Personalized News Recommender using Twitter

Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily swamped by information of little interest to them. News recommender systems are one approach to help users find interesting articles to read. News recommender systems present the articles to individual users based on their interests rather than presenting articles in order of their occurrence. In this thesis, we present our research on developing personalized news recommendation system with the help of a popular micro-blogging service Twitter . The news articles are ranked based on the popularity of the article that is identified with the help of the tweets from the Twitter\u27s public timeline. Also, user profiles are built based on the user\u27s interests and the news articles are ranked by matching the characteristics of the user profile. With the help of these two approaches, we present a hybrid news recommendation model that recommends interesting news stories to the user based on their popularity and their relevance to the user profile

Peeking into the other half of the glass : handling polarization in recommender systems.

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users\u27 interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate it to the item reviews, when available. We then propose a domain independent data science pipeline to automatically detect polarization using the ratings rather than the properties, typically used to detect polarization, such as item\u27s content or social network topology. We perform an extensive comparison of polarization measures on several benchmark data sets and show that our polarization detection framework can detect different degrees of polarization and outperforms existing measures in capturing an intuitive notion of polarization. We also investigate and uncover certain peculiar patterns that are characteristic of environments where polarization emerges: A machine learning algorithm finds it easier to learn discriminating models in polarized environments: The models will quickly learn to keep each user in the safety of their preferred viewpoint, essentially, giving rise to filter bubbles and making them easier to learn. After quantifying the extent of polarization in current recommender system benchmark data, we propose new counter-polarization approaches for existing collaborative filtering recommender systems, focusing particularly on the state of the art models based on Matrix Factorization. Our work represents an essential step toward the new research area concerned with quantifying, detecting and counteracting polarization in human-generated data and machine learning algorithms.We also make a theoretical analysis of how polarization affects learning latent factor models, and how counter-polarization affects these models. In the second part of our dissertation, we investigate the problem of discovering related information by recommendation of tags on social media micro-blogging platforms. Real-time micro-blogging services such as Twitter have recently witnessed exponential growth, with millions of active web users who generate billions of micro-posts to share information, opinions and personal viewpoints, daily. However, these posts are inherently noisy and unstructured because they could be in any format, hence making them difficult to organize for the purpose of retrieval of relevant information. One way to solve this problem is using hashtags, which are quickly becoming the standard approach for annotation of various information on social media, such that varied posts about the same or related topic are annotated with the same hashtag. However hashtags are not used in a consistent manner and most importantly, are completely optional to use. This makes them unreliable as the sole mechanism for searching for relevant information. We investigate mechanisms for consolidating the hashtag space using recommender systems. Our methods are general enough that they can be used for hashtag annotation in various social media services such as twitter, as well as for general item recommendations on systems that rely on implicit user interest data such as e-learning and news sites, or explicit user ratings, such as e-commerce and online entertainment sites. To conclude, we propose a methodology to extract stories based on two types of hashtag co-occurrence graphs. Our research in hashtag recommendation was able to exploit the textual content that is available as part of user messages or posts, and thus resulted in hybrid recommendation strategies. Using content within this context can bridge polarization boundaries. However, when content is not available, is missing, or is unreliable, as in the case of platforms that are rich in multimedia and multilingual posts, the content option becomes less powerful and pure collaborative filtering regains its important role, along with the challenges of polarization

Minds Online: The Interface between Web Science, Cognitive Science, and the Philosophy of Mind

Alongside existing research into the social, political and economic impacts of the Web, there is a need to study the Web from a cognitive and epistemic perspective. This is particularly so as new and emerging technologies alter the nature of our interactive engagements with the Web, transforming the extent to which our thoughts and actions are shaped by the online environment. Situated and ecological approaches to cognition are relevant to understanding the cognitive significance of the Web because of the emphasis they place on forces and factors that reside at the level of agent–world interactions. In particular, by adopting a situated or ecological approach to cognition, we are able to assess the significance of the Web from the perspective of research into embodied, extended, embedded, social and collective cognition. The results of this analysis help to reshape the interdisciplinary configuration of Web Science, expanding its theoretical and empirical remit to include the disciplines of both cognitive science and the philosophy of mind

Realising context-oriented information filtering.

The notion of information overload is an increasing factor in modern information service environments where information is ‘pushed’ to the user. As increasing volumes of information are presented to computing users in the form of email, web sites, instant messaging and news feeds, there is a growing need to filter and prioritise the importance of this information. ‘Information management’ needs to be undertaken in a manner that not only prioritises what information we do need, but to also dispose of information that is sent, which is of no (or little) use to us.The development of a model to aid information filtering in a context-aware way is developed as an objective for this thesis. A key concern in the conceptualisation of a single concept is understanding the context under which that concept exists (or can exist). An example of a concept is a concrete object, for instance a book. This contextual understanding should provide us with clear conceptual identification of a concept including implicit situational information and detail of surrounding concepts.Existing solutions to filtering information suffer from their own unique flaws: textbased filtering suffers from problems of inaccuracy; ontology-based solutions suffer from scalability challenges; taxonomies suffer from problems with collaboration. A major objective of this thesis is to explore the use of an evolving community maintained knowledge-base (that of Wikipedia) in order to populate the context model from prioritise concepts that are semantically relevant to the user’s interest space. Wikipedia can be classified as a weak knowledge-base due to its simple TBox schema and implicit predicates, therefore, part of this objective is to validate the claim that a weak knowledge-base is fit for this purpose. The proposed and developed solution, therefore, provides the benefits of high recall filtering with low fallout and a dependancy on a scalable and collaborative knowledge-base.A simple web feed aggregator has been built using the Java programming language that we call DAVe’s Rss Organisation System (DAVROS-2) as a testbed environment to demonstrate specific tests used within this investigation. The motivation behind the experiments is to demonstrate that the combination of the concept framework instantiated through Wikipedia can provide a framework to aid in concept comparison, and therefore be used in news filtering scenario as an example of information overload. In order to evaluate the effectiveness of the method well understood measures of information retrieval are used. This thesis demonstrates that the utilisation of the developed contextual concept expansion framework (instantiated using Wikipedia) improved the quality of concept filtering over a baseline based on string matching. This has been demonstrated through the analysis of recall and fallout measures

“I exploit my children for millions and millions of dollars on my mommyblog” How Heather B. Armstrong’s personal blog became a successful business

This study interrogates strategies to convert a personal blog into a brand and a business by analysing the narrative and aesthetic techniques involved in generating audience engagement, trust and affection, and the branding and monetisation approaches involved in developing a blog into a revenue-generating enterprise. The strategies presented in this study have been extrapolated from an in-depth analysis of the extremely successful personal blog: www.dooce.com, the website of Heather B. Armstrong. The research questions this study aims to address are grounded in distinct fields of enquiry, examining the narrative and aesthetic features underpinning the conversion of a personal blog into a brand; the representation of the everyday and its role in the construction of the blogger avatar as a human brand; the interplay between writing motivations and brand core values; and the influence that stereotypes about stay-at-home mothers, pregnancy and motherhood exert on the brand creation process of a female author. The interdisciplinary nature of this study is mirrored in its multi-faceted analytical approach which draws on theories pertaining to diverse fields of enquiry such as narratology, aesthetics, digital media, marketing communications and branding. The study aims to present strategies to construct a personal brand in the context of co-created online forums, with an emphasis on attaining authenticity, followership and audience loyalty through careful framing and strategic use of second person narration, and aesthetic categories such as zany, cute, interesting and abject. The study transposes a narrative approach to branding and online marketing studies with the aim of proposing a model of personal branding whereby blogger identity is simultaneously the product of authorial control and consumer-driven cultural work, with the blogger negotiating her personal brand in relation to personal values, everyday life circumstances, commercial pressures and audience feedback. The key propositions of this study are, firstly, that the use of second person narration as interpellation into active readerhood and of the cute, interesting, zany and abject as aesthetic categories that create novel reading experiences can generate high audience engagement, the abject being also directly related to fostering trust and authenticity. Secondly, bloggers can become human brands by strategically exhibiting and then reinforcing personality traits related to sophistication, competence, sincerity, excitement, ruggedness and non-conformism. Thirdly, consistency in writing style and self-disclosure can foster audience attachment and trust in the integrity and authenticity of the human brand. Fourthly, consumer attachment can be strategically cultivated through audience autonomy, competence and relatedness to the human brand and the development of an online brand community

Conversational agents with personality

Conversational agents (CAs) such as voice assistants and chatbots have permeated people's everyday lives. When interacting with these CAs, people automatically attribute a personality to them regardless of whether the CA designer intended it or not. This personality attribution fundamentally influences people's interaction behaviour and attitude towards the CA. By deliberately shaping the CA personality, designers have the opportunity to steer these automatic personality attributions in a desired direction. However, little information is available on how to design such a desired personality impression for a CA. Furthermore, in inter-human interaction, there is no such thing as a perfect personality. Nonetheless, today's commercial CAs have adopted a one-size-fits-all approach to their personality design, ignoring the potential benefits of adaptation. These two insights, namely (1) that users assign a personality to CAs and (2) that there is no such thing as a perfect personality, motivate the vision of this thesis: To improve the interaction between users and CAs by deliberately imbuing CAs with personality and tailoring them to user preferences. This dissertation pursues two primary goals to realise this vision: (1) to develop methods to imbue CAs with personality systematically and (2) to examine user preferences for CA personalities. To achieve the first goal, I introduce two approaches to imbue CAs with personality based on two underlying personality descriptions. The first approach adopts the human Big Five personality model as the theoretical basis for describing CA personality. This adoption allows me to transfer behaviour cues associated with human personality traits compiled from the psycholinguistic literature and my work to synthesise three levels of Agreeableness and Extraversion implemented in fully functional text-based CAs. An empirical evaluation of users' perceptions of these CAs after interacting with them demonstrates that human behaviour cues may be used to synthesise Agreeableness. However, they are insufficient to elicit the impression of low Extraversion or paint a complete picture of CA personality. Due to this insufficiency, I develop a second approach in which I explore whether the human Big Five model can be used to describe CA personality. To this end, I apply the psycholexical approach, which yields ten personality dimensions that do not correspond with the human Big Five model. Consequently, I propose these ten dimensions as an alternative comprehensive way to describe CA personality and introduce a new method, Enactment-based Dialogue Design, to synthesise personality based on these ten dimensions. To achieve the second goal, I present two approaches to examine user preferences for CA personality. Using a deductive approach, I investigate whether users prefer low, average, or high levels of four different personality dimensions in a CA in the context of different use cases. These investigations show that users have very individual preferences for the dimensions Extraversion and Social-Entertaining, whereas the majority prefer CAs that have a medium or high level of Agreeableness and a low level of Confrontational. I find the deductive approach to be useful for capturing users' evaluation of a personality-imbued CA, but it is not effective in collecting user requirements and visions of a perfect CA. The second inductive approach, however, furnishes a novel pragmatic method to better engage users in developing CA personalities. In this context, I also examine the influence of users’ personalities on their preferences for CA personality, but the effects are minimal. In summary, this thesis makes the following contributions to imbuing CAs with personality: (1) theoretical clarity on the necessity of dedicated personality descriptions for CAs, (2) a set of verbal cues associated with human personality implemented in fully functional text-based CA artefacts, (3) an exploration of two methods for synthesising personality in CAs, and (4) a new method for eliciting users' vision of the perfect CA. I consolidate these methods into a user-centred design process for developing CAs with personality. Furthermore, I provide empirical evidence of diverging user preferences and discuss overarching patterns which CA designers may use to tailor their CA personalities to individual users. Finally, this thesis proposes a research agenda for future work, which addresses the challenges that emerged from the presented work.Conversational Agents (CAs) wie Sprachassistenten und Chatbots sind aus dem Alltag der Menschen nicht mehr wegzudenken. In der Interaktion mit CAs schreiben Benutzer:innen ihnen automatisch eine Persönlichkeit zu, unabhängig davon, ob die CA-Designer:innen dies beabsichtigten oder nicht. Diese Persönlichkeitszuschreibung beeinflusst grundlegend das Interaktionsverhalten und die Einstellung der Benutzer:innen gegenüber den CAs. Eine bewusste Gestaltung der CA-Persönlichkeit erlaubt Designer:innen, diese automatischen Persönlichkeitszuschreibungen in eine gewünschte Richtung zu lenken. Jedoch gibt es nur wenige Informationen darüber, wie eine solche gewünschte Persönlichkeit für einen CA gestaltet werden kann. Darüber hinaus gibt es in der zwischenmenschlichen Interaktion nicht die eine perfekte CA-Persönlichkeit, die allen Benutzer:innen gleichermaßen gefällt. Nichtsdestotrotz sind heutige kommerzielle CAs lediglich mit einer Persönlichkeit für alle Benutzer:innen ausgestattet und lassen somit die potenziellen Vorteile einer Anpassung an individuelle Präferenzen außer Acht. Diese beiden Erkenntnisse, (1) dass Benutzer:innen CAs eine Persönlichkeit zuweisen und (2) dass es die eine perfekte Persönlichkeit nicht gibt, motivieren die Vision dieser Arbeit: Die Interaktion zwischen Benutzer:innen und CAs zu verbessern, indem CAs gezielt mit einer Persönlichkeit ausgestattet und an die Präferenzen der Benutzer:innen angepasst werden. Um diese Vision zu realisieren, verfolgt die vorliegende Dissertation zwei primäre Ziele: (1) die Entwicklung von Methoden, um CAs systematisch eine Persönlichkeit zu verleihen und (2) die Untersuchung von Präferenzen der Benutzer:innen für CA-Persönlichkeiten. Um das erste Ziel zu erreichen, stelle ich zwei Ansätze zur Ausstattung von CAs mit Persönlichkeit vor, die auf der jeweiligen zugrunde liegenden Persönlichkeitsbeschreibung basieren. In dem ersten Ansatz verwende ich das menschliche Big Five Persönlichkeitsmodell als theoretische Grundlage für die Beschreibung von CA-Persönlichkeit. Diese Annahme ermöglicht es, Verhaltenshinweise, die mit menschlichen Persönlichkeitsmerkmalen assoziiert sind, in der psycholinguistischen Literatur sowie meiner eigenen Arbeit zu identifizieren. Diese Verhaltenshinweise übertrage ich dann auf CAs, um jeweils drei Ausprägungen von Verträglichkeit und Extraversion zu synthetisieren, die in vollständig funktionsfähigen text-basierten CAs implementiert sind. Eine empirische Untersuchung der Wahrnehmung dieser text-basierten CAs deutet darauf hin, dass menschliche Verhaltenshinweise genutzt werden können, um Verträglichkeit zu synthetisieren. Sie sind jedoch unzureichend, um den Eindruck von niedriger Extraversion zu vermitteln sowie die Persönlichkeit von CAs vollständig abzubilden. Aufgrund der mangelnden Eignung der menschlichen Persönlichkeitsbeschreibung entwickle ich einen zweiten Ansatz, in dem ich untersuche, ob das menschliche Big Five Modell für die Beschreibung von CA-Persönlichkeit genutzt werden kann. Zu diesem Zweck wende ich den psycholexikalischen Ansatz an, aus dem zehn Persönlichkeitsdimensionen hervorgehen, die nicht mit dem menschlichen Big Five Modell übereinstimmen. Folglich schlage ich diese zehn Dimensionen als eine alternative und vollständige Möglichkeit zur Beschreibung von CA-Persönlichkeit vor. Außerdem führe ich eine neue Methode, genannt Inszenierung-basiertes Dialogdesign, ein, die es ermöglicht, Persönlichkeit auf Grundlage dieser zehn Dimensionen zu synthetisieren. Um das zweite Ziel zu erreichen, stelle ich zwei Ansätze zur Untersuchung der Präferenzen von Benutzer:innen für CA-Persönlichkeit vor. In einem deduktiven Ansatz untersuche ich zunächst, ob Benutzer:innen eine niedrige, durchschnittliche oder hohe Ausprägung von vier verschiedenen Persönlichkeitsdimensionen in einem CA im Kontext unterschiedlicher Anwendungsfälle bevorzugen. Diese Untersuchungen zeigen, dass die Benutzer:innen sehr individuelle Präferenzen für die Dimensionen Extraversion und Sozial-Unterhaltend haben, während die Mehrheit CAs bevorzugt, die eine mittlere oder hohe Ausprägung in Verträglichkeit sowie eine niedrige Ausprägung in Konfrontativ aufweisen. Obgleich der deduktive Ansatz nützlich für die Evaluierung von CA-Prototypen ist, ermöglicht dieser es nicht, Bedürfnisse und Vorstellungen der Benutzer:innen einzufangen. Im zweiten, induktiven Ansatz präsentiere ich daher eine neue pragmatische Methode, um die Benutzer:innen besser in die Entwicklung von CA-Persönlichkeiten einzubinden. In diesem Zusammenhang untersuche ich darüber hinaus den Einfluss der Persönlichkeit der Benutzer:innen auf ihre Präferenzen für die CA-Persönlichkeit, finde jedoch nur einen begrenzten Effekt. Zusammenfassend leistet die vorliegende Arbeit die folgenden wissenschaftlichen Beiträge zur Ausstattung von CAs mit Persönlichkeit: (1) Theoretische Klarheit über die Notwendigkeit dedizierter Persönlichkeitsbeschreibungen für CAs, (2) eine Sammlung verbaler Verhaltenshinweise, die mit menschlicher Persönlichkeit assoziiert sind und in voll funktionsfähigen CA-Artefakten implementiert sind, (3) eine Exploration von zwei Methoden zur Synthese von Persönlichkeit in CAs und (4) eine neue Methode, um die Vision eines perfekten CAs von Benutzer:innen zu eruieren. Ich führe diese Methoden in einem benutzungszentrierten Designprozess für die Entwicklung von CA-Persönlichkeiten zusammen. Darüber hinaus liefere ich empirische Belege für divergierende Präferenzen der Benutzer:innen für CA-Persönlichkeit und erörtere übergreife Muster, die CA-Designer:innen anwenden können, um ihre CA-Persönlichkeiten auf individuelle Benutzer:innen zuzuschneiden. Abschließend wird eine Forschungsagenda für zukünftige Arbeiten präsentiert, welche die Herausforderungen diskutiert, die sich aus den vorgestellten Arbeiten ergeben