The analysis of authorial style, termed stylometry, assumes that style is quantifiably measurable for evaluation of distinctive qualities. Stylometry research has yielded several methods and tools over the past 200 years to handle a variety of challenging cases. This survey reviews several articles within five prominent subtasks: authorship attribution, authorship verification, authorship profiling, stylochronometry, and adversarial stylometry. Discussions on datasets, features, experimental techniques, and recent approaches are provided. Further, a current research challenge lies in the inability of authorship analysis techniques to scale to a large number of authors with few text samples. Here, we perform an extensive performance analysis on a corpus of 1,000 authors to investigate authorship attribution, verification, and clustering using 14 algorithms from the literature. Finally, several remaining research challenges are discussed, along with descriptions of various open-source and commercial software that may be useful for stylometry subtasks.

The Journal of Pattern Recognition Research (JPRR) provides an open access forum for the publication of research articles in areas of pattern recognition, machine learning, artificial intelligence, computational algorithms, and fuzzy learning

Artificial intelligence-enabled deception detection is an emerging tool for identifying dishonest behavior in a wide range of applications, from security and forensics to politics and lower-risk everyday interactions, addressing the pressing need for enhanced trust and security in an increasingly digital and interconnected world. However, to date, approaches to achieve deception detection with AI have not been evaluated by application area, leaving a disconnect between approaches leveraged and their potential real-world use. Thus, this paper provides a systematic review of application areas for AI-enabled deception detection approaches following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Specifically, we discuss 93 articles in detail, (1) identifying common applications of automated deception detection in the literature, (2) enumerating deception detection approaches by application area, and (3) describing publicly available datasets per application area. We also identify open challenges, such as the lack of datasets that support cross-domain deception detection research. By focusing on the application areas of automated deception detection, this review helps to contextualize the surveyed literature and understand the specific challenges and requirements associated with different domains. Further, by examining various application areas, researchers can tailor their approaches and techniques to address the unique characteristics and constraints of each domain. This targeted approach increases the practical relevance and applicability of deception detection methods in real-world scenarios.

In this paper, we explore the soft biometric classification of 13 demographic and behavioral attributes using phone data collected from 46 subjects. We utilize the results of this analysis to further evaluate reduced search spaces for the primary identification task by implementing a ranking formula which quantifies the performance of each attribute considering the predictability of some attributes compared to others. Results show that soft biometric classification is feasible with up to 90% accuracy; however, because people exhibit high intra-class variance, templates and queries are significantly affected in terms of how well they match, even with reduced search spaces. We analyze these findings using a combination of approaches, including an evaluation of the biometric menagerie, visualizing the distribution of the data, and observing how subjects vary in their soft biometric class across different times of the day.

Patterns in the use of mobile devices have the potential to be used as a behavioral biometric for identification of the device user. We explore the distinctiveness and permanence of application, Bluetooth, and Wi-Fi mobile device usage data. Our database consists of data from two hundred mobile phone users collected over a 19-month span. To our knowledge, this is one of the largest databases of its kind. Results of over 500 experiments indicate that user identification rates averaging 80%, 77%, 93%, and 85% are achievable when using application, Bluetooth, Wi-Fi, and the combination of these three types of behavioral features, respectively.

According to data from the U.S. Center for Disease Control and Prevention, as of June 2020, a significant number of African Americans had been infected with the coronavirus disease, experiencing disproportionately higher death rates compared to other demographic groups. These disparities highlight the urgent need to examine the experiences, behaviors, and opinions of the African American population in relation to the COVID-19 pandemic. By understanding their unique challenges in navigating matters of health and well-being, we can work towards promoting health equity, eliminating disparities, and addressing persistent barriers to care. Since Twitter data has shown significant promise as a representation of human behavior and for opinion mining, this study leverages Twitter data published in 2020 to characterize the pandemic-related experiences of the United States’ African American population using aspect-based sentiment analysis. Sentiment analysis is a common task in natural language processing that identifies the emotional tone (i.e., positive, negative, or neutral) of a text sample. Aspect-based sentiment analysis increases the granularity of sentiment analysis by also extracting the aspect for which sentiment is expressed. We developed a machine learning pipeline consisting of image and language-based classification models to filter out tweets not related to COVID-19 and those unlikely published by African American Twitter subscribers, leading to an analysis of nearly 4 million tweets. Overall, our results show that the majority of tweets had a negative tone, and that the days with larger numbers of published tweets often coincided with major U.S. events related to the pandemic as suggested by major news headlines (e.g., vaccine rollout). We also show how word usage evolved throughout the year (e.g.,outbreaktopandemicandcoronavirustocovid). This work also points to important issues like food insecurity and vaccine hesitation, along with exposing semantic relationships between words, such ascovidandexhausted. As such, this work furthers understanding of how the nationwide progression of the pandemic may have impacted the narratives of African American Twitter users.