Spatio-semantic user profiles in location-based social networks

Knowledge of users’ visits to places is one of the keys to understanding their interest in places. User-contributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users’ association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets

Exploring a capability-demand interaction model for inclusive design evaluation

Designers are required to evaluate their designs against the needs and capabilities of their target user groups in order to achieve successful, inclusive products. This dissertation presents exploratory research into the specific problem of supporting analytical design evaluation for Inclusive Design. The analytical evaluation process involves evaluating products with user data rather than testing with actual users. The work focuses on the exploration of a capability-demand model of product interaction as the basis for analytical inclusive evaluation. This model suggests that by comparing the measured sensory, cognitive and motor capabilities of a user population to the corresponding product demands, the degree of fit between users and products can be assessed. The research problem was addressed by firstly examining theories of human function and performance together with existing sources of user capability data. It was found that user capability data was fragmented and lacking in terms of predicting design exclusion and difficulty at the population level. More fundamentally, however, it was found that the relationships between measured capability in populations with low functional capacity and real world task performance with products (such as errors, times and difficulty) were not well understood. Given that an understanding of these relationships are necessary to guide capability data collection and to drive valid and robust analytical evaluation methods, the research effort focused on exploring these relationships via empirical and analytical studies. The research process culminated in an experimental study with nineteen users of various functional capability profiles performing tasks with four consumer products (a clock radio, a mobile phone, a blender and a vacuum cleaner). Measures of user capability were related to corresponding product demands (on those capabilities) and task outcome measures. A complex picture emerged, where linear relationships did not generally account for significant variance in task outcome measures. Further, it appeared that multiple capabilities were possibly interacting in unknown ways to support real world interaction. These indicative results point to the further investigation of multivariate and non-linear models for describing capability-demand relationships, and also the replication of similar studies with larger sample sizes to confirm the relationships observed. The resulting overall recommendation, therefore, is that there is a need to direct research efforts in this critical but largely unexplored area of capability-demand model building for Inclusive Design evaluation

Modeling Substance Use and Mental Disorder Comorbidity Using Latent Variable and Network Approaches

Introduction. Substance use disorder (SUD) is a common condition that affects millions of Americans. Addressing SUD has been complicated by comorbid mental disorders and co-occurring substance use. Consequently, detailing and addressing SUD and comorbid SUD represent an important goal to improve the health of Americans. Objective. The research goal of this dissertation was to characterize the comorbidity between substance use, including tobacco use, and mental disorder symptoms measured as negative affect and externalizing symptoms in a population-based sample using latent variable and network approaches. Methods. Waves 1 – 3 from the Population Assessment of Tobacco and Health Study were used. Various statistical analyses were used to complete each project including multinomial and ordinal regression, latent class analysis, cumulative ROC curve analysis, and network analysis. Results. The associations between psychopathology (negative affect vs. externalizing severity) varied by different substance use combinations. Both latent class analysis and network analysis results identified relationships between (1) exclusive cigarette, dual cigarette and e-cigarette, marijuana, and PDNP with negative affect symptoms, and (2) alcohol with externalizing symptoms. The comorbidity structure remained stable with transition to lower severity groups but identification of stronger connections across three data points. Conclusions. This dissertation identified specific combinations of substance use behaviors and mental disorder symptoms, determined which sociodemographic factors play a role in specific comorbidity profiles, and assessed the patterns of comorbidity among three waves of data. The results can inform robust and targeted prevention strategies to effectively mitigate the substantial burden and societal costs of comorbidity in the U.S. population

Modeling relationship strength in online social networks.

ABSTRACT Previous work analyzing social networks has mainly focused on binary friendship relations. However, in online social networks the low cost of link formation can lead to networks with heterogeneous relationship strengths (e.g., acquaintances and best friends mixed together). In this case, the binary friendship indicator provides only a coarse representation of relationship information. In this work, we develop an unsupervised model to estimate relationship strength from interaction activity (e.g., communication, tagging) and user similarity. More specifically, we formulate a link-based latent variable model, along with a coordinate ascent optimization procedure for the inference. We evaluate our approach on real-world data from Facebook and LinkedIn, showing that the estimated link weights result in higher autocorrelation and lead to improved classification accuracy