Female characters and references to femininity throughout American war literature disrupt discursive and biological divisions of the masculine and feminine. In examining gender and war literature over the twentieth century, I propose an alternative genealogy of American war literature in which narratives since the end of the nineteenth century initiate two related patterns of gender representation that Vietnam War literature dramatically expands: they critique aggression, camaraderie, and heroism, rejecting these traditional sites of masculinity through desertion narratives, and they harness sentimentality, domesticity, motherhood, and penetration, embracing these traditional sites of femininity in ways that disrupt gender norms. By examining these sites of cross-gender identification through psychoanalytic, rhetorical, and feminist methods, I argue that narratives by Stephen Crane, Ernest Hemingway, Kurt Vonnegut, Tim O'Brien, Stephen Wright, and Larry Heinemann reveal the power of contemporary redefinitions of gender by absorbing feminist discourse into the performance of masculinity.

Author profiling is a field within Natural Language Processing (NLP) that is concerned with identifying various characteristics and demographic factors of authors, such as gender, age, location, native language, political orientation, and personality by analyzing the style and content of their writings. There is a growing interest in author profiling, with applications in marketing and advertising, opinion mining, personalization, recommendation systems, forensics, security, and defense. In this work, we build several classification models using NLP, Deep Learning, and classical Machine Learning techniques that can identify the gender and age of a Twitter user based on the textual contents of their correspondence (tweets) on the platform. Our SVM gender classifier utilizes a combination of word and character n-grams as features, dimensionality reduction using Latent Semantic Analysis (LSA), and a Support Vector Machine (SVM) classifier with linear kernel. At the PAN 2018 author profiling shared task, this model achieved the highest performance with 82.21%, 82.00%, and 80.90% accuracy on the English, Spanish, and Arabic datasets, respectively. Our age classifier was trained on a dataset of 11,160 Twitter users, using the same approach, though the age classification experiments are preliminary. Our Deep Learning gender classifiers are trained and tested on English datasets. Our feedforward neural network consisting of a word embedding layer, flattening, and two densely-connected layers achieves 79.57% accuracy, and our bidirectional Long Short-Term Memory (LSTM) neural network achieves 76.85% accuracy on the gender classification task.

Acoustic analysis of animal vocalizations has been widely used to identify the presence of individual species, classify vocalizations, identify individuals, and determine gender. In this work automatic identification of speaker and gender of mice from ultrasonic vocalizations and speaker identification of meerkats from their Close calls is investigated. Feature extraction was implemented using Greenwood Function Cepstral Coefficients (GFCC), designed exclusively for extracting features from animal vocalizations. Mice ultrasonic vocalizations were analyzed using Gaussian Mixture Models (GMM) which yielded an accuracy of 78.3% for speaker identification and 93.2% for gender identification. Meerkat speaker identification with Close calls was implemented using Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM), with an accuracy of 90.8% and 94.4% respectively. The results obtained shows these methods indicate the presence of gender and identity information in vocalizations and support the possibility of robust gender identification and individual identification using bioacoustic data sets.

Η εντυπωσιακή εξάπλωση των μέσων κοινωνικής δικτύωσης τα τελευταία χρόνια, θέτει βασικά ζητήματα τα οποία απασχολούν την ερευνητική κοινότητα. Η συγκέντρωση και οργάνωση του τεράστιου όγκου πληροφορίας βάσει θέματος, συγγραφέα, ηλικίας ή και φύλου αποτελούν χαρακτηριστικά παραδείγματα προβλημάτων που πρέπει να αντιμετωπιστούν. Η συσσώρευση παρόμοιας πληροφορίας από τα ψηφιακά ίχνη που αφήνει ο κάθε χρήστης καθώς διατυπώνει τη γνώμη του για διάφορα θέματα ή περιγράφει στιγμιότυπα από τη ζωή του δημιουργεί τάσεις, οι οποίες εξαπλώνονται ταχύτατα μέσω των tweets, των δημοσιευμάτων σε ιστολόγια (blogs) και των αναρτήσεων στο Facebook. Ιδιαίτερο ενδιαφέρον παρουσιάζει το πώς μπορεί όλη αυτή η πληροφορία να κατηγοριοποιηθεί βάσει δημογραφικών χαρακτηριστικών, όπως το φύλο ή η ηλικία. Άμεσες πληροφορίες που παρέχει ο κάθε χρήστης για τον εαυτό του, όπως επίσης και έμμεσες πληροφορίες που μπορούν να προκύψουν από την γλωσσολογική ανάλυση των κειμένων του χρήστη, αποτελούν σημαντικά δεδομένα που μπορούν να χρησιμοποιηθούν για την ανίχνευση του φύλου του συγγραφέα. Πιο συγκεκριμένα, η αναγνώριση του φύλου ενός χρήστη από δεδομένα κειμένου, μπορεί να αναχθεί σε ένα πρόβλημα κατηγοριοποίησης κειμένου. Το κείμενο υφίσταται επεξεργασία και στη συνέχεια, με τη χρήση μηχανικής μάθησης, εντοπίζεται το φύλο. Ειδικότερα, μέσω στατιστικής και γλωσσολογικής ανάλυσης των κειμένων, εξάγονται διάφορα χαρακτηριστικά (π.χ. συχνότητα εμφάνισης λέξεων, μέρη του λόγου, μήκος λέξεων, χαρακτηριστικά που συνδέονται με το περιεχόμενο κ.τ.λ.), τα οποία στη συνέχεια χρησιμοποιούνται για να γίνει η αναγνώριση του φύλου. Στην παρούσα διπλωματική εργασία σκοπός είναι η μελέτη και η ανάπτυξη ενός συστήματος κατηγοριοποίησης κειμένων ιστολογίου και ιστοσελίδων κοινωνικής δικτύωσης, βάσει του φύλου. Εξετάζεται η απόδοση διαφορετικών συνδυασμών χαρακτηριστικών και κατηγοριοποιητών στoν εντοπισμό του φύλου. / The rapid growth of social media in recent years creates important research tasks. The collection and management of the huge information available, based on topic, author, age or gender are some examples of the problems that need to be addressed. The gathering of such information from the digital traces of the users, when they express their opinions on different subjects or they describe moments of their lives, creates trends, which expand through tweets, blog posts and Facebook statuses. An interesting aspect is to classify all the available information, according to demographic characteristics, such as gender or age. The direct clues provided by the users about themselves, along with the indirect information that can come of the linguistic analysis of their texts, are useful elements that can be used for the identification of the authors’ gender. More specifically, the detection of the users’ gender from textual data can be faced as a document classification problem. The document is processed and then, machine learning techniques are applied, in order to detect the gender. The features used for the gender identification can be extracted from statistical and linguistic analysis of the document. In the present thesis, we aim to develop an automatic system for the classification of web blog and social media posts, according to their authors’ gender. We study the performance of different combinations of features and classifiers for the identification of the gender.

The purpose of this project was to present continuing medical education providers with a handbook that presents current perspectives on women's learning and suggests practice guidelines that can be incorporated into the planning of existing and future medical education activities.

This thesis addresses the rational properties of women’s gender identification and self-representation from political theorist Lois McNay’s generative logics, employing the Praedia of Julia Felix, Pompeii, as a case study. Previous debates rooted in semantics and representationalism have focused on non-elite stereotypes or negative gendered dichotomies fostered by comprehensive views on Roman women’s exclusion from public life. In contrast, this thesis adopts a new materialist approach, specifically drawing on feminist theorist Karen Barad’s agential realist method building on intra-activity in order to shed new light on the well-studied subject of how a non-elite woman was able to carve out an existence for herself in patriarchal ancient Roman society. Whereas past research has labelled non-elite Roman women as both passive and unproductive individuals, the present thesis proposes that agencies functioned as lived experiences which determined individuals’ abilities to actively connect with things and surroundings in different ways. Therefore, in order to analyze the interceding effects of agencies on gender identification and self-representation in connection to the Praedia of Julia Felix I argue that a broader view of performativity, embodying processes and materiality is needed. This view calls for a reconceptualization of relational entanglements in which material and social worlds are seen as mutually interconnected rather than separate entities isolated from the bodies responsible for creating these settings. Presenting the results based on a combined analysis of generative and new materialist models, I suggest that the Praedia of Julia Felix demonstrates a non-elite woman’s active participation in creating personal sustainability. This dynamic interplay between Julia Felix and her social surroundings in worth understanding in detail.

[EN] The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language or personality traits, simply by analysing her texts, opens a wide range of possibilities from the point of view of forensics, security and marketing. Furthermore, social media proliferation, which allows for new communication models and human relations, strengthens this wide range of possibilities to bounds never seen before. Idiosyncrasy inherent to social media makes them a special environment of communication, where freedom of expression, informality and spontaneous generation of topics and trends, enhances the knowledge of the daily reality of people in their use of language. However, the same idiosyncrasy makes difficult, or extremely costly, the application of linguistic techniques. In this work we have proposed EmoGraph, a graph-based approach with the aim at modelling the way that users express their emotions, and the way they include them in their discourse, bearing in mind not only their frequency of occurrence, but also their position and relationship with other elements in the discourse. Our starting hypothesis is that users express themselves and their emotions differently depending on their age and gender, and besides, we think that this is independent on their language and social media where they write. We have collaborated in the creation of a common framework of evaluation at the PAN Lab of CLEF, generating resources that allowed us to verify our hypothesis achieving comparable and competitive results with the best ones obtained by other researchers on the field. In addition, we have investigated whether the expression of emotions would help to differentiate among users of different varieties of the same language, for example, Spanish from Spain, Mexican and Argentinian, or Portuguese from Portugal and Brazil. Our hypothesis is that the variation among languages is based more on lexical aspects, and we have corroborated it after comparing EmoGraph with representations based on word patterns, distributed representations and a representation that uses the whole vocabulary, but reducing its dimensionality to only 6 features per class, what is suitable for its application to big data environments such as social media. / [ES] La posibilidad de conocer rasgos de una persona a partir únicamente de los textos que escribe se ha convertido en un área de gran interés denominada author profiling. Ser capaz de inferir de un usuario su sexo, edad, idioma nativo o los rasgos de su personalidad, simplemente analizando sus textos, abre todo un abanico de posibilidades desde el punto de vista forense, de la seguridad o del marketing. Además, la proliferación de los medios sociales, que favorece nuevos modelos de comunicación y relación humana, potencia este abanico de posibilidades hasta cotas nunca antes vistas. La idiosincrasia inherente a estos medios sociales hace de ellos un entorno de comunicación especial, donde la libertad de expresión, la informalidad y la generación espontánea de temáticas y tendencias propician el acercamiento a la realidad diaria de las personas en su uso de la lengua. Sin embargo, esa misma idiosincrasia hace que en muchas ocasiones la aplicación de técnicas lingüísticas de análisis no sea posible, o sea extremadamente costoso. En este trabajo hemos propuesto EmoGraph, una representación basada en grafos con el objetivo de modelar el modo en que los usuarios expresan sus emociones, y el modo en que las articulan en el marco de su discurso, teniendo en consideración no sólo su frecuencia, sino también su posición y relación con y respecto a los elementos del mismo. Nuestra hipótesis de partida es que los usuarios se expresan y expresan sus emociones de manera diferente dependiendo de su edad y sexo, y además, pensamos que esto es así independientemente de su idioma y del medio donde escriban. Hemos colaborado en la creación de un marco común de evaluación en el laboratorio PAN del CLEF, generando recursos que nos han permitido verificar nuestra hipótesis y conseguir resultados comparables y competitivos con los mejores resultados obtenidos por los investigadores del área. Además, hemos querido investigar si la expresión de emociones permitiría diferenciar entre hablantes de diferentes variedades de una misma lengua, por ejemplo españoles, mexicanos o argentinos, o portugueses y brasileños. Nuestra hipótesis es que la variación entre lenguas se basa más en aspectos léxicos, y así lo hemos corroborado tras comparar EmoGraph con representaciones basadas en patrones, representaciones distribuidas y una representación que toma en consideración el vocabulario completo, pero reduciendo su dimensionalidad a únicamente 6 características por clase y que se erige idónea para su aplicación en entornos big data como los medios sociales. / [CAT] La possibilitat de conèixer trets d'una persona únicament a partir dels textos que escriu s'ha convertit en una àrea de gran interès anomenada author profiling. Ser capaç d'inferir d'un usuari el sexe, l'edat, l'idioma nadiu o els trets de la seua personalitat tan sols analitzant els seus textos, obre tot un ventall de possibilitats des del punt de vista forense, de la seguretat o del màrketing. A més, la proliferació dels mitjans socials, que afavoreix nous models de comunicació i de relació humana, potencia aquest ventall de possibilitats fins a cotes que no s'han vist fins ara. La idiosincràsia inherent a aquests mitjans socials en fa d'ells un entorn de comunicació especial, on la llibertat d'expressió, la informalitat i la generació espontània de temàtiques i tendències propicien l'aproximació a la realitat diària de les persones en l'ús que fan de la llengua. Tanmateix, aquesta idiosincràsia fa que en moltes ocasions no es puguin aplicar tècniques lingüístiques d'anàlisi, o que fer-ho resulti extremadament costós. En aquest treball hem proposat EmoGraph, una representació basada en grafs que té l'objectiu de modelar la manera en què els usaris expressen les seves emocions, i la manera com les articulen en el marc de llur discurs, considerant-ne no només la freqüència sinó també la posició i la relació amb i respecte als elements del discurs. La nostra hipòtesi de partida és que els usuaris s'expressen i expressen llurs emocions de manera diferent depenent de l'edat i el sexe, i a més, pensem que això és així independentment de l'idioma i del mitjà en què escriguin. Hem col·laborat en la creació d'un marc comú d'avaluació al laboratori PAN del CLEF, generant recursos que ens han permès verificar la nostra hipòtesi i aconseguir resultats comparables i competitius amb els millors resultats obtinguts pels investigadors de l'àrea. A més, hem volgut investigar si l'expressió d'emocions permetria establir diferències enre parlants de diferents varietats d'una mateixa llengua, per exemple espanyols, mexicans o argentins, o portuguesos i brasilers. La nostra hipòtesi és que la variació entre llengües es basa més en aspectes lèxics, i així ho hem corroborat després de comparar EmoGraph amb representacions basades en patrons, representacions distribuïdes i una representació que considera el vocabulari complet, però reduint-ne la dimensionalitat només a 6 característiques per classe i que s'erigeix de manera idònia per a aplicar-la en entorns big data com els mitjans socials. / Rangel Pardo, FM. (2016). Author Profiling en Social Media: Identificación de Edad, Sexo y Variedad del Lenguaje [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/67270 / TESIS

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Road traffic crashes are a serious public health problem worldwide, and human factors are the most prominent factor of accidents, affecting mostly the young adults. Past studies found that both gender and personality traits such as impulsivity are associated with risky driving, however, the interaction of these predictors is rarely addressed in the literature. To bridge the gap, the present study explores how a specific facet of impulsivity interacts with our hypothesized moderator, gender identification, leads to drug driving using a moderator analysis. We recruited participants from 17 to 35 years old possessing a valid drivers' licence via Facebook advertising. They were invited to complete a questionnaire on their socio-demographic characteristics, cannabis consumption habits and impulsivity scores. A moderator analysis is conducted to disentangle the relationship between sensation seeking, gender and driving after cannabis consumption using SPSS Process. The proposed model contains sensation seeking as an exogenous variable directly associated with driving after cannabis use, and this relationship is moderated by gender identification. The current study provides evidence that sensation seeking and gender identification are not only associated with DACU but also interact to affect driving behaviour. Implications of the study are discussed. / Les accidents routiers constituent de graves problèmes de santé publique dans le monde et les facteurs humains sont connus pour être le principal facteur d'accidents, impliquant principalement les jeunes adultes. Des études antérieures ont démontré que le genre ainsi que des facteurs liés à la personnalité tels que l'impulsivité sont associés à la conduite après consommation récente de cannabis, cependant, l'interaction de ces prédicteurs est rarement abordée dans la littérature. Pour cette raison, cette étude vise à explorer le processus par lequel une facette spécifique de l'impulsivité interagit avec le genre ou le sexe pour modérer la probabilité de prendre le volant après avoir consommé du cannabis. Des participants de 17 à 35 ans possédant un permis de conduire valide ont été recrutés à partir de Facebook par le biais d’annonces payantes. Ils étaient invités à remplir un questionnaire portant sur leur caractéristique socio-démographique, leur habitude de consommation de cannabis, ainsi que sur les composantes de l'impulsivité. Une analyse de modération a été effectuée pour clarifier la relation entre la recherche de sensations, le genre et la conduite d’automobile à l'aide du SPSS PROCESS. Le modèle proposé inclut la recherche de sensations comme variable exogène directement associée à la conduite après la consommation du cannabis, et cette relation est modérée par le genre ressenti. Effectivement, le genre ressenti des participants semble être une variable modératrice de l’association entre la recherche de sensation et la prise de volant après avoir consommé du cannabis. Les implications de ces résultats seront discutées.

People around the globe rely on their mobile devices for instant access to entertainment and social media. Children comprise a large majority of individuals who use smartphone applications, particularly for gaming and learning opportunities. Understandably, these apps become part of the identity development process, including the formation of one's gender identity. App developers include gendered content to capture and maintain children's attention, but much of the existing research examines children in late childhood and early adolescence, leaving the ages of six to eight relatively undiscussed. The researcher utilized a thematic analysis to review 20 children's mobile applications for instances of gender stereotypes. Social cognitive theory offers a guiding principle for understanding the process of developing one's gender identity, as well as the role that external stimuli, such as digital media examples and parent models, play. This study aimed to determine whether mobile applications targeting the identified age group use gender stereotypes, as well as how they employ these stereotypes within the application. The researcher randomly selected 20 top children's applications on the Apple App Store and examined them for gendered instances, such as occupations and interests as well as character depictions. The results reflect that instances of gender stereotypes do occur in the children's mobile applications. Many of the applications portrayed feminine stereotypes surrounding nurturing and caregiving tasks ("Mommy in Training"), making it one of the most frequently exploited feminine stereotypes in the sample. The "Boys will be Boys" stereotype comprised the most frequently displayed masculine stereotypes across the studied applications. These findings represent the idea that society places higher value on these stereotypes than others, such as social relationships ("The Power of Motivational Friendship") or recklessness ("The Risk Taker"). Implications include modeling of traditionally masculine and feminine stereotypes for young users by utilizing popular characters recognizable by most children in the target age range. / Master of Arts / Handheld electronic devices, such as smartphones and tablets, encompass some of the most widely used electronic devices in today's society. Most families in America have at least one mobile device with internet capability. Apple, the manufacturer of perhaps the most popular brand of electronic devices, pre-install their App Store on all devices they sell, giving users instant access to hundreds of thousands of different mobile applications that offer functions to make every aspect of life simpler. Young children spend a significant amount of their time playing games on these devices, although the American Academy of Pediatrics (2017) recommends that parents limit their children's daily screen time to no more than 2 hours, depending on the age of the child. The games that children download and play impose a number of different messages and stereotypes on their users, including gender stereotypes. Due to the substantial time children dedicate to these apps, the messages communicated regarding gender play crucial roles in the development of their gender identity. Social cognitive theory offers valuable insight and guidance into the gender identity development process. Therefore, the present study examines the gender stereotypes conveyed within 20 of the top children's mobile applications available on the Apple App Store in April 2018. The researcher randomly selected 20 children's applications, 10 each from the Top Free and Top Paid categories and examined them for gendered instances, such as occupations and interests as well as character depictions. The results reflect that instances of gender stereotypes do occur in the children's mobile applications. These represent the idea that society places higher value on certain stereotypes, like being caring and nurturing ("Mommy in Training") or engaging in messy, adventurous play ("Boys will be Boys"), than others, such as social relationships ("The Power of Motivational Friendship") or recklessness ("The Risk Taker"). Implications include modeling of traditionally masculine and feminine stereotypes for young users by utilizing popular characters recognizable by most children in the target age range.