Parameter-free agglomerative hierarchical clustering to model learners' activity in online discussion forums

L'anàlisi de l'activitat dels estudiants en els fòrums de discussió online implica un problema de modelització altament depenent del context, el qual pot ser plantejat des d'aproximacions tant teòriques com empíriques. Quan aquest problema és abordat des de l'àmbit de la mineria de dades, l'enfocament més comunament adoptat és el de la classificació no supervisada (o clustering), donant lloc, d'aquesta manera, a un escenari de clustering en el qual el nombre real de clústers és a priori desconegut. Per tant, aquesta aproximació revela una qüestió subjacent, la qual no és sinó un dels problemes més coneguts del paradigma del clustering: l'estimació del nombre de clústers, habitualment seleccionat per l'usuari concorde a algun tipus de criteri subjectiu que pot comportar fàcilment l'aparició de biaixos indesitjats en els models obtinguts. Amb l'objectiu d'evitar qualsevol intervenció de l'usuari en l'etapa de clustering, dos nous criteris d'unió entre clústers són proposats en la present tesi, els quals, al seu torn, permeten la implementació d'un nou algorisme de clustering jeràrquic aglomeratiu lliure de paràmetres. Un complet conjunt d'experiments indica que el nou algorisme de clustering és capaç de proporcionar solucions de clustering òptimes enfront d'una gran varietat d'escenaris de clustering, sent capaç de bregar amb diferents classes de dades, així com de millorar el rendiment ofert pels algorismes de clustering més àmpliament emprats en la pràctica. Finalment, una estratègia d'anàlisi de dues etapes basada en el paradigma del clustering subespaial és proposada a fi d'abordar adequadament el problema de la modelització de la participació dels estudiants en les discussions asíncrones. Combinada amb el nou algorisme clustering, l'estratègia proposada demostra ser capaç de limitar la intervenció subjectiva de l'usuari a les etapes d'interpretació del procés d'anàlisi i de donar lloc a una completa modelització de l'activitat duta a terme pels estudiants en els fòrums de discussió online.El análisis de la actividad de los estudiantes en los foros de discusión online acarrea un problema de modelización altamente dependiente del contexto, el cual puede ser planteado desde aproximaciones tanto teóricas como empíricas. Cuando este problema es abordado desde el ámbito de la minería de datos, el enfoque más comúnmente adoptado es el de la clasificación no supervisada (o clustering), dando lugar, de este modo, a un escenario de clustering en el que el número real de clusters es a priori desconocido. Por tanto, esta aproximación revela una cuestión subyacente, la cual no es sino uno de los problemas más conocidos del paradigma del clustering: la estimación del número de clusters, habitualmente seleccionado por el usuario acorde a algún tipo de criterio subjetivo que puede conllevar fácilmente la aparición de sesgos indeseados en los modelos obtenidos. Con el objetivo de evitar cualquier intervención del usuario en la etapa de clustering, dos nuevos criterios de unión entre clusters son propuestos en la presente tesis, los cuales, a su vez, permiten la implementación de un nuevo algoritmo de clustering jerárquico aglomerativo libre de parámetros. Un completo conjunto de experimentos indica que el nuevo algoritmo de clustering es capaz de proporcionar soluciones de clustering óptimas frente a una gran variedad de escenarios de clustering, siendo capaz de lidiar con diferentes clases de datos, así como de mejorar el rendimiento ofrecido por los algoritmos de clustering más ampliamente utilizados en la práctica. Finalmente, una estrategia de análisis de dos etapas basada en el paradigma del clustering subespacial es propuesta a fin de abordar adecuadamente el problema de la modelización de la participación de los estudiantes en las discusiones asíncronas. Combinada con el nuevo algoritmo clustering, la estrategia propuesta demuestra ser capaz de limitar la intervención subjetiva del usuario a las etapas de interpretación del proceso de análisis y de dar lugar a una completa modelización de la actividad llevada a cabo por los estudiantes en los foros de discusión online.The analysis of learners' activity in online discussion forums leads to a highly context-dependent modelling problem, which can be posed from both theoretical and empirical approaches. When this problem is tackled from the data mining field, a clustering-based perspective is usually adopted, thus giving rise to a clustering scenario where the real number of clusters is a priori unknown. Hence, this approach reveals an underlying problem, which is one of the best-known issues of the clustering paradigm: the estimation of the number of clusters, habitually selected by user according to some kind of subjective criterion that may easily lead to the appearance of undesired biases in the obtained models. With the aim of avoiding any user intervention in the cluster analysis stage, two new cluster merging criteria are proposed in the present thesis, which allow to implement a novel parameter-free agglomerative hierarchical algorithm. A complete set of experiments indicate that the new clustering algorithm is able to provide optimal clustering solutions in the face of a great variety of clustering scenarios, both having the ability to deal with different kinds of data and outperforming clustering algorithms most widely used in practice. Finally, a two-stage analysis strategy based on the subspace clustering paradigm is proposed to properly tackle the issue of modelling learners' participation in the asynchronous discussions. In combination with the new clustering algorithm, the proposed strategy proves to be able to limit user's subjective intervention to the interpretation stages of the analysis process and to lead to a complete modelling of the activity performed by learners in online discussion forums

Public Archaeology in a Digital Age

This thesis examines the impact of the democratic promises of Internet communication technologies, social, and participatory media on the practice of public archaeology. It is focused on work within archaeological organisations in the UK in commercial archaeology, higher education, local authority planning departments and community settings, as well the voluntary planning departments and community settings, as well the voluntary archaeology sector archaeology sector . This work has taken an innovative approach to the subject matter through its use of a Grounded Theory method for data collection and analysis, and the use of a combination of online surveys, case studies and email questionnaires in order to address the following issues: the provision of authoritative archaeological information online; barriers to participation; policy and organisational approaches to evaluating success and archiving; community formation and activism, and the impact of digital inequalities and literacies. This thesis is the first overarching study into the use of participatory media in archaeology. It is an important exploration of where and how the profession is creating and managing digital platforms, and the expanding opportunities for networking and sharing information within the discipline, against a backdrop of rapid advancement in the use of Internet technologies within society. This work has made significant contributions to debates on the practice and impact of public archaeology. It has shown that archaeologists do not yet fully understand the complexities of Internet use and issues of digital literacy, the impact of audience demographics or disposition towards participation in online projects. It has shown that whilst recognition of democratic participation is not, on the whole, undertaken through a process of actively acknowledging responses to archaeological information, there remains potential for participatory media to support and accommodate these ideals. This work documents a period of great change within the practice of archaeology in the UK, and concludes with the observation that it is vital that the discipline undertake research into online audiences for archaeological information if we are to create sustainable digital public archaeologies

“YOU DON’T NEED PEOPLE’S OPINIONS ON A FACT!”: SATIRICAL COMEDY CORRECTS CLIMATE CHANGE DISINFORMATION

Satirical comedy has often been recognized as a corrective to, if not alternative for, commercial news as well as a source of accurate science information (Brewer & McKnight, 2015). In this dissertation, I analyze how satirical comedy debunks climate change myths, delivers accurate information, and promotes scientific expertise. Five interconnected assumptions guide the context and methodology of this interdisciplinary study: 1) that various actors have transformed climate change into a “manufactured scientific controversy” (Ceccarelli, 2011); 2) that satire, as a method, both assails targets and aggregates people (Hutcheon,1994); 3) that celebrity activism is impactful but problematic (Collins, 2007; Boykoff & Goodman, 2009); 4) that the YouTube comment board represents an audience study (Lange, 2008); and 5) that online comment is worthy of analysis (Reagle, 2015). This project analyzes two case studies, each consisting of two examples of satirical climate change comedy from John Oliver (his Statistically Representative Climate Change Debate and his Paris Agreement monologues) and from Jimmy Kimmel (his Scientists on Climate Change and Hey Donald Trump -- Climate Change Affects You Too segments). A three-tiered, mixed-methods approach is adopted to investigate the context, construction, circulation, and online reception of these satirical comedy videos. My project finds that the discursive integration (Baym, 2005) of satirical climate change comedy is potentially persuasive, but also risky and polarizing. Though centrist and left-of-center voices appreciate Oliver’s and Kimmel’s satirical interventions, conservative and right-of-center voices mark strict boundaries between comedy, celebrity, and climate change. It was also discovered that satirical comedy, which is accessible and viral, may intervene on YouTube’s climate change denial problem, correcting climate change falsehoods, and potentially drawing audiences away from their echo chambers and towards meaningful communication about the climate crisis. That is, many commenters use these videos as entry points to debate the causes of American climate change denial, correct climate change disinformation, and offer anecdotal evidence about the effects of climate change. At the same time, YouTube comments from the most resistant skeptics and repeat commenters provide insight into the persistence of circulating climate change myths and conflict frames. This study finally concludes that the analysis of comments on satirical climate change comedy exposes strategies for avoiding confirmation bias and the backfire effect along with techniques for creating more effective climate change communication

Quantitative Assessment of Factors in Sentiment Analysis

Sentiment can be defined as a tendency to experience certain emotions in relation to a particular object or person. Sentiment may be expressed in writing, in which case determining that sentiment algorithmically is known as sentiment analysis. Sentiment analysis is often applied to Internet texts such as product reviews, websites, blogs, or tweets, where automatically determining published feeling towards a product, or service is very useful to marketers or opinion analysts. The main goal of sentiment analysis is to identify the polarity of natural language text. This thesis sets out to examine quantitatively the factors that have an effect on sentiment analysis. The factors that are commonly used in sentiment analysis are text features, sentiment lexica or resources, and the machine learning algorithms employed. The main aim of this thesis is to investigate systematically the interaction between sentiment analysis factors and machine learning algorithms in order to improve sentiment analysis performance as compared to the opinions of human assessors. A software system known as TJP was designed and developed to support this investigation. The research reported here has three main parts. Firstly, the role of data pre-processing was investigated with TJP using a combination of features together with publically available datasets. This considers the relationship and relative importance of superficial text features such as emoticons, n-grams, negations, hashtags, repeated letters, special characters, slang, and stopwords. The resulting statistical analysis suggests that a combination of all of these features achieves better accuracy with the dataset, and had a considerable effect on system performance. Secondly, the effect of human marked up training data was considered, since this is required by supervised machine learning algorithms. The results gained from TJP suggest that training data greatly augments sentiment analysis performance. However, the combination of training data and sentiment lexica seems to provide optimal performance. Nevertheless, one particular sentiment lexicon, AFINN, contributed better than others in the absence of training data, and therefore would be appropriate for unsupervised approaches to sentiment analysis. Finally, the performance of two sophisticated ensemble machine learning algorithms was investigated. Both the Arbiter Tree and Combiner Tree were chosen since neither of them has previously been used with sentiment analysis. The objective here was to demonstrate their applicability and effectiveness compared to that of the leading single machine learning algorithms, Naïve Bayes, and Support Vector Machines. The results showed that whilst either can be applied to sentiment analysis, the Arbiter Tree ensemble algorithm achieved better accuracy performance than either the Combiner Tree or any single machine learning algorithm

Located Lexicon: a project that explores how user generated content describes place

This extended conference paper explores the use and potential of location data in social media contexts. The research involved a series of experiments undertaken to assess the extent to which location information is present in exchanges, directly or indirectly. A prototype application was designed to exploit the insight obtained from the data-gathering experiments. This enabled us to develop a method and toolkit for searching, extracting and visualising mass-generated data for open source use. Ultimately, we were able to generate insights into data quality and ‘scale of query’ for emerging pedagogical research in learning swarms and distributed learners

Play as an indicator of public opinion in online political commentary : a content analysis of online news forums leading up to the 2014 South African General Elections

This study seeks to look at play as an indicator of public opinion in online political commentary of online news forums leading to the 2014 South African general elections. A qualitative content analysis was used to analyse viewers’s comments about 2014 South African general elections posted online. The concepts of critical discourse analysis, frame analysis play theory and network analysis were applied to extend and inform the study. A corpus of all commentary appended to 2014 South African general election news reports published online by Media24, Times Media Group, Mail &Guardian, Independent Newspapers, Caxton CTP, and TNA Media were selected. The study employed a purposive sampling technique and 1000 comments were extracted. The sample began four weeks before the election and ended two weeks after the event. NVIVO 11 was utilized to code these readers’ comments into their respective categories. The core findings of this thesis reflect that online readers do not just engage in play but are more interactive and participative on these online public forums and their political discourse echo political affiliations with different political parties, bearing in mind that South Africa has 13 political parties that participated and are represented in parliament. In addition, the findings revealed that, play cannot be parted with and remains inseparable with "what is real"; instead, play renews the real world by giving it sense and meaning. Play does not "re-present" nor falsify certainty but it enunciates certainty. The findings also revealed that most participants identify themselves with the ANC as the ruling party, the DA as the main opposition, the EFF as the most vocal party and then other parties. The findings further revealed that participants have different perspectives on different economic and socio-political matters such as, entertainment, slate politics, and political affiliation, cadre deployment, political bias, economic meltdown, diaspora, and western influence, abuse of power by those in high places, land reform programme, political power struggles, leadership change and corruption

Management for Bachelors

The textbook contains educational module, which embraces the content of main regulatory disciplines on specialists training by the direction 6.030601 “Management” in the knowledge branch 03.06 “Management and administration” of the educational and qualification level “Bachelor”. According to the content the disciplines completely conform to curricula approved by scientific and methodological commission on management and agreed with logical and structural scheme of educational process. The textbook embraces almost all aspects of bachelor training. The chapters contain questions for self-control and list of recommended literature. While creating the chapters the results of fundamental and applied scientific researches of the evaluation branch, the forecasting and management of economic potential of complicated industrial system were used