Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Mapping (Dis-)Information Flow about the MH17 Plane Crash

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators

Impact of Mobility on Information Systems and Information System Design

The subject of this thesis is to analyse the impact of mobile hardware and software on information systems, to survey existing approaches for specifying mobile systems of computer science in general, and to provide suitable means for the formal design of information systems comprising such mobile units in particular. We consider a mobile unit to denote a mobile hardware or software entity, and a mobile system as a system comprising or being accessed by such mobile components. The various forms of mobile units occurring in computer science are explained and a taxonomy for them is developed, followed by a detailed discussion of their effects on computer and information systems. Several approaches for specifying mobile systems are presented and classified, with a particular emphasis on formal methods. As it turns out, these approaches do not allow to describe the set-up and release of communication links or to distinguish between the ever-mobile units of a compound system and those which provide the fixed subsystem as the context for the mobile entities, which are both important aspects to consider when developing information systems with mobile components. Therefore, corresponding constructs are then presented as an extension to the specification language Troll and its theoretical foundations, i.e. extended data signatures and the Module Distributed Temporal Logic Mdtl, both being interpreted over event structures. Finally, the application of the constructs is illustrated with the development of a system for accessing web services from mobile phones, which complements the ongoing example of information retrieval via mobile agents used to explain the constructs and concepts.Thema dieser Arbeit ist die Analyse der Auswirkungen von mobiler Hard- und Software auf Informationssysteme, die Untersuchung vorhandener Ansätze zur Spezifikation mobiler Systeme in der Informatik allgemein und für den formalen Entwurf von Informationssystemen mit mobilen Einheiten insbesondere. "Mobile Einheit" wird dabei als Oberbegriff für mobile Hardware- und Softwarekomponenten verwendet, und ein "mobiles System" ist ein System, das solche mobilen Komponenten beinhaltet oder auf das durch diese zugegriffen wird. Wir beschreiben die verschiedenen Formen, in denen mobile Einheiten in der Informatik auftreten, und entwickeln eine entsprechende Taxonomie, bevor wir deren Auswirkungen auf Computer- und Informationssysteme ausführlich diskutieren. Verschiedene Ansätze zur Spezifikation mobiler Systeme werden vorgestellt und eingeordnet, wobei das Augenmerk speziell auf formalen Methoden liegt. Es stellt sich heraus, dass es keiner dieser Ansätze ermöglicht, den Auf- und Abbau von Kommunikationsverbindungen zu beschreiben und zwischen den stets mobilen Einheiten und denjenigen zu unterscheiden, die das feste Teilsystem als Kontext für sie bilden. Beides sind aber wesentliche Aspekte, die in der Entwicklung von Informationssystemen mit mobilen Bestandteilen zu berücksichtigen sind. Daher stellen wir dann entsprechende Sprachkonstrukte als Erweiterung der Spezifikationssprache Troll inklusive der formalen Grundlagen vor. Diese Grundlagen beruhen auf erweiterten Datensignaturen und einer modularen verteilten temporalen Logik Mdtl, die beide über Ereignisstrukturen interpretiert werden. Schließlich wird die Verwendbarkeit der Sprachkonstrukte in der Entwicklung eines Systems zur Nutzung von Web-Diensten von Mobiltelefonen aus illustriert

Everything You Never Wanted to Know about Trolls:An Interdisplinary Exploration of the Who's, What's, and Why's of Trolling in Online Games

Summary Within the world of online gaming, trolling has become a regular menace. While gamers try to connect and socialize with one another, or even simply play the game, there are other gamers – trolls – on the prowl for an entirely different kind of good time, one in which they are enjoying themselves at the expense of everyone else (Chapters 2 and 3). Although trolling is common, and mass-media has latched onto it as a hot topic, it is only recently that the academic community has begun to take a serious look at how trolling occurs in and affects the gaming community at large. However, a lot of this literature is either descriptive in nature (see Thacker & Griffiths, 2012), or jumps ahead to prevention (see Cheng et al., 2017) without taking a deeper look at more than a single underlying motivation at a time. In short, there is a complex and prolific phenomenon happening online, but the research on it is only emerging. This dissertation’s goal is to take a deeper look at trolling as a phenomenon, beyond what has been done so far. More specifically, I aim to figure out a) what trolling is, b) why people do it, and c) who helps and who hinders trolling in online games. To do this, I took four different perspectives: the troll’s (Chapter 2), the researcher’s (Chapter 3), the victim’s (Chapter 4), and the bystander’s (Chapter 5). The purpose of Chapter 2 is to give the troll’s perspective on trolling, something that researchers had yet to do at the time. To do this, I interviewed 22 people who said that they had a history of trolling in online games. More specifically, I asked them about times they witnessed, were victims of, or perpetrated trolling, as well as what they thought about how the gaming community dealt with and felt about trolls and trolling. My goal with these interviews was threefold: I wanted to figure out a) what trolls consider trolling, b) what motivates them to do it, and c) the role of everyone else in game when it comes to encouraging or discouraging more trolling. What I found was that although trolling was almost universally considered a negative part of online gaming culture, and all the trolls in our group of participants started as victims of trolls before becoming trolls themselves, the online community neither encourages nor discourages it, making it an asocial activity. The next chapter allowed me to look at an archive of trolling incidents to find patterns in the way that different people involved in real-life trolling incidents communicate with one another. This public online archive consisted of 10,000 reported incidents of trolling in the popular online game League of Legends, and it included game data like player statistics, as well as everything all the players involved said during the game. Once the data was properly cleaned and prepared, myself and my co-author, Dr. Rianne Conijn, analysed the chat logs in two different ways: structural topic modelling (STM), and a traditional dictionary-based content analysis. In this way, we were able to see what characterized all the different actors – the troll, their victim(s), and the bystanders – and what was similar when it came to their messages. All this information was then compared to what existed already in literature used to describe trolls and trolling and complement what I had learned about trolls from Chapter 2. The key finding was that trolls and their teammates actually share a lot of the negative speech patterns (e.g., profanity, negative emotional content) normally associated with only trolls. Practically, this means that we have to be extremely careful as researchers when labelling trolls for the purpose of study, as we could very easily be falsely labelling victims. After speaking to trolls and looking at trolling interactions broadly, Chapter 4 focuses intently on the victim and their personal experience in a trolling simulation, taking into account their cultural background and values. It is also the first study to directly compare and contrast two different types of trolling: verbal (flaming) and behavioural (ostracism). They are both really common online occurrences, so the participants could easily relate, but they are extremely different in how they are executed, with flaming being vicious insults and ostracism being totally ignoring a person. Our participants were either Dutch, Pakistani, or Taiwanese, so that we could also look at how people from vastly different cultural backgrounds would react to – behaviourally and emotionally – the different kinds of trolling in the study. We simulated a trolling experience by putting our participants in a virtual game of catch with two computerized co-players, who they were led to believe were real people of either the same nationality or a minority member (e.g., a Moroccan immigrant in the Netherlands), who I had programmed to either troll them or silently watch the trolling happen. We found that there are indeed cultural differences when it comes to reactions, as well as differences between reactions to the two trolling types, but the core take-away is that future trolling interventions have to take into account the cultures of the target population as well as the specific type of trolling they are trying to fix or prevent in order to be effective. In the penultimate chapter, I shift the focus one last time to bystanders by putting participants in a game of League of Legends with two confederates who would troll one another throughout the game. This study’s goal was to see what motivated gamers to report trolls to an authority figure (the game developer) using the game’s built-in reporting functions, as the results of Chapter 2’s study suggested that this was an effective trolling deterrent. It is also, according to the results of the same study, the least-used recourse by bystanders faced with trolls in the proverbial wild. We found that how warm and friendly the troll was perceived to be and how competent the victim was perceived to be were what determined whether the participant reported our fake troll or not. A more competent victim and a less warm troll lead to more reports. To conclude, there is still a lot more to learn about trolls and trolling, but the field is farther along now than when this project started in 2015. There is a broad definition developed that encompasses most of the descriptive literature on trolling in games thus far. We also now know that there is the indication of a trolling cycle that requires further exploration. This is particularly important to know when it comes to the world of game development, as knowing the cycle exists allows for multiple points of intervention in order to protect their customers. Finally, this dissertation has shown the complexity of not just trolls – who are often portrayed in the media as one-dimensional antagonists – but also of everyone else involved in trolling interactions. Trolls, victims, and bystanders are all multi-faceted humans, and trolling, like all interactions, is an intricate social dance that deserves to be studied in even further depth in the future than what I have done here

Ontological foundations for structural conceptual models

In this thesis, we aim at contributing to the theory of conceptual modeling and ontology representation. Our main objective here is to provide ontological foundations for the most fundamental concepts in conceptual modeling. These foundations comprise a number of ontological theories, which are built on established work on philosophical ontology, cognitive psychology, philosophy of language and linguistics. Together these theories amount to a system of categories and formal relations known as a foundational ontolog