Sporting events attract high volumes of people, which in turn leads to increased use of social media. In addition, research shows that sporting events may trigger violent behavior that can lead to crime. This study analyses the spatial relationships between crime occurrences, demographic, socio-economic and environmental variables, together with geo-located Twitter messages and their 'violent' subsets. The analysis compares basketball and hockey game days and non-game days. Moreover, this research aims to analyze crime prediction models using historical crime data as a basis and then introducing tweets and additional variables in their role as covariates of crime. First, this study investigates the spatial distribution of and correlation between crime and tweets during the same temporal periods. Feature selection models are applied in order to identify the best explanatory variables. Then, we apply localized kernel density estimation model for crime prediction during basketball and hockey games, and on non-game days. Findings from this study show that Twitter data, and a subset of violent tweets, are useful in building prediction models for the seven investigated crime types for home and away sporting events, and non-game days, with different levels of improvement.

Increasing globalization of the world leads to an emerging need for ways to analysis and understand groups from different cultures and ideologies. Researchers have used written text as a medium to examine political discourse and analyze value-motivated groups. Previous works showed that computational linguistic analysis can be performed to infer the flexibility of value-motivated groups from their writings. The main premise of these works is that text can bring insights into individuals' and groups' way of thinking, and potentially, behaviour. While existing works provide viable solutions for characterizing groups' ideological behaviour, they perform their analyses over all text published by the groups. However, researchers have found that religious and value-motivated groups can't be analyzed collectively as they regularly evolve. To address this gap, we analyze the performance of existing methods to single documents. Experimental results show that previous features (e.g., use of pronouns and judgment statements) used to predict groups' flexibility are less predictive for single documents' flexibility. We show that a newly added feature regarding the identity of a group provides a significant contribution to the prediction process. Furthermore, due to the unbalanced nature of our data, we propose a weighting scheme for linear regression based on the inter-group variance. Results indicate that a weighted least squares significantly outperforms a traditional least squares approach. This work brings new insights into the characteristics of different linguistic and performative signals, and their relationship to the linguistic flexibility of groups. It also provides a decision making support tool for practical use by practitioners.

Motivated by the findings in social science that people's opinions are diverse and variable while together they are shaped by evolving social norms, we perform personalized sentiment classification via shared model adaptation over time.In our proposed solution, a global sentiment model is constantly updated to capture the homogeneity in which users express opinions, while personalized models are simultaneously adapted from the global model to recognize the heterogeneity of opinions from individuals.Global model sharing alleviates data sparsity issue, and individualized model adaptation enables efficient online model learning.Extensive experimentations are performed on two large review collections from Amazon and Yelp, and encouraging performance gain is achieved against several state-of-the-art transfer learning and multi-task learning based sentiment classification solutions.

Kernel density estimation is a popular method for identifying crime hotspots for the purpose of data-driven policing. However, computing a kernel density estimate is computationally intensive for large crime datasets, and the quality of the resulting estimate depends heavily on parameters that are difficult to set manually. Inspired by methods from image processing, we propose a novel way for performing hotspot analysis using localized kernel density estimation optimized with an evolutionary algorithm. The proposed method uses local learning to address three challenges associated with traditional kernel density estimation: computational complexity, bandwidth selection, and kernel function selection. We evaluate our localized kernel model on 17 crime types from Chicago, Illinois, USA. Preliminary results indicate significant improvement in prediction performance over the traditional approach. We also examine the effect of data sparseness on the performance of both models.

The convergence of public data and statistical modeling has created opportunities for public safety officials to prioritize the deployment of scarce resources on the basis of predicted crime patterns. Current crime prediction methods are trained using observed crime and information describing various criminogenic factors. Researchers have favored global models (e.g., of entire cities) due to a lack of observations at finer resolutions (e.g., ZIP codes). These global models and their assumptions are at odds with evidence that the relationship between crime and criminogenic factors is not homogeneous across space. In response to this gap, we present area-specific crime prediction models based on hierarchical and multi-task statistical learning. Our models mitigate sparseness by sharing information across ZIP codes, yet they retain the advantages of localized models in addressing non-homogeneous crime patterns. Out-of-sample testing on real crime data indicates predictive advantages over multiple state-of-the-art global models.

Prior work in statistical crime prediction has not investigated micro-level movement patterns of individuals in the area of interest. Geotagged social media implicitly describe these patterns for many individuals; however, methods of extracting such patterns and integrating them into a statistical model remain undeveloped. This paper presents methods and experiments that begin to fill this gap. We investigate the use of spatiotemporally tagged Twitter posts for inferring micro-level movement patterns, and we use real crime data to develop and test a model informed by such patterns. Our results indicate improved performance for 15 of the 20 crime types studied, when comparing our model with a baseline that does not use micro-level movement patterns.

Ontologies have been widely used as a knowledge representation framework, and numerous methods have been put forth to match ontologies. It is well known that ontology matchers behave differently in various domains, and it is a challenge to predict or characterize their behavior. Herein, a hybrid expertise-agreement aggregation strategy is proposed. Although others rely on the existence of a reference ontology, this typically does not exist in the real world. In this article, the fuzzy integral (FI) is used to aggregate multiple ontology matchers in lieu of a reference ontology. Specifically, we present a measure of expertise and fuse it with our previous agreement measure that is motivated by crowd sourcing to improve recall. This way, any available domain knowledge, in terms of partial ordering of a subset of inputs, can be included in the decision-making process. By adding the domain knowledge to the agreement model, we are able to reach the upmost performance. Preliminary results demonstrate the robustness of our approach across domains. Sensitivity analysis is also provided, which shows the limits to which extreme destructive expertise affects system performance.