Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Empirically derived ability-achievement subtypes in a heterogeneous clinic-referred sample.

The purpose of the present study was to identify clinically meaningful and reliable patterns of ability and achievement using the WISC-III and WIAT. As an extension of the work of Saunders, Casey, and Jones (2001), it was anticipated that several of the derived subtypes would share a similar profile to many of the subtypes described in their research, and that many of the derived subtypes would demonstrate a predictable pattern of neuropsychological test results. Cluster analysis was used to group the 182 WISC-III and WIAT profiles (10 WISC-III subtests and 4 WIAT subtests) of children between the ages of 9 and 14 years. Theoretical and empirical considerations were used to identify a cluster solution, which involved comparison of several five-, six- and eight cluster solutions. Ultimately, a five-cluster solution was selected as being representative of the data, which was well-replicated across three hierarchical clustering methods (i.e., complete linkage, average linkage-within groups, and average linkage-between groups (UPGMA)). The clusters were labeled based on their most salient characteristics, which included a group of predominantly Low Ability and achievement, a group demonstrating a pattern of verbal processing deficits, a group demonstrating a pattern of visual spatial/processing speed deficits, and a group with deficits consistent with an ACID pattern. Three of the subtypes were found to be highly similar to subtypes of Saunders et al., and all five subtypes had been identified in the learning disabilities literature. The external validity of the five subtypes was assessed through evaluation of the relationship between cluster membership and neuropsychological profile. Most predictions regarding neuropsychological performance were supported by the data, providing further evidence of the validity of the five-cluster solution. Clinical implications of the ability-achievement typology and suggestions for future research are discussed.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .W39. Source: Dissertation Abstracts International, Volume: 65-07, Section: B, page: 3732. Adviser: Joseph Casey. Thesis (Ph.D.)--University of Windsor (Canada), 2004

COMPARING PERSONALITY DISORDER MODELS: FFM AND DSM-IV-TR

The current edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR; American Psychiatric Association, 2000) defines personality disorders as categorical entities that are distinct from themselves and from normal personality traits. However, many scientists now believe that personality disorders can best be conceptualized using a dimensional model of traits that span normal and abnormal personality, such as the Five-Factor Model (FFM). Many research studies have indicated that the current personality disorder system can be adequately conceptualized using the FFM. However, if the FFM or any dimensional model is to be considered as a credible alternative to the current model, it must first demonstrate an increment in the validity of the assessment offered within a clinical setting. Thus, the current study extended previous research by comparing the convergent and discriminant validity of the current DSM-IV-TR model to the FFM across four assessment methodologies. Eighty-eight individuals that were currently receiving ongoing psychotherapy were assessed for the FFM and the DSM-IV-TR personality disorders using self-report, informant report, structured interview, and therapist ratings. The results indicated that the FFM had an appreciable advantage over the DSM-IV-TR in terms of discriminant validity and, at the domain level, convergent validity. Implications of the findings for future research are discussed

Putting the person first : an examination of thought disorder and personality heterogeneity in schizophrenia.

The Recovery Model of mental illness, emerging as the new zeitgeist in regards to treatment, emphasizes the optimization of functioning for each individual, using personal strengths and preferences to drive the recovery process. Thought disorder has long been considered a core symptom of schizophrenia and has been implicated in multiple domains of functional outcome. In spite of its relationship to functioning and substantial heterogeneity of the phenomenon, little to no research has examined potential factors which may be related to these differences in thought disorder and its related domains of functioning. The current study proposes that “normal” personality traits, such as those captured by the widely accepted Five-Factor Model (FFM), may be of particular utility in understanding the differences among individuals with schizophrenia, consistent with the Recovery Model’s attention to individual differences. This dissertation specifically explores the relationship between personality and thought disorder in schizophrenia. Participants in the study were assessed for thought disorder and personality via the Thought Disorder Index and the Big Five Inventory, respectively. It was hypothesized that 1) personality would be related to the severity of thought disorder, and 2) personality would be related to the characteristics of thought disorder observed. It was also hypothesized that significant personality differences within the sample would emerge. Hypotheses were partially supported. Three clusters with significant personality differences emerged within the sample. While personality and the severity of thought disorder were not related, personality was related to the quality of thought disorder. Results suggest that personality may be related to the heterogeneity of thought disorder within the schizophrenia population. Additionally, results indicate those with a diagnosis of schizophrenia demonstrate distinct personalities which distinguish them as individuals, may be relevant to functional outcome, and inform intervention

Kinetic Intersections as a Source of Information on Rate Coefficients

C

Object Detection and Classification in the Visible and Infrared Spectrums

The over-arching theme of this dissertation is the development of automated detection and/or classification systems for challenging infrared scenarios. The six works presented herein can be categorized into four problem scenarios. In the first scenario, long-distance detection and classification of vehicles in thermal imagery, a custom convolutional network architecture is proposed for small thermal target detection. For the second scenario, thermal face landmark detection and thermal cross-spectral face verification, a publicly-available visible and thermal face dataset is introduced, along with benchmark results for several landmark detection and face verification algorithms. Furthermore, a novel visible-to-thermal transfer learning algorithm for face landmark detection is presented. The third scenario addresses near-infrared cross-spectral periocular recognition with a coupled conditional generative adversarial network guided by auxiliary synthetic loss functions. Finally, a deep sparse feature selection and fusion is proposed to detect the presence of textured contact lenses prior to near-infrared iris recognition