Doctor of Philosophy in Computer Science

dissertationOver the last decade, social media has emerged as a revolutionary platform for informal communication and social interactions among people. Publicly expressing thoughts, opinions, and feelings is one of the key characteristics of social media. In this dissertation, I present research on automatically acquiring knowledge from social media that can be used to recognize people's affective state (i.e., what someone feels at a given time) in text. This research addresses two types of affective knowledge: 1) hashtag indicators of emotion consisting of emotion hashtags and emotion hashtag patterns, and 2) affective understanding of similes (a form of figurative comparison). My research introduces a bootstrapped learning algorithm for learning hashtag in- dicators of emotions from tweets with respect to five emotion categories: Affection, Anger/Rage, Fear/Anxiety, Joy, and Sadness/Disappointment. With a few seed emotion hashtags per emotion category, the bootstrapping algorithm iteratively learns new hashtags and more generalized hashtag patterns by analyzing emotion in tweets that contain these indicators. Emotion phrases are also harvested from the learned indicators to train additional classifiers that use the surrounding word context of the phrases as features. This is the first work to learn hashtag indicators of emotions. My research also presents a supervised classification method for classifying affective polarity of similes in Twitter. Using lexical, semantic, and sentiment properties of different simile components as features, supervised classifiers are trained to classify a simile into a positive or negative affective polarity class. The property of comparison is also fundamental to the affective understanding of similes. My research introduces a novel framework for inferring implicit properties that 1) uses syntactic constructions, statistical association, dictionary definitions and word embedding vector similarity to generate and rank candidate properties, 2) re-ranks the top properties using influence from multiple simile components, and 3) aggregates the ranks of each property from different methods to create a final ranked list of properties. The inferred properties are used to derive additional features for the supervised classifiers to further improve affective polarity recognition. Experimental results show substantial improvements in affective understanding of similes over the use of existing sentiment resources

Conference Programme v1 & Book of Abstracts

7th INTERNATIONAL CONFERENCE ON MEANING AND KNOWLEDGE REPRESENTATION MKR2018 @ ITB Dublin 4, 5 and 6 July, 201

Patterns and Variation in English Language Discourse

The publication is reviewed post-conference proceedings from the international 9th Brno Conference on Linguistics Studies in English, held on 16–17 September 2021 and organised by the Faculty of Education, Masaryk University in Brno. The papers revolve around the themes of patterns and variation in specialised discourses (namely the media, academic, business, tourism, educational and learner discourses), effective interaction between the addressor and addressees and the current trends and development in specialised discourses. The principal methodological perspectives are the comparative approach involving discourses in English and another language, critical and corpus analysis, as well as identification of pragmatic strategies and appropriate rhetorical means. The authors of papers are researchers from the Czech Republic, Italy, Luxembourg, Serbia and Georgia

Scientific dissemination and professional practices through digital media: The study of pragmatic strategies in the communication of international research projects

La investigación científica hoy en día está ligada a los procesos de globalización y a la búsqueda de la innovación y la excelencia, lo cual favorece una creciente colaboración, internacionalización y multidisciplinariedad. Para llevar a cabo estas iniciativas ambiciosas y de gran escala, los investigadores necesitan la financiación externa que distintas organizaciones, instituciones y programas pueden proporcionar. Esta reconfiguración del trabajo académico va de la mano de la ubiquidad y popularidad de Internet. Un extenso abanico de géneros, plataformas y medios digitales permiten a los científicos y académicos difundir sus investigaciones a una audiencia amplia y heterogénea. La inversión de esfuerzo en la comunicación mediada digitalmente permite a los investigadores contribuir a una diseminación más efectiva del conocimiento generado, así como cumplir con su compromiso social. Por otra parte, este esfuerzo les puede permitir reforzar su reputación como investigadores y conseguir un mayor impacto. Un ejemplo destacado de este escenario académico cambiante donde se maximiza el discurso digital para propósitos investigadores es el de los proyectos de investigación internacionales. Se trata de consorcios compuestos de miembros provenientes de entornos socioculturales y profesionales distintos que hacen uso de sitios web y redes sociales para la diseminación de sus proyectos conjuntos y utilizan las características tecnológicas y comunicativas de estos espacios digitales para ofrecer actualizaciones periódicas de su trabajo e información sobre hallazgos en progreso y resultados de investigación. De este modo, rinden cuentas a los organismos que los financian y aumentan su visibilidad entre los lectores digitales. Las intenciones comunicativas de estos equipos de investigación para cumplir dichos objetivos se codifican y transmiten discursivamente a través de diversas estrategias pragmáticas, que se encuadran en determinados parámetros contextuales y que responden a las especificidades del medio y se ven constreñidas por estas. Estas estrategias revelan cómo los investigadores comparten la información, cómo publicitan sus hallazgos y cómo se dirigen a sus potenciales lectores.Así, esta tesis doctoral tiene como objetivo investigar las estrategias pragmáticas prominentes en lengua inglesa empleadas por grupos de investigación internacionales en sus prácticas digitales discursivas, que normalmente se materializan en sitios webs y redes sociales para sus proyectos. Con este propósito, se compiló y analizó el corpus digital EUROPRO, que contiene 30 sitios web de proyectos de investigación que recibieron financiación en el marco del programa Horizonte2020 (subcorpus EUROPROwebs) y las correspondientes cuentas de Twitter de aquellos proyectos (subcorpus EUROPROtweets). Dichos subcorpus han sido extraídos de la base de datos digital EUROPRO recopilada por el grupo de investigación InterGedi. En mi tesis doctoral propongo una taxonomía derivada de los datos como resultado del análisis del corpus, que comprende 27 estrategias organizadas en torno a tres macrocategorías: informativas, promocionales e interaccionales. Incido teórica y metodológicamente en el proceso de diseñar y revisar esta herramienta analítica para así demonstrar su solidez y viabilidad. Además, analizo el rango de ocurrencia, la frecuencia y el uso específico de estas estrategias en las secciones que aparecen de manera sistemática en los sitios web incluidos en el corpus y en las páginas web donde se aloja la mayor parte de la información sobre el proyecto (Homepage, About, Partners, News & Events), en las cuentas de Twitter y, de forma comparativa, entre las secciones web y los tuits, con el fin de observar tendencias significativas y en cuanto a similitudes y diferencias en su funcionamiento en estos medios digitales. Además, adopto un enfoque etnográfico mediante la inclusión de evidencias contextuales conseguidas a través de entrevistas semi-estructuradas con investigadores de los proyectos Horizonte2020, cuyos resultados ayudan a sustentar los hallazgos procedentes del análisis textual. También tomo una perspectiva multimodal sobre cómo se emplean las estrategias pragmáticas en los sitios web de proyectos de investigación en relación a la sección Homepages. Este análisis, en concreto, permite reconocer el potencial de los recursos verbales y visuales para la construcción de significado desde una perspectiva pragmática. En general, el presente estudio busca ahondar en nuestro entendimiento de prácticas académicas digitales que están evolucionando rápidamente y que tienen gran alcance, en particular adoptadas por grupos de investigación, que pueden beneficiarse de los resultados y las implicaciones de esta investigación para la futura comunicación y diseminación de sus proyectos científicos.<br /

Metadiscourse analysis of digital interpersonal interactions in academic settings in Turkey

Rapid technological advances, efficiency and easy access have firmly established emailing as a vital medium of communication in the last decades. Nowadays, all around the world, particularly in educational settings, the medium is one of the most widely used modes of interaction between students and university lecturers. Despite their important role in academic life, very little is known about the metadiscursive characteristics of these e-messages and as far as the author is aware there is no study that has examined metadiscourse in request emails in Turkish. This study aims to contribute to filling in this gap by focusing on the following two research questions: (i) How many and what type of interpersonal metadiscourse markers are used in request emails sent by students to their lecturers? (ii) Where are they placed and how are they combined with other elements in the text? In order to answer these questions a corpus of unsolicited request e-mails in Turkish was compiled. The data collection started in January 2010 and continued until March 2018. A total of 353 request emails sent from university students to their lecturers were collected. The data were first transcribed in CLAN CHILDES format and analysed using the interpersonal model. The metadiscourse categories that aimed to involve readers in the email were identified and classified. Next, their places in the text were determined and described in detail. Findings of the study show that request emails include a wide array of multifunctional interpersonal metadiscourse markers which are intricately combined and employed by the writers to reach their aims. The results also showed that there is a close relation between the “weight of the request” and number of the interpersonal metadiscourse markers in request mails

Workshop Proceedings of the 12th edition of the KONVENS conference

The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years

Linguistic variation across Twitter and Twitter trolling

Trolling is used to label a variety of behaviours, from the spread of misinformation and hyperbole to targeted abuse and malicious attacks. Despite this, little is known about how trolling varies linguistically and what its major linguistic repertoires and communicative functions are in comparison to general social media posts. Consequently, this dissertation collects two corpora of tweets – a general English Twitter corpus and a Twitter trolling corpus using other Twitter users’ accusations – and introduces and applies a new short-text version of Multi-Dimensional Analysis to each corpus, which is designed to identify aggregated dimensions of linguistic variation across them. The analysis finds that trolling tweets and general tweets only differ on the final dimension of linguistic variation, but share the following linguistic repertoires: “Informational versus Interactive”, “Personal versus Other Description”, and “Promotional versus Oppositional”. Moreover, the analysis compares trolling tweets to general Twitter’s dimensions and finds that trolling tweets and general tweets are remarkably more similar than they are different in their distribution along all dimensions. These findings counter various theories on trolling and problematise the notion that trolling can be detected automatically using grammatical variation. Overall, this dissertation provides empirical evidence on how trolling and general tweets vary linguistically

The disinformation pandemic : understanding, identification, and mitigation in COVID-19 era

In 2020 during COVID-19, in addition to the spread of coronavirus disease, we also observed a pandemic of disinformation about the disease. This pandemic of disinformation became known as Infodemic in the medical world. Just as coronavirus was infecting our bodies, Infodemic was infecting our information ecosystem and exasperating the fight against the COVID-19 pandemic. Disinformation can be produced by various sources including scientists, media personalities, and others and it can be disseminated by news media, webpages, and social media from one source to another. Additionally, disinformation can spread easily from web media to social media where it can spread even faster to a wider audience. Therefore, it is important that disinformation be detected before it has a chance to spread. However, the identification of disinformation is fraught with several challenges. This fact highlights the importance of studying and identifying disinformation both in the content of web pages and social media posts before it is allowed to spread. In this dissertation, I pursued a three-essay approach to understand, identify, and mitigate the disinformation pandemic. While manual fact-checking is difficult, time-consuming, and expensive, various automated detection solutions could speed up this process. Therefore, in my first essay, I explored whether Machine Learning (ML) techniques can be used to develop predictive models for automatic identification of disinformation. Computational linguistics methods are used to extract content-based, and sentiment-based features of selected webpage’ articles to construct our study dataset. This dataset is used to train various ML algorithms to develop predictive models to identify disinformation. The results showed that there are significant differences among features of true and false information that can be used to identify disinformation. Since the spread of disinformation happens both on media pages and on social media platforms, it is important to analyze disinformation at both levels. Moreover, the literature shows that disinformation spreads six times faster than true information on social media, demonstrating that users get more engaged with disinformation. Therefore, I extended my research to enhance the understanding of disinformation detection based on content-based features and its impact on users’ engagement in social media posts. The findings of the second essay highlighted the critical role of linguistic structure, emotional tone, and the psychological load of social media posts on users’ engagement that can be used to differentiate information from disinformation. The results of the first two essays confirmed that negative emotional tone was one of the most important factors in disinformation posts and was associated with a high engagement score. So, in the third essay, I explore the impact of negative emotional tones in developing users’ perceptions regarding the accuracy of the content. Three separate experiments were developed to explore this. The results of experiments in the third essay highlighted the significant role of negative emotional tones on the believability of the content and their potential influence on behavioral change. My research findings allow for a better understanding and identification of disinformation by highlighting and identifying content-based features that are meant to mislead users to falsely perceive disinformation as information