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.

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.

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.

Ontologies are valuable tools for knowledge representation and important building blocks of the Semantic Web. They are not static and can change over time. Changing an ontology can be necessary for various reasons: the domain that is represented by an ontology can change or an ontology is reused and must be adapted to the new context. In addition, modeling errors could have been introduced into the ontology which must be found and removed. The non-triviality of the change process has led to the emerge of ontology change as an own field of research. The removal of knowledge from ontologies is an important aspect of this change process, because even the addition of new knowledge to an ontology potentially requires the removal of older, conflicting knowledge. Such a removal must be performed in a thought-out way. A naïve change of concepts within the ontology can easily remove other, unrelated knowledge or alter the semantics of concepts in an unintended way [2]. For these reasons, this thesis introduces a formal operator for the fine-grained retraction of knowledge from EL concepts which is partially based on the postulates for belief set contraction and belief base contraction [3, 4, 5] and the work of Suchanek et al. [6]. For this, a short introduction to ontologies and OWL 2 is given and the problem of ontology change is explained. It is then argued why a formal operator can support this process and why the Description Logic EL provides a good starting point for the development of such an operator. After this, a general introduction to Description Logic is given. This includes its history, an overview of its applications and common reasoning tasks in this logic. Following this, the logic EL is defined. In a next step, related work is examined and it is shown why the recovery postulate and the relevance postulate cannot be naïvely employed in the development of an operator that removes knowledge from EL concepts. Following this, the requirements to the operator are formulated and properties are given which are mainly based on the postulates for belief set and belief base contraction. Additional properties are developed which make up for the non-applicability of the recovery and relevance postulates. After this, a formal definition of the operator is given and it is shown that the operator is applicable to the task of a fine-grained removal of knowledge from EL concepts. In a next step, it is proven that the operator fulfills all the previously defined properties. It is then demonstrated how the operator can be combined with laconic justifications [7] to assist a human ontology editor by automatically removing unwanted consequences from an ontology. Building on this, a plugin for the ontology editor Protégé is introduced that is based on algorithms that were derived from the formal definition of the operator. The content of this work is then summarized and a final conclusion is drawn. The thesis closes with an outlook into possible future work.

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.