Measuring Similarity in Large-Scale Folksonomies

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. Unlike\ud taxonomies, which overimpose a hierarchical categorisation of content, folksonomies enable end-users to freely create and choose the categories (in this case, tags) that best\ud describe some content. However, as tags are informally de-\ud fined, continually changing, and ungoverned, social tagging\ud has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of\ud users and the noise they introduce. To address this issue, a\ud variety of approaches have been proposed that recommend\ud users what tags to use, both when labelling and when looking for resources. As we illustrate in this paper, real world\ud folksonomies are characterized by power law distributions\ud of tags, over which commonly used similarity metrics, including the Jaccard coefficient and the cosine similarity, fail\ud to compute. We thus propose a novel metric, specifically\ud developed to capture similarity in large-scale folksonomies,\ud that is based on a mutual reinforcement principle: that is,\ud two tags are deemed similar if they have been associated to\ud similar resources, and vice-versa two resources are deemed\ud similar if they have been labelled by similar tags. We offer an efficient realisation of this similarity metric, and assess its quality experimentally, by comparing it against cosine similarity, on three large-scale datasets, namely Bibsonomy, MovieLens and CiteULike

Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure

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.Comment: 6 pages, 2 figures, CIKM 2011: 20th ACM Conference on Information and Knowledge Managemen

Social contribution settings and newcomer retention in humanitarian crowd mapping

Organisers of crowd mapping initiatives seek to identify practices that foster an active contributor community. Theory suggests that social contribution settings can provide important support functions for newcomers, yet to date there are no empirical studies of such an effect. We present the first study that evaluates the relationship between colocated practice and newcomer retention in a crowd mapping community, involving hundreds of first-time participants. We find that certain settings are associated with a significant increase in newcomer retention, as are regular meetings, and a greater mix of experiences among attendees. Factors relating to the setting such as food breaks and technical disruptions have comparatively little impact. We posit that successful social contribution settings serve as an attractor: they provide opportunities to meet enthusiastic contributors, and can capture prospective contributors who have a latent interest in the practice

La historia de la ciencia como herramienta para la enseñanza de física en secundaria : un ejemplo en calor y temperatura

In this work we suggest and analyze some stages of a course on heat and temperature which knowledge of the past of the ideas and the solution provided can enable a better understanding of the content. The hypothesis that guided the planning of activities was that the scientific content studied throughout its history comes closer to the cognitive universe, not only of the student, but of man himself

A global-scale analysis of the sharing economy model – an AirBnB case study

Abstract: The sharing economy model has changed the way in which people engage in a variety of activities, including travelling, trading, working, and lending/borrowing money. Several studies exist that aim to understand, quantify and model such phenomenon, but most such studies are geographically focused on countries in the Western World. Knowledge about the penetration and adoption of this novel market model in non-Western countries is much more limited, and almost completely lacking when it comes to emerging markets, where it was touted to bring the biggest benefits and be a game changer to uplift people economically. To close the gap, we chose Airbnb as an example of sharing economy model with worldwide market penetration, and performed a large-scale quantitative study of its penetration and adoption in seven cities in Asia, five cities in Latin America. We compared findings against seven cities in the Western World, and observed patterns to be similar across all locales, with two notable exceptions: the geographic penetration of such services, and the experience that guests travelling to such destinations shared in their reviews

Nowcasting gentrification using Airbnb data

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

Duration of Untreated Disorder and Cannabis Use: An Observational Study on a Cohort of Young Italian Patients Experiencing Psychotic Experiences and Dissociative Symptoms

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Background: The Duration of Untreated Psychosis (DUP) is the time between the first-episode psychosis (FEP) and the initiation of antipsychotic treatment. It is an important predictor of several disease-related outcomes in psychotic disorders. The aim of this manuscript is investigating the influence of cannabis on the DUP and its clinical correlates. Methods: During years 2014−2019, sixty-two FEP patients with and without cannabis use disorder (CUD) were recruited from several Italian psychiatric hospitals. The subjects were then divided into two groups based on the duration of the DUP and assessed at the beginning of the antipsychotic treatment and after 3 and 6 months, using the Positive and Negative Syndrome Scale (PANSS), the Global Assessment of Functioning (GAF) scale, and the Dissociative Experiences Scale (DES-II). Results: As expected, a longer DUP was associated with worse symptoms and cannabis use did not seem to affect the DUP, but both were related with more dissociative symptoms at onset and over time. Discussion: According to our study, cannabis use can be a predictor of FEP and DUP, and of disease outcome. However, several factors might influence the relationship between cannabis use and DUP. Preventing cannabis use and early diagnosis of psychotic disorders might impact the disease by reducing the persistence of symptoms and limiting dissociative experiences.Peer reviewedFinal Published versio