This paper presents mPASS (mobile Pervasive Accessibility Social Sensing), a system designed to collect data about urban and architectural accessibility and to provide users with personalized paths, computed on the basis of their preferences and needs. The system combines data obtained by sensing, crowdsourcing and mashing-up with main geo-referenced social systems, with the aim of offering services based on a detailed and valid data set.

This work presents a novel geospatial mapping service, based on OpenStreetMap, which has been designed and developed in order to provide personalized path to users with special needs. This system gathers data related to barriers and facilities of the urban environment via crowdsourcing and sensing done by users. It also considers open data provided by bus operating companies to identify the actual accessibility feature and the real time of arrival at the stops of the buses. The resulting service supports citizens with reduced mobility (users with disabilities and/or elderly people) suggesting urban paths accessible to them and providing information related to travelling time, which are tailored to their abilities to move and to the bus arrival time. The manuscript demonstrates the effectiveness of the approach by means of a case study focusing on the differences between the solutions provided by our system and the ones computed by main stream geospatial mapping services.

This paper presents a study on urban data crowdsourcing driven by Geo-Zombie, a multimedia mobile application we designed and developed to engage pedestrians in taking note of urban architectural impediments and facilities by documenting them through pictures and multimedia data. Geo-Zombie aims at transforming the civic activity of contributing into a virtual gamified experience where players attempt to escape from horrific situations in which zombies are ready to cannibalize unsuspecting walkers. In some sense, walkers that kill zombies deeply reconnect with the concept of imminent danger which can be fought resorting to appropriate civic actions. To challenge our initial hypotheses we conducted a design process, starting with a concept generation where three different concepts were discussed which gave rise to five different multimedia mobile apps including the one with zombies. Then, focus group, experience prototyping, application design and implementation, and finally field trials were exploited to refine the design and to select the best apps out of the five that better responded to the need of involving common people in collecting urban accessibility data. It is worth noting that the experiences of use with 50 avid walkers have demonstrated that a multimedia mobile app with maps and zombies can be a concrete step towards a social inclusion strategy while inviting new reflections and discussions on the issue of urban data crowdsourcing.

BackgroundData concerning patients originates from a variety of sources on social media.ObjectiveThe aim of this study was to show how methodologies borrowed from different areas including computer science, econometrics, statistics, data mining, and sociology may be used to analyze Facebook data to investigate the patients’ perspectives on a given medical prescription.MethodsTo shed light on patients’ behavior and concerns, we focused on Crohn’s disease, a chronic inflammatory bowel disease, and the specific therapy with the biological drug Infliximab. To gain information from the basin of big data, we analyzed Facebook posts in the time frame from October 2011 to August 2015. We selected posts from patients affected by Crohn’s disease who were experiencing or had previously been treated with the monoclonal antibody drug Infliximab. The selected posts underwent further characterization and sentiment analysis. Finally, an ethnographic review was carried out by experts from different scientific research fields (eg, computer science vs gastroenterology) and by a software system running a sentiment analysis tool. The patient feeling toward the Infliximab treatment was classified as positive, neutral, or negative, and the results from computer science, gastroenterologist, and software tool were compared using the square weighted Cohen’s kappa coefficient method.ResultsThe first automatic selection process returned 56,000 Facebook posts, 261 of which exhibited a patient opinion concerning Infliximab. The ethnographic analysis of these 261 selected posts gave similar results, with an interrater agreement between the computer science and gastroenterology experts amounting to 87.3% (228/261), a substantial agreement according to the square weighted Cohen’s kappa coefficient method (w2K=0.6470). A positive, neutral, and negative feeling was attributed to 36%, 27%, and 37% of posts by the computer science expert and 38%, 30%, and 32% by the gastroenterologist, respectively. Only a slight agreement was found between the experts’ opinion and the software tool.ConclusionsWe show how data posted on Facebook by Crohn’s disease patients are a useful dataset to understand the patient’s perspective on the specific treatment with Infliximab. The genuine, nonmedically influenced patients’ opinion obtained from Facebook pages can be easily reviewed by experts from different research backgrounds, with a substantial agreement on the classification of patients’ sentiment. The described method allows a fast collection of big amounts of data, which can be easily analyzed to gain insight into the patients’ perspective on a specific medical therapy.