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Data visualization is an effective way to explore data. It helps people to get a valuable insight of the data by placing it in a visual context. However, choosing a good chart without prior knowledge in the area is not a trivial job. Users have to manually explore all possible visualizations and decide upon ones that reflect relevant and desired trend in the data, are insightful and easy to decode, have a clear focus and appealing appearance. To address these challenges we developed a Tool for Automatic Generation of Good viSualizations using Scoring (TAG²S²). The approach tackles the problem of identifying an appropriate metric for judging visualizations as good or bad. It consists of two modules: visualization detection: given a data-set it creates a list of combination of data attributes for scoring and visualization ranking: scores each chart and decides which ones are good or bad. For the later, an utility metric of ten criteria was developed and each visualization detected in the first module is evaluated on these criteria. Only those visualizations that received enough scores are then presented to the user. Additionally to these data parameters, the tool considers user perception regarding the choice of visual encoding when selecting a visualization. To evaluate the utility of the metric and the importance of each criteria, test cases were developed, executed and the results presented.
Most social media platforms allow users to freely express their opinions, feelings, and beliefs. However, in recent years the growing propagation of hate speech, offensive language, racism and sexism on the social media outlets have drawn attention from individuals, companies, and researchers. Today, sexism both online and offline with different forms, including blatant, covert, and subtle lan- guage, is a common phenomenon in society. A notable amount of work has been done over identifying sexist content and computationally detecting sexism which exists online. Although previous efforts have mostly used peoples’ activities on social media platforms such as Twitter as a public and helpful source for collecting data, they neglect the fact that the method of gathering sexist tweets could be biased towards the initial search terms. Moreover, some forms of sexism could be missed since some tweets which contain offensive language could be misclassified as hate speech. Further, in existing hate speech corpora, sexist tweets mostly express hostile sexism, and to some degree, the other forms of sexism which also appear online was disregarded. Besides, the creation of labeled datasets with manual exertion, relying on users to report offensive comments with a tremendous effort by human annotators is not only a costly and time-consuming process, but it also raises the risk of involving discrimination under biased judgment.
This thesis generates a novel sexist and non-sexist dataset which is constructed via "UnSexistifyIt", an online web-based game that incentivizes the players to make minimal modifications to a sexist statement with the goal of turning it into a non-sexist statement and convincing other players that the modified statement is non-sexist. The game applies the methodology of "Game With A Purpose" to generate data as a side-effect of playing the game and also employs the gamification and crowdsourcing techniques to enhance non-game contexts. When voluntary participants play the game, they help to produce non-sexist statements which can reduce the cost of generating new corpus. This work explores how diverse individual beliefs concerning sexism are. Further, the result of this work highlights the impact of various linguistic features and content attributes regarding sexist language detection. Finally, this thesis could help to expand our understanding regarding the syntactic and semantic structure of sexist and non-sexist content and also provides insights to build a probabilistic classifier for single sentences into sexist or non-sexist classes and lastly find a potential ground truth for such a classifier.
Our work finds the fine grained edits in context of neighbouring tokens in Wikipedia articles. We cluster those edits according to similar neighbouring context. We encode neighbouring context into vector space using word vectors. We evaluate clusters returned by our algorithm on extrinsic and intrinsic metric and compare it with previous work. We analyse the relation between extrinsic and intrinsic measurements of fine grained edit tokens.
The purpose of this thesis is to explore the sentiment distributions of Wikipedia concepts.
We analyse the sentiment of the entire English Wikipedia corpus, which includes 5,669,867 articles and 1,906,375 talks, by using a lexicon-based method with four different lexicons.
Also, we explore the sentiment distributions from a time perspective using the sentiment scores obtained from our selected corpus. The results obtained have been compared not only between articles and talks but also among four lexicons: OL, MPQA, LIWC, and ANEW.
Our findings show that among the four lexicons, MPQA has the highest sensitivity and ANEW has the lowest sensitivity to emotional expressions. Wikipedia articles show more sentiments than talks according to OL, MPQA, and LIWC, whereas Wikipedia talks show more sentiments than articles according to ANEW. Besides, the sentiment has a trend regarding time series, and each lexicon has its own bias regarding text describing different things.
Moreover, our research provides three interactive widgets for visualising sentiment distributions for Wikipedia concepts regarding the time and geolocation attributes of concepts.
Navigation is a natural way to explore and discover content in a digital environment. Hence, providers of online information systems such as Wikipedia---a free online encyclopedia---are interested in providing navigational support to their users. To this end, an essential task approached in this thesis is the analysis and modeling of navigational user behavior in information networks with the goal of paving the way for the improvement and maintenance of web-based systems. Using large-scale log data from Wikipedia, this thesis first studies information access by contrasting search and navigation as the two main information access paradigms on the Web. Second, this thesis validates and builds upon existing navigational hypotheses to introduce an adaptation of the well-known PageRank algorithm. This adaptation is an improvement of the standard PageRank random surfer navigation model that results in a more "reasonable surfer" by accounting for the visual position of links, the information network regions they lead to, and the textual similarity between the link source and target articles. Finally, using agent-based simulations, this thesis compares user models that have a different knowledge of the network topology in order to investigate the amount and type of network topological information needed for efficient navigation. An evaluation of agents' success on four different networks reveals that in order to navigate efficiently, users require only a small amount of high-quality knowledge of the network topology. Aside from the direct benefits to content ranking provided by the "reasonable surfer" version of PageRank, the empirical insights presented in this thesis may also have an impact on system design decisions and Wikipedia editor guidelines, i.e., for link placement and webpage layout.
Wikipedia is the biggest, free online encyclopaedia that can be expanded by any-one. For the users, who create content on a specific Wikipedia language edition, a social network exists. In this social network users are categorised into different roles. These are normal users, administrators and functional bots. Within the networks, a user can post reviews, suggestions or send simple messages to the "talk page" of another user. Each language in the Wikipedia domain has this type of social network.
In this thesis characteristics of the three different roles are analysed in order to learn how they function in one language network of Wikipedia and apply them to another Wikipedia network to identify bots. Timestamps from created posts are analysed to reveal noticeable characteristics referring to continuous messages, message rates and irregular behaviour of a user are discovered. Through this process we show that there exist differences between the roles for the mentioned characteristics.
We examine the systematic underrecognition of female scientists (Matilda effect) by exploring the citation network of papers published in the American Physical Society (APS) journals. Our analysis shows that articles written by men (first author, last author and dominant gender of authors) receive more citations than similar articles written by women (first author, last author and dominant gender of authors) after controlling for the journal of publication, year of publication and content of the publication. Statistical significance of the overlap between the lists of references was considered as the measure of similarity between articles in our analysis. In addition, we found that men are less likely to cite articles written by women and women are less likely to cite articles written by men. This pattern leads to receiving more citations by articles written by men than similar articles written by women because the majority of authors who published in APS journals are male (85%). We also observed Matilda effect reduces when articles are published in journals with the highest impact factors. In other words, people’s evaluation of articles published in these journals is not affected by the gender of authors significantly. Finally, we suggested a method that can be applied by editors in academic journals to reduce the evaluation bias to some extent. Editors can identify missing citations using our proposed method to complete bibliographies. This policy can reduce the evaluation bias because we observed papers written by female scholars (first author, last author, the dominant gender of authors) miss more citations than articles written by male scholars (first author, last author, the dominant gender of authors).
Ontologien sind wichtige Werkzeuge zur Wissensrepräsentation und elementare Bausteine des Semantic Web. Sie sind jedoch nicht statisch und können sich über die Zeit verändern. Die Gründe hierfür sind vielfältig: Konzepte innerhalb einer Ontologie können fehlerhaft modelliert worden sein, die von der Ontologie repräsentierte Domäne kann sich verändern oder eine Ontologie kann wiederverwendet werden und muss an den neuen Kontext angepasst oder mit bestehenden Ontologien verbunden werden. Die Schwierigkeit dieses Prozesses hat zur Entstehung des Forschungsfeldes der Ontology Change geführt. Das Entfernen von Wissen aus Ontologien ist ein wichtiger Aspekt dieses Änderungsprozesses, da selbst das Hinzufügen neuen Wissens zu einer Ontologie das Entfernen bestehenden Wissens notwendig machen kann, falls dieses mit den neuen Vorstellungen in Konflikt steht. Dieses Entfernen muss jedoch wohldurchdacht sein, da das Ändern bestehender Konzepte leicht zu viel Wissen aus der Ontologie entfernen oder die semantische Bedeutung der Konzepte auf eine potenziell unerwartete Weise verändern kann. In dieser Arbeit wird daher ein formaler Operator zum präzisen Entfernen von Wissen aus Konzepten vorgestellt. Dieser basiert auf der Beschreibungslogik EL und baut partiell auf den Postulaten für Belief Set und Belief Base Contraction sowie der Arbeit von Suchanek et al. auf. Hierfür wird zunächst ein Einstieg in das Thema Ontologien und die Ontologiesprache OWL 2 gegeben und das Problemfeld der Ontology Change wird erläutert. Es wird dann gezeigt, wie ein formaler Operator diesen Prozess unterstützen kann und weshalb die Beschreibungslogik EL einen guten Ausgangspunkt für die Entwicklung eines solchen Operators darstellt. Anschließend wird ein Einblick in das Feld der Beschreibungslogiken gegeben. Hierfür wird die Geschichte der Beschreibungslogik kurz umrissen, Anwendungsgebiete werden genannt und es werden Standardprobleme in dieser Logik erläutert. In diesem Zusammenhang wird die Beschreibungslogik EL formal eingeführt. In einem nächsten Schritt werden verwandte Arbeiten untersucht und es wird gezeigt, warum das Recovery- und Relevance-Postulat für das Entfernen von Wissen aus Konzepten nicht unmittelbar anwendbar ist. Die hier gewonnenen Erkenntnisse werden anschließend dazu genutzt, die Anforderungen an den Operator zu formalisieren. Diese basieren hauptsächlich auf den Postulaten für Belief Set und Belief Base Contraction. Zusätzlich werden weitere Eigenschaften formuliert welche den Verlust des Recovery- bzw. Relevance-Postulates ausgleichen sollen. In einem nächsten Schritt wird der Operator definiert und es wird gezeigt, dass diese Definition das präzise Entfernen von Wissen aus EL-Konzepten gestattet. Mittels formaler Beweise wird zudem gezeigt, dass diese Definition alle zuvor aufgestellten Anforderungen erfüllt. In einem weiteren Beispiel wird dargestellt, wie der Operator in Verbindung mit sogenannten Laconic Justifications verwendet werden kann, um einen menschlichen Ontology-Editor durch das automatisierte Entfernen von unerwünschten Konsequenzen aus der Ontologie zu unterstützen. Aufbauend auf Algorithmen, welche aus der formalen Definition des Operators abgeleitet wurden, wird ein Plugin zum Entfernen von Wissen aus Ontologien für den Ontology-Editor Protégé vorgestellt. Anschließend werden die bisherigen Erkenntnisse zusammengefasst und es wird ein Fazit gezogen. Die Arbeit schließt mit einem Ausblick über mögliche zukünftige Forschung.
Knowledge-based authentication methods are vulnerable to Shoulder surfing phenomenon.
The widespread usage of these methods and not addressing the limitations it has could result in the user’s information to be compromised. User authentication method ought to be effortless to use and efficient, nevertheless secure.
The problem that we face concerning the security of PIN (Personal Identification Number) or password entry is shoulder surfing, in which a direct or indirect malicious observer could identify the user sensitive information. To tackle this issue we present TouchGaze which combines gaze signals and touch capabilities, as an input method for entering user’s credentials. Gaze signals will be primarily used to enhance targeting and touch for selecting. In this work, we have designed three different PIN entry method which they all have similar interfaces. For the evaluation, these methods were compared based on efficiency, accuracy, and usability. The results uncovered that despite the fact that gaze-based methods require extra time for the user to get familiar with yet it is considered more secure. In regards to efficiency, it has the similar error margin to the traditional PIN entry methods.
This Master Thesis is an exploratory research to determine whether it is feasible to construct a subjectivity lexicon using Wikipedia. The key hypothesis is that that all quotes in Wikipedia are subjective and all regular text are objective. The degree of subjectivity of a word, also known as ''Quote Score'' is determined based on the ratio of word frequency in quotations to its frequency outside quotations. The proportion of words in the English Wikipedia which are within quotations is found to be much smaller as compared to those which are not in quotes, resulting in a right-skewed distribution and low mean value of Quote Scores.
The methodology used to generate the subjectivity lexicon from text corpus in English Wikipedia is designed in such a way that it can be scaled and reused to produce similar subjectivity lexica of other languages. This is achieved by abstaining from domain and language-specific methods, apart from using only readily-available English dictionary packages to detect and exclude stopwords and non-English words in the Wikipedia text corpus.
The subjectivity lexicon generated from English Wikipedia is compared against other lexica; namely MPQA and SentiWordNet. It is found that words which are strongly subjective tend to have high Quote Scores in the subjectivity lexicon generated from English Wikipedia. There is a large observable difference between distribution of Quote Scores for words classified as strongly subjective versus distribution of Quote Scores for words classified as weakly subjective and objective. However, weakly subjective and objective words cannot be differentiated clearly based on Quote Score. In addition to that, a questionnaire is commissioned as an exploratory approach to investigate whether subjectivity lexicon generated from Wikipedia could be used to extend the coverage of words of existing lexica.
The content aggregator platform Reddit has established itself as one of the most popular websites in the world. However, scientific research on Reddit is hindered as Reddit allows (and even encourages) user anonymity, i.e., user profiles do not contain personal information such as the gender. Inferring the gender of users in large-scale could enable the analysis of gender-specific areas of interest, reactions to events, and behavioral patterns. In this direction, this thesis suggests a machine learning approach of estimating the gender of Reddit users. By exploiting specific conventions in parts of the website, we obtain a ground truth for more than 190 million comments of labeled users. This data is then used to train machine learning classifiers to use them to gain insights about the gender balance of particular subreddits and the platform in general. By comparing a variety of different approaches for classification algorithm, we find that character-level convolutional neural network achieves performance with an 82.3% F1 score on a task of predicting a gender of a user based on his/her comments. The score surpasses 85% mark for frequent users with more than 50 comments. Furthermore, we discover that female users are less active on Reddit platform, they write fewer comments and post in fewer subreddits on average, when compared to male users.
Topic Models sind ein beliebtes Werkzeug um Themen in großen Textkorpora zu identifizieren. Diese Textkorpora enthalten oft versteckte Meta-Gruppen. Das Größenverhältnis zwischen diesen Gruppen variiert meist stark. Die Präsenz dieser Gruppen wird in der Praxis oft ignoriert. Diese Masterarbeit erforscht daher, ob diese Gruppen Einfluss auf ein Topic Model haben.
Um den Einfluss zu testen, wird LDA auf Samples mit unterschiedlichen Gruppengrößen trainiert. Die Samples werden von Textkorpora mit großen Gruppenunterschieden (d.h. Sprachunterschieden) und kleinen Gruppenunterschieden (d.h. Unterschiede in der politische Orientierung) generiert. Die Leistungsfähigkeit von LDA wird per "Perplexity" evaluiert.
Der Einfluss von Gruppen auf die generelle Leistungsfähigkeit von Topic Models hängt von verschiedenen Faktoren der Gruppen ab, z.B. der Vorhersagbarkeit der Sprache generell. Die Leistungsfähigkeit der Topic Models für die einzelnen Gruppen wird von der Variation der relativen Gruppengrößen beeinflusst. Allerdings ist der Effekt für alle Datensätze verschieden.
LDA kann die Gruppen intern unterscheiden, wenn die Unterschiede der Gruppen groß genug sind (z.B. Sprachunterschiede). Der Anteil der Topics, die explizit für eine Gruppe gelernt werden, ist jedoch unterproportional zu dem Anteil der Gruppe im Trainingskorpus. Dieser Effekt verstärkt sich für kleinere Minderheiten.
The output of eye tracking Web usability studies can be visualized to the analysts as screenshots of the Web pages with their gaze data. However, the screenshot visualizations are found to be corrupted whenever there are recorded fixations on fixed Web page elements on different scroll positions. The gaze data are not gathered on their fixated fixed elements; rather they are scattered on their recorded scroll positions. This problem has raised our attention to find an approach to link gaze data to their intended fixed elements and gather them in one position on the screenshot. The approach builds upon the concept of creating the screenshot during the recording session, where images of the viewport are captured on visited scroll positions and lastly stitched into one Web page screenshot. Additionally, the fixed elements in the Web page are identified and linked to their fixations. For the evaluation, we compared the interpretation of our enhanced screenshot against the video visualization, which overcomes the problem. The results revealed that both visualizations equally deliver accurate interpretations. However, interpreting the visualizations of eye tracking Web usability studies using the enhanced screenshots outperforms the video visualizations in terms of speed and it requires less temporal demands from the interpreters.
The Web contains some extremely valuable information; however, often poor quality, inaccurate, irrelevant or fraudulent information can also be found. With the increasing amount of data available, it is becoming more and more difficult to distinguish truth from speculation on the Web. One of the most, if not the most, important criterion used to evaluate data credibility is the information source, i.e., the data origin. Trust in the information source is a valuable currency users have to evaluate such data. Data popularity, recency (or the time of validity), reliability, or vagueness ascribed to the data may also help users to judge the validity and appropriateness of information sources. We call this knowledge derived from the data the provenance of the data. Provenance is an important aspect of the Web. It is essential in identifying the suitability, veracity, and reliability of information, and in deciding whether information is to be trusted, reused, or even integrated with other information sources. Therefore, models and frameworks for representing, managing, and using provenance in the realm of Semantic Web technologies and applications are critically required. This thesis highlights the benefits of the use of provenance in different Web applications and scenarios. In particular, it presents management frameworks for querying and reasoning in the Semantic Web with provenance, and presents a collection of Semantic Web tools that explore provenance information when ranking and updating caches of Web data. To begin, this thesis discusses a highly exible and generic approach to the treatment of provenance when querying RDF datasets. The approach re-uses existing RDF modeling possibilities in order to represent provenance. It extends SPARQL query processing in such a way that given a SPARQL query for data, one may request provenance without modifying it. The use of provenance within SPARQL queries helps users to understand how RDF facts arederived, i.e., it describes the data and the operations used to produce the derived facts. Turning to more expressive Semantic Web data models, an optimized algorithm for reasoning and debugging OWL ontologies with provenance is presented. Typical reasoning tasks over an expressive Description Logic (e.g., using tableau methods to perform consistency checking, instance checking, satisfiability checking, and so on) are in the worst case doubly exponential, and in practice are often likewise very expensive. With the algorithm described in this thesis, however, one can efficiently reason in OWL ontologies with provenance, i.e., provenance is efficiently combined and propagated within the reasoning process. Users can use the derived provenance information to judge the reliability of inferences and to find errors in the ontology. Next, this thesis tackles the problem of providing to Web users the right content at the right time. The challenge is to efficiently rank a stream of messages based on user preferences. Provenance is used to represent preferences, i.e., the user defines his preferences over the messages' popularity, recency, etc. This information is then aggregated to obtain a joint ranking. The aggregation problem is related to the problem of preference aggregation in Social Choice Theory. The traditional problem formulation of preference aggregation assumes a I fixed set of preference orders and a fixed set of domain elements (e.g. messages). This work, however, investigates how an aggregated preference order has to be updated when the domain is dynamic, i.e., the aggregation approach ranks messages 'on the y' as the message passes through the system. Consequently, this thesis presents computational approaches for online preference aggregation that handle the dynamic setting more efficiently than standard ones. Lastly, this thesis addresses the scenario of caching data from the Linked Open Data (LOD) cloud. Data on the LOD cloud changes frequently and applications relying on that data - by pre-fetching data from the Web and storing local copies of it in a cache - need to continually update their caches. In order to make best use of the resources (e.g., network bandwidth for fetching data, and computation time) available, it is vital to choose a good strategy to know when to fetch data from which data source. A strategy to cope with data changes is to check for provenance. Provenance information delivered by LOD sources can denote when the resource on the Web has been changed last. Linked Data applications can benefit from this piece of information since simply checking on it may help users decide which sources need to be updated. For this purpose, this work describes an investigation of the availability and reliability of provenance information in the Linked Data sources. Another strategy for capturing data changes is to exploit provenance in a time-dependent function. Such a function should measure the frequency of the changes of LOD sources. This work describes, therefore, an approach to the analysis of data dynamics, i.e., the analysis of the change behavior of Linked Data sources over time, followed by the investigation of different scheduling update strategies to keep local LOD caches up-to-date. This thesis aims to prove the importance and benefits of the use of provenance in different Web applications and scenarios. The exibility of the approaches presented, combined with their high scalability, make this thesis a possible building block for the Semantic Web proof layer cake - the layer of provenance knowledge.
This thesis presents novel approaches for integrating context information into probabilistic models. Data from social media is typically associated with metadata, which includes context information such as timestamps, geographical coordinates or links to user profiles. Previous studies showed the benefits of using such context information in probabilistic models, e.g.\ improved predictive performance. In practice, probabilistic models which account for context information still play a minor role in data analysis. There are multiple reasons for this. Existing probabilistic models often are complex, the implementation is difficult, implementations are not publicly available, or the parameter estimation is computationally too expensive for large datasets. Additionally, existing models are typically created for a specific type of content and context and lack the flexibility to be applied to other data.
This thesis addresses these problems by introducing a general approach for modelling multiple, arbitrary context variables in probabilistic models and by providing efficient inference schemes and implementations.
In the first half of this thesis, the importance of context and the potential of context information for probabilistic modelling is shown theoretically and in practical examples. In the second half, the example of topic models is employed for introducing a novel approach to context modelling based on document clusters and adjacency relations in the context space. They can cope with areas of sparse observations and These models allow for the first time the efficient, explicit modelling of arbitrary context variables including cyclic and spherical context (such as temporal cycles or geographical coordinates). Using the novel three-level hierarchical multi-Dirichlet process presented in this thesis, the adjacency of ontext clusters can be exploited and multiple contexts can be modelled and weighted at the same time. Efficient inference schemes are derived which yield interpretable model parameters that allow analyse the relation between observations and context.
In this article we analyze the privacy aspects of a mobile sensor application used for recording urban travel patterns as part of a travel-survey service. This service has been developed and field-tested within the Live+Gov EU Project. The privacy analysis follows a structured approach established in. Eight privacy recommendations are derived, and have already led to corresponding enhancements of the travel-survey service.
“Did I say something wrong?” A word-level analysis of Wikipedia articles for deletion discussions
(2016)
Diese Arbeit beschäftigt sich damit, linguistische Erkenntnisse auf Wortebene über schriftlichen Diskussionen zu gewinnen. Die Unterscheidung zwischen Botschaften, welche sich förderlich auf Diskussionen auswirken und jene, welche diese unterbrechen, spielte dabei eine besondere Rolle. Hierbei lag ein Schwerpunkt darauf, zu ermitteln, ob Ich- und Du-Botschaften charakteristisch für die beiden Kommunikationsarten sind. Diese Botschaften sind über Jahre hinweg zu Empfehlungen für erfolgreiche Kommunikation avanciert. Ihre zugeschriebene Wirkung wurde zwar mehrfach bestätigt, jedoch geschah dies stets in kleineren Studien. Deshalb wurde in dieser Arbeit mithilfe der Löschdiskussionen der englischen Wikipedia und der Liste gesperrter Nutzer eine vollautomatische Erstellung eines annotierten Datensatzes entwickelt. Dabei wurden Diskussionsbotschaften entweder als förderlich oder schädlich für einen konstruktiven Diskussionsverlauf markiert. Dieser Datensatz wurde anschließend im Rahmen einer binären Klassifikation verwendet, um charakteristische Worte für die beiden Kommunikationsarten zu bestimmen. Es wurde zudem untersucht, ob anhand von Synsemantika (auch bekannt als Funktionswörter) wie Pronomen oder Konjunktionen eine Entscheidung über die Kommunikationsart einer Botschaft getroffen werden kann. Du-Botschaften wurden, übereinstimmend mit ihrer zugeschriebenen negativen Auswirkung auf Kommunikation, als schädlich in den durchgeführten Untersuchungen identifiziert. Entgegen der zugeschriebenen positiven Auswirkung von Ich-Botschaften, wurde bei diesen ebenfalls eine schädlich Wirkung festgestellt. Eine klare Aussage über die Relevanz von Synsemantika konnte anhand der Ergebnisse nicht getroffen werden. Weitere charakteristische Worte konnten nicht festgestellt werden. Die Ergebnisse deuten darauf hin, dass ein anderes Modell textliche Diskussionen potentiell besser abbilden könnte.
This habilitation thesis collects works addressing several challenges on handling uncertainty and inconsistency in knowledge representation. In particular, this thesis contains works which introduce quantitative uncertainty based on probability theory into abstract argumentation frameworks. The formal semantics of this extension is investigated and its application for strategic argumentation in agent dialogues is discussed. Moreover, both the computational as well as the meaningfulness of approaches to analyze inconsistencies, both in classical logics as well as logics for uncertain reasoning is investigated. Finally, this thesis addresses the implementation challenges for various kinds of knowledge representation formalisms employing any notion of inconsistency tolerance or uncertainty.
Politische und gesellschaftliche Polarisierung ist ein interessantes Phänomen, über dessen Auswirkungen viele unterschiedliche, zum Teil auch gegensätzliche, Theorien existieren.
Polarisierung wird in der Literatur mit unterschiedlichen Methoden gemessen. Die vorliegende Arbeit gibt einen Überblick über existierende Polarisierungsmaße und es werden zwei neuartige Maße aus dem Gebiet der spektralen Graphentheorie vorgestellt. Anschließend werden die bekannten und die neu entwickelten Maße auf den LiquidFeedback-Datensatz der Piratenpartei Deutschland angewandt. Als Ergebnis lässt sich festhalten, dass die Maße teilweise zu unterschiedlichen Ergebnisse kommen. Dies liegt darin begründet, dass nicht alle Maße das Gleiche messen. Um zu verstehen was die einzelnen Maße aussagen, werden wesentliche Eigenschaften von Polarisierungsmaßen herausgearbeitet und es wird für jedes Maß dargelegt, welche Eigenschaften es erfüllt. Die angesprochenen Polarisierungsmaße beziehen sich auf die Entwicklung der Polarisierung zwischen Usern des LiquidFeedback-Systems. Bei der Betrachtung von einzelnen Personen und Abstimmungen fiel unter anderem auf, dass polarisierende Personen mehr Macht durch
Delegationen besitzen als die restlichen Personen und dass polarisierte Vorschläge circa doppelt so häufig umgesetzt werden.
Viele Menschen kommunizieren und interagieren zunehmend über soziale Online-Netzwerke wie Twitter oder Facebook, oder tauschen Meinungen mit Freunden oder auch Fremden aus. Durch die zunehmende Verfügbarkeit des Internets wird auch Wissen für immer mehr Menschen offen verfügbar gemacht. Beispiele hierfür sind die Online-Enzyklopädie Wikipedia oder auch die vielfältigen Informationen in diversen Webforen und Webseiten. Diese zwei Netzwerkkategorien - Soziale Netzwerke und Wissensnetzwerke - verändern sich sehr schnell. Fast sekündlich befreunden sich neue Nutzer in sozialen Netzwerken und Wikipedia-Artikel werden überarbeitet und neu mit anderen Artikeln verlinkt. Diese Änderungen an der Verlinkung von Menschen oder Wissensbausteinen folgen bestimmten strukturellen Regeln und Charakteristiken, die weit weniger zufällig sind als man zunächst annehmen würde.
Das Ziel dieser Doktorarbeit ist es, drei charakteristische Verlinkungsmuster in diesen zwei Netzwerkkategorien vorherzusagen: das Hinzufügen von neuen Verlinkungen, das Entfernen bestehender Verbindungen und das Vorhandensein von latent negativen Verlinkungen. Zunächst widmen wir uns dem relativ neuen Problem der Vorhersage von Entlinkungen in einem Netzwerk. Hierzu gibt es zahlreiche soziologische Vorarbeiten, die nahelegen, dass die Ursachen zur Entstehung von Beziehungsabbrüchen komplementär zu den Gründen für neue Beziehungen sind. Obwohl diese Arbeiten eine strukturelle Ähnlichkeit der Probleme vermuten lassen, zeigen wir, dass beide Probleme nicht komplementär zueinander sind. Insbesondere zeigen wir, dass das dynamische Zusammenspiel von neuen Verlinkungen und Entlinkungen in Netzwerken durch die vier Zustände des Wachstums, des Zerfalls, der Stabilität und der Instabilität charakterisiert ist. Für Wissensnetzwerke zeigen wir, dass die Vorhersagbarkeit von Entlinkungen deutlich verbessert wird, wenn zeitliche Informationen wie der Zeitpunkt von einzelnen Netzwerkergeignissen mit genutzt werden. Wir präsentieren und evaluieren hierfür insgesamt vier verschiedene Strategien, die von zeitlichen Informationen Gebrauch machen. Für soziale Netzwerke analysieren wir, welche strukturellen Einflussfaktoren zur Entstehung und Löschung von Links zwischen Benutzern in Twitter indikativ sind. Auch hier zeigt sich, dass zeitliche Informationen darüber, dass eine Kante schon einmal gelöscht wurde, die Vorhersagbarkeit von Verlinkungen und insbesondere Entlinkungen enorm verbessert. Im letzten Teil der Doktorarbeit zeigen wir, wie negative Beziehungen (beispielsweise Misstrauen oder Feindschaft) aus positiven Beziehungen zwischen Nutzern (etwa Vertrauen und Freundschaft) abgeleitet werden können. Dies ist besonders relevant für Netzwerke, in denen nur positive Beziehungen kenntlich gemacht werden können. Für dieses Szenario zeigen wir, wie latent negative Beziehungen zwischen Nutzern dennoch erkannt werden können.