The Stylometric Processing of Sensory Open Source Data

This research project’s end goal is on the Lone Wolf Terrorist. The project uses an exploratory approach to the self-radicalisation problem by creating a stylistic fingerprint of a person's personality, or self, from subtle characteristics hidden in a person's writing style. It separates the identity of one person from another based on their writing style. It also separates the writings of suicide attackers from ‘normal' bloggers by critical slowing down; a dynamical property used to develop early warning signs of tipping points. It identifies changes in a person's moods, or shifts from one state to another, that might indicate a tipping point for self-radicalisation. Research into authorship identity using personality is a relatively new area in the field of neurolinguistics. There are very few methods that model how an individual's cognitive functions present themselves in writing. Here, we develop a novel algorithm, RPAS, which draws on cognitive functions such as aging, sensory processing, abstract or concrete thinking through referential activity emotional experiences, and a person's internal gender for identity. We use well-known techniques such as Principal Component Analysis, Linear Discriminant Analysis, and the Vector Space Method to cluster multiple anonymous-authored works. Here we use a new approach, using seriation with noise to separate subtle features in individuals. We conduct time series analysis using modified variants of 1-lag autocorrelation and the coefficient of skewness, two statistical metrics that change near a tipping point, to track serious life events in an individual through cognitive linguistic markers. In our journey of discovery, we uncover secrets about the Elizabethan playwrights hidden for over 400 years. We uncover markers for depression and anxiety in modern-day writers and identify linguistic cues for Alzheimer's disease much earlier than other studies using sensory processing. In using these techniques on the Lone Wolf, we can separate their writing style used before their attacks that differs from other writing

The Identification of Authors using Cross Document Co-Referencing

One of the major problems facing the Australian war-fighter is the change from conventional, nation-state conflict to that of asymmetric warfare. In this type of warfare, insurgents dress and appear the same as the civilian population and improvised explosive devices (IEDs) are used in place of conventional weaponry. In order to support the war fighter, identifying the networks of the insurgents and their supporters becomes important. Author identification methods are used to identify insurgents and their supporters from masses of data (reports, web sites, etc.). This research project applies Neuro-Linguistic Programming techniques to extract key word phrase and gender-based pronouns for author identification. Results demonstrate that this technique has merit in the identification of particular authors through analysis of their specific use of key language elements in their publications. Future research will explore the automation of this technique. Three experiments were conducted using logistic regression, disciminant analysis, and exploratory and confirmatory factor analysis across the 30 observations in the sample. In the first experiment logistic regression was used to test the gender of an author through their use of pronouns and found that it might be possible to determine gender based on the use of the words ‘my’, ‘her’, and ‘its’. In the second experiment, discriminant analysis was used to test the sensory-based style of an author through their use of predicates and while unable to identify any Preferred Representational System, it was possible to identify Representational Systems within an author’s work. In the third experiment,, exploratory and confirmatory factor analysis was used to see if any underlying factors that might have influenced the observed results could be identified. While there were not enough observations in each of the categories to identify any underlying factors, overall the results were sufficient to develop two rule-based algorithms and identify an author by their sensory-based style and gender

Detecting Extreme Ideologies in Shifting Landscapes: an Automatic & Context-Agnostic Approach

In democratic countries, the ideology landscape is foundational to individual and collective political action; conversely, fringe ideology drives Ideologically Motivated Violent Extremism (IMVE). Therefore, quantifying ideology is a crucial first step to an ocean of downstream problems, such as; understanding and countering IMVE, detecting and intervening in disinformation campaigns, and broader empirical opinion dynamics modeling. However, online ideology detection faces two significant hindrances. Firstly, the ground truth that forms the basis for ideology detection is often prohibitively labor-intensive for practitioners to collect, requires access to domain experts and is specific to the context of its collection (i.e., time, location, and platform). Secondly, to circumvent this expense, researchers generate ground truth via other ideological signals (like hashtags used or politicians followed). However, the bias this introduces has not been quantified and often still requires expert intervention. This work presents an end-to-end ideology detection pipeline applicable to large-scale datasets. We construct context-agnostic and automatic ideological signals from widely available media slant data; show the derived pipeline is performant, compared to pipelines of common ideology signals and state-of-the-art baselines; employ the pipeline for left-right ideology, and (the more concerning) detection of extreme ideologies; generate psychosocial profiles of the inferred ideological groups; and, generate insights into their morality and preoccupations

Using Shakespeare's Sotto Voce to Determine True Identity From Text

Little is known of the private life of William Shakespeare, but he is famous for his collection of plays and poems, even though many of the works attributed to him were published anonymously. Determining the identity of Shakespeare has fascinated scholars for 400 years, and four significant figures in English literary history have been suggested as likely alternatives to Shakespeare for some disputed works: Bacon, de Vere, Stanley, and Marlowe. A myriad of computational and statistical tools and techniques have been used to determine the true authorship of his works. Many of these techniques rely on basic statistical correlations, word counts, collocated word groups, or keyword density, but no one method has been decided on. We suggest that an alternative technique that uses word semantics to draw on personality can provide an accurate profile of a person. To test this claim, we analyse the works of Shakespeare, Christopher Marlowe, and Elizabeth Cary. We use Word Accumulation Curves, Hierarchical Clustering overlays, Principal Component Analysis, and Linear Discriminant Analysis techniques in combination with RPAS, a multi-faceted text analysis approach that draws on a writer's personality, or self to identify subtle characteristics within a person's writing style. Here we find that RPAS can separate the known authored works of Shakespeare from Marlowe and Cary. Further, it separates their contested works, works suspected of being written by others. While few authorship identification techniques identify self from the way a person writes, we demonstrate that these stylistic characteristics are as applicable 400 years ago as they are today and have the potential to be used within cyberspace for law enforcement purposes

Table1.DOCX

<p>Little is known of the private life of William Shakespeare, but he is famous for his collection of plays and poems, even though many of the works attributed to him were published anonymously. Determining the identity of Shakespeare has fascinated scholars for 400 years, and four significant figures in English literary history have been suggested as likely alternatives to Shakespeare for some disputed works: Bacon, de Vere, Stanley, and Marlowe. A myriad of computational and statistical tools and techniques have been used to determine the true authorship of his works. Many of these techniques rely on basic statistical correlations, word counts, collocated word groups, or keyword density, but no one method has been decided on. We suggest that an alternative technique that uses word semantics to draw on personality can provide an accurate profile of a person. To test this claim, we analyse the works of Shakespeare, Christopher Marlowe, and Elizabeth Cary. We use Word Accumulation Curves, Hierarchical Clustering overlays, Principal Component Analysis, and Linear Discriminant Analysis techniques in combination with RPAS, a multi-faceted text analysis approach that draws on a writer's personality, or self to identify subtle characteristics within a person's writing style. Here we find that RPAS can separate the known authored works of Shakespeare from Marlowe and Cary. Further, it separates their contested works, works suspected of being written by others. While few authorship identification techniques identify self from the way a person writes, we demonstrate that these stylistic characteristics are as applicable 400 years ago as they are today and have the potential to be used within cyberspace for law enforcement purposes.</p

Parsing Science - Uncovering Uncertain Identities

<a rel="noopener" target="_blank">David Kernot</a> from the <a href="http://www.anu.edu.au/" rel="noopener" target="_blank">Australian National University</a> talks with us about his research that uses the writings of William Shakespeare for fine-tuning an algorithm that determines an author’s identity from their text. His article “<a href="https://www.frontiersin.org/articles/10.3389/fpsyg.2018.00289/full" rel="noopener" target="_blank">Using Shakespeare’s Sotto Voce to Determine True Identity From Text</a>” was published in <a href="https://www.frontiersin.org/journals/psychology" rel="noopener" target="_blank"><em>Frontiers in Psychology</em></a> on March 15, 2018, co-authored with <a href="https://bjbs.csu.edu.au/schools/computing-and-mathematics/staff/profiles/professorial-staff/terry-bossomaier" rel="noopener" target="_blank">Terry Bossomaier</a> and <a href="https://crawford.anu.edu.au/people/academic/roger-bradbury" rel="noopener" target="_blank">Roger Bradbury</a>.<div><br></div><div>https://www.parsingscience.org/2018/05/15/david-kernot/<br></div

Tower of Santa Catalina [Material gráfico] : Valencia

Resumen: Descripción: vista de la torre de Santa CatalinaPlancha de acer

Table2.DOCX

<p>Little is known of the private life of William Shakespeare, but he is famous for his collection of plays and poems, even though many of the works attributed to him were published anonymously. Determining the identity of Shakespeare has fascinated scholars for 400 years, and four significant figures in English literary history have been suggested as likely alternatives to Shakespeare for some disputed works: Bacon, de Vere, Stanley, and Marlowe. A myriad of computational and statistical tools and techniques have been used to determine the true authorship of his works. Many of these techniques rely on basic statistical correlations, word counts, collocated word groups, or keyword density, but no one method has been decided on. We suggest that an alternative technique that uses word semantics to draw on personality can provide an accurate profile of a person. To test this claim, we analyse the works of Shakespeare, Christopher Marlowe, and Elizabeth Cary. We use Word Accumulation Curves, Hierarchical Clustering overlays, Principal Component Analysis, and Linear Discriminant Analysis techniques in combination with RPAS, a multi-faceted text analysis approach that draws on a writer's personality, or self to identify subtle characteristics within a person's writing style. Here we find that RPAS can separate the known authored works of Shakespeare from Marlowe and Cary. Further, it separates their contested works, works suspected of being written by others. While few authorship identification techniques identify self from the way a person writes, we demonstrate that these stylistic characteristics are as applicable 400 years ago as they are today and have the potential to be used within cyberspace for law enforcement purposes.</p