The sharing economy model is a contested concept: on one hand, its proponents have praised it to be enabler of fair marketplaces, with all participants receiving equal opportunities; on the other hand, its detractors have criticised it for actually exacerbating preexisting societal inequalities. In this paper, we propose a scalable quantitative method to measure participants' diversity and inclusion in such marketplaces, with the aim to offer evidence to ground this debate. We apply the method to the case of the Airbnb hospitality service for the city of London, UK. Our findings reveal that diversity is high for gender, but not so for age and ethnicity. As for inclusion, we find strong signals of homophily both in terms of gender, age and ethnicity, thus suggesting that under-represented groups have significantly fewer opportunities to gain from this market model. Interestingly, the sentiment associated to same-group (homophilic) interactions is just as positive as that associated to heterophilic ones, even after controlling for Airbnb property's type, price and location. This suggests that increased diversity and inclusion are desirable not only for moral but also for economic and market reasons.

Accurate daily rainfall predictions are becoming increasingly important, particularly in the era of changing climate conditions. These predictions are essential for various sectors, including agriculture, water resource management, flood preparedness, and pollution monitoring. This study delves into the complex relationship between meteorological data, with a focus on the accurate forecasting of rainfall by identifying the impact of temperature variations on rainfall patterns in different regions of the United Kingdom (UK). The meteorological data was collected from the National Aeronautics and Space Administration (NASA) and covers daily observations from January 1, 1981, to July 31, 2023, in four distinct regions of the UK: England, Wales, Scotland, and Northern Ireland. The main objective of this research is to introduce hybrid deep learning models, namely Convolutional Neural Networks (CNN) with Long Short Term Memory (LSTM) and Recurrent Neural Networks (RNN) with Long Short Term Memory (LSTM), for predicting daily rainfall using time-series data from the four UK countries, specifically designed for daily rainfall forecasting of four regions in the UK. The models are fine-tuned using the hyperparameter optimisation method. Comprehensive performance evaluations, including Loss Function, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), are employed to compare the effectiveness of our proposed hybrid models with established baseline models, including LSTM, stacked LSTM, and Bidirectional LSTM. Additionally, a visual analysis of actual and predicted rainfall data is conducted to identify the most proficient forecasting model for each region. Results reveal that the proposed hybrid models consistently outperform other models in terms of both quantitative performance metrics and visual assessments across all four regions in the UK. This research contributes to improved rainfall forecasting methodologies, which are critical for sustainable agricultural practices and resource management.

There is a rumbling debate over the impact of gentrification: presumed gentrifiers have been the target of protests and attacks in some cities, while they have been welcome as generators of new jobs and taxes in others. Census data fails to measure neighborhood change in real-time since it is usually updated every ten years. This work shows that Airbnb data can be used to quantify and track neighborhood changes. Specifically, we consider both structured data (e.g., number of listings, number of reviews, listing information) and unstructured data (e.g., user-generated reviews processed with natural language processing and machine learning algorithms) for three major cities, New York City (US), Los Angeles (US), and Greater London (UK). We find that Airbnb data (especially its unstructured part) appears to nowcast neighborhood gentrification, measured as changes in housing affordability and demographics. Overall, our results suggest that user-generated data from online platforms can be used to create socioeconomic indices to complement traditional measures that are less granular, not in real-time, and more costly to obtain.

Airbnb is one of the most successful examples of sharing economy marketplaces. With rapid and global market penetration, understanding its attractiveness and evolving growth opportunities is key to plan business decision making. There is an ongoing debate, for example, about whether Airbnb is a hospitality service that fosters social exchanges between hosts and guests, as the sharing economy manifesto originally stated, or whether it is (or is evolving into being) a purely business transaction platform, the way hotels have traditionally operated. To answer these questions, we propose a novel market analysis approach that exploits customers’ reviews. Key to the approach is a method that combines thematic analysis and machine learning to inductively develop a custom dictionary for guests’ reviews. Based on this dictionary, we then use quantitative linguistic analysis on a corpus of 3.2 million reviews collected in 6 different cities, and illustrate how to answer a variety of market research questions, at fine levels of temporal, thematic, user and spatial granularity, such as (i) how the business vs social dichotomy is evolving over the years, (ii) what exact words within such top-level categories are evolving, (iii) whether such trends vary across different user segments and (iv) in different neighbourhoods.

Crowdsourcing platforms have become important information providers after disaster events. While they can build on some prior experiences, it is not yet well understood how contributor capacity for such activities is constituted. To what extent are initiatives building a dormant task force that springs to action when it is needed? Alternatively, do they mainly rely on the recruitment of new contributors during disaster events, possibly at the expense of contribution quality? We seek to develop a better understanding of these relationships, using the example of the Humanitarian OpenStreetMap Team. In a large-scale quantitative study, we assess the outcomes of 26 campaigns with almost 20,000 participants. We find that event-centric campaigns can be significant recruiting and reactivation events, however that this is not guaranteed. Our analytical methods provide a means of interpreting key differences in outcomes. We close with recommendations relating to the promotion and coordination of event-centric campaigns in HOT and related platforms.

Crowd-mapping is a form of collaborative work that empowers users to share geographic knowledge. Despite geographic information being intrinsically evolving, little research has so far gone into analysing maintenance practices in these domains. In this paper, we quantitatively capture maintenance dynamics in geographic crowd-sourced datasets, in terms of: the extent to which different maintenance actions are taking place, the type of spatial information that is being maintained, who engages in these practices and where. We apply this method to 117 countries in OpenStreetMap, one of the most successful examples of geographic crowd-sourced datasets. Furthermore, we explore what triggers maintenance, by means of an online survey to which 96 OpenStreetMap contributors took part. Our findings reveal that, although maintenance practices vary substantially from country to country in terms of how widespread they are, strong commonalities exist in terms of what metadata is being maintained, by whom, and what triggers them.

Organisers of large-scale crowdsourcing initiatives need to consider how to produce outcomes with their projects, but also how to build volunteer capacity. The initial project experience of contributors plays an important role in this, particularly when the contribution process requires some degree of expertise. We propose three analytical dimensions to assess first-time contributor engagement based on readily available public data: cohort analysis, task analysis, and observation of contributor performance. We apply these to a large-scale study of remote mapping activities coordinated by the Humanitarian OpenStreetMap Team, a global volunteer effort with thousands of contributors. Our study shows that different coordination practices can have a marked impact on contributor retention, and that complex task designs can be a deterrent for certain contributor groups. We close by providing recommendations about how to build and sustain volunteer capacity in these and comparable crowdsourcing systems.

What is the relationship between urban form and citizens' well-being? In this paper, we propose a quantitative approach to help answer this question, inspired by theories developed within the fields of architecture and population health. The method extracts a rich set of metrics of urban form and well-being from openly accessible datasets. Using linear regression analysis, we identify a model which can explain 30% of the variance of well-being when applied to Greater London, UK. Outcomes of this research can inform the discussion on how to design cities which foster the well-being of their residents.

Sharing economy platforms have become extremely popular in the last few years, and they have changed the way in which we commute, travel, and borrow among many other activities. Despite their popularity among consumers, such companies are poorly regulated. For example, Airbnb, one of the most successful examples of sharing economy platform, is often criticized by regulators and policy makers. While, in theory, municipalities should regulate the emergence of Airbnb through evidence-based policy making, in practice, they engage in a false dichotomy: some municipalities allow the business without imposing any regulation, while others ban it altogether. That is because there is no evidence upon which to draft policies. Here we propose to gather evidence from the Web. After crawling Airbnb data for the entire city of London, we find out where and when Airbnb listings are offered and, by matching such listing information with census and hotel data, we determine the socio-economic conditions of the areas that actually benefit from the hospitality platform. The reality is more nuanced than one would expect, and it has changed over the years. Airbnb demand and offering have changed over time, and traditional regulations have not been able to respond to those changes. That is why, finally, we rely on our data analysis to envision regulations that are responsive to real-time demands, contributing to the emerging idea of ``algorithmic regulation''.

Crowd-sourcing has become a popular form of computer mediated collaborative work and OpenStreetMap represents one of the most successful crowd-sourcing systems, where the goal of building and maintaining an accurate global map of the world is being accomplished by means of contributions made by over 1.2M citizens. However, within this apparently large crowd, a tiny group of highly active users is responsible for the mapping of almost all the content. One may thus wonder to what extent the information being mapped is biased towards the interests and agenda of this group of users. In this paper, we present a method to quantitatively measure content bias in crowd-sourced geographic information. We then apply the method to quantify content bias across a three-year period of OpenStreetMap mapping in 40 countries. We find almost no content bias in terms of what is being mapped, but significant geographic bias; furthermore, we find that bias in terms of meticulousness varies with culture.

Measuring socioeconomic deprivation of cities in an accurate and timely fashion has become a priority for governments around the world, as the massive urbanization process we are witnessing is causing high levels of inequalities which require intervention. Traditionally, deprivation indexes have been derived from census data, which is however very expensive to obtain, and thus acquired only every few years. Alternative computational methods have been proposed in recent years to automatically extract proxies of deprivation at a fine spatio-temporal level of granularity; however, they usually require access to datasets (e.g., call details records) that are not publicly available to governments and agencies. To remedy this, we propose a new method to automatically mine deprivation at a fine level of spatio-temporal granularity that only requires access to freely available user-generated content. More precisely, the method needs access to datasets describing what urban elements are present in the physical environment; examples of such datasets are Foursquare and OpenStreetMap. Using these datasets, we quantitatively describe neighborhoods by means of a metric, called Offering Advantage, that reflects which urban elements are distinctive features of each neighborhood. We then use that metric to (i) build accurate classifiers of urban deprivation and (ii) interpret the outcomes through thematic analysis. We apply the method to three UK urban areas of different scale and elaborate on the results in terms of precision and recall.

Crowd-mapping is a form of collaborative work that empowers citizens to collect and share geographic knowledge. OpenStreetMap (OSM) is a successful example of such paradigm, where the goal of building and maintaining an accurate global map of the changing world is being accomplished by means of local contributions made by over 1.2M citizens. While OSM has been subject to many country-specific studies, the relationship between national culture and economic affluence and users' participation has been so far unexplored. In this work, we systematically study the link between them: we characterise OSM users in terms of who they are, how they contribute, during what period of time, and across what geographic areas. We find strong correlations between these characteristics and national culture factors (e.g., power distance, individualism, pace of life, self expression), and well as Gross Domestic Product per capita. Based on these findings, we discuss design issues that developers of crowd-mapping services should consider to account for cross-cultural differences.

Urban crowd-sourcing has become a popular paradigm to harvest spatial information about our evolving cities directly from citizens. OpenStreetMap is a successful example of such paradigm, with an accuracy of its user-generated content comparable to that of curated databases (e.g., Ordnance Survey). Coverage is however low and most importantly non-uniformly distributed across the city. Being able to model the spontaneous growth of digital information in these domains is required, so to be able to plan interventions aimed at gathering content about areas that would otherwise be neglected. Inspired by models of physical urban growth developed by urban planners, we build a model of digital growth of crowd-sourced spatial information that is both easy to interpret and dynamic, so to be able to determine what factors impact growth and how these change over time. We build and test the model against five years of OpenStreetMap data for the city of London, UK. We then run the model against two other cities, chosen for their different physical and digital growth's characteristics, so to stress-test the model. We conclude with a discussion of the implications of this work on both developers and users of urban crowd-sourcing applications.

Ubiquitous crowd-sourcing has become a popular mechanism to harvest knowledge from the masses. OpenStreetMap (OSM) is a successful example of ubiquitous crowd-sourcing, where citizens volunteer geographic information in order to build and maintain an accurate map of the changing world. Research has shown that OSM information is accurate, by comparing it with centrally maintained spatial information such as Ordnance Survey. However, we find that coverage is low and non uniformly distributed, thus challenging the suitability of ubiquitous crowd-sourcing as a mechanism to map the whole world. In this paper, we investigate what contextual factors correlate with coverage of OSM information in urban settings. We find that, although there is a direct correlation between population density and information coverage, other socio-economic factors also play an important role. We discuss the implications of these findings with respect to the design of urban crowd-sourcing applications.

In the recent years Wikis have become an attractive platform for social studies of the human behaviour. Containing millions records of edits across the globe, collaborative systems such as Wikipedia have allowed researchers to gain a better understanding of editors participation and their activity patterns. However, contributions made to Geo-wikis_wiki-based collaborative mapping projects_ differ from systems such as Wikipedia in a fundamental way due to spatial dimension of the content that limits the contributors to a set of those who posses local knowledge about a specific area and therefore cross-platform studies and comparisons are required to build a comprehensive image of online open collaboration phenomena. In this work, we study the temporal behavioural pattern of OpenStreetMap editors, a successful example of geo-wiki, for two European capital cities. We categorise different type of temporal patterns and report on the historical trend within a period of 7 years of the project age. We also draw a comparison with the previously observed editing activity patterns of Wikipedia.

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this issue, a variety of approaches have been proposed that recommend users what tags to use, both when labeling and when looking for resources. These techniques work well in dense folksonomies, but they fail to do so when tag usage exhibits a power law distribution, as it often happens in real-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy. The proposed metric, which represents the core of our approach, is based on the mutual reinforcement principle. Our experimental evaluation proves that the accuracy and coverage of searches guaranteed by our metric are higher than those achieved by applying classical metrics.

In this paper, we present a multiagent system to support patients in search of healthcare services in an e-health scenario. The proposed system is HL7-aware in that it represents both patient and service information according to the directives of HL7, the information management standard adopted in medical context. Our system builds a profile for each patient and uses it to detect Healthcare Service Providers delivering e-health services potentially capable of satisfying his needs. In order to handle this search it can exploit three different algorithms: the first, called PPB, uses only information stored in the patient profile; the second, called DS-PPB, considers both information stored in the patient profile and similarities among the e-health services delivered by the involved providers; the third, called AB, relies on {\rm A}{\bf^*}, a popular search algorithm in Artificial Intelligence. Our system builds also a social network of patients; once a patient submits a query and retrieves a set of services relevant to him, our system applies a spreading activation technique on this social network to find other patients who may benefit from these services.

Ontologies significantly enhance the possibility to make agent-based Web applications really “semantic”. However, in order to make their usage both effective and efficient, some alignment problems, arising from the presence of semantic heterogeneitie

Social internetworking systems are a significantly emerging new reality; they group together a set of social networks and allow their users to share resources, to acquire opinions and, more in general, to interact, even if these users belong to different social networks and, therefore, did not previously know each other. In this context the notions of trust and reputation play a very relevant role. These notions have been widely studied in the past in several contexts whereas they have been largely neglected in the social internetworking research; however, since this application field presents several peculiarities, the results found in other application contexts are not automatically valid here. This paper introduces a model to represent and handle trust and reputation in a social internetworking system and proposes an approach that exploits these parameters to provide users with suggestions about the most reliable persons they can contact or social networks they can register to.

In this paper, we present a multiagent system aiming at assisting citizens in the current e-government scenario characterized by a huge amount of heterogeneous services that makes it difficult to quickly answer citizen queries. We show that the adoption of intelligent agent technology makes our system capable of helping a citizen in his search of services in such a way as to satisfy his interests and to face his needs; moreover, this technology ensures a high level of proactivity because it can identify services potentially relevant to a citizen even though he has never required them explicitly; finally, it can enhance citizen participation to decisional processes because it can encourage citizens to form communities who can debate in such a way as to propose the activation of new services of interest to them.

In this paper, we propose an Extensible Markup Language (XML)-based multiagent recommender system for supporting online recruitment services. Our system is characterized by the following features: 1) it handles user profiles for personalizing the job search over the Internet; 2) it is based on the intelligent agent technology; and 3) it uses XML for guaranteeing a light, versatile, and standard mechanism for information representation, storing, and exchange. This paper discusses the basic features of the proposed system, presents the results of an experimental study we have carried out for evaluating its performance, and makes a comparison between the proposed system and other e-recruitment systems already presented in the past.