framework able to use characteristic temporal signatures of places together with k-nearest neighbor metrics capturing similarities among urban regions, to forecast weekly popularity dynamics of a new venue establishment in a city neighborhood. We further show how we are able to forecast the popularity of the new venue after one month following its opening by using locality and temporal similarity as features. For the evaluation of our approach we focus on London. We show that temporally similar areas of the city can be successfully used as inputs of predictions of the visit patterns of new venues, with an improvement of 41% compared to a random selection of wards as a training set for the prediction task. We apply these concepts of temporally similar areas and locality to the real-time predictions related to new venues and show that these features can effectively be used to predict the future trends of a venue. Our findings have the potential to impact the design of location-based technologies and decisions made by new business owners