Open Access

Evaluating another person´s personality is an essential part of human life. How an individual reacts to a certain trigger, let it be a statement, strongly depends on his personality. Therefore, knowledge about the personality of a conversational counterpart is crucial to predict how he or she will react to a question or an answer. Personality is commonly understood as ´patterns of thought, emotion, and behavior that are relatively consistent over time and across situations´ (Funder 2012). If personality is as aforementioned defined as stable ´over time and across situations´, then it has to be differentiated from the character, which might change as an actor plays a role. A large proportion of an individual´s outer behavior can be explained by the inner personality. The outer behavior as a result of the personality determines various socio-demographic attributes, like job satisfaction (Furnham et al. 2002), the success of romantic relationships (Noftle, Shaver 2006), job performance (Barrik, Mount 1991) or high income, conservative political attitudes, early life adjustment to challenges, and social relationships (Soldz, Vaillant 1999). Humans can infer another person´s personality pretty precise. A first impression like a short video in many cases is enough to asses a personality (Carney et al. 2007). However, personality assessment is not limited to the social-cognitive domain of human brains - machine learning models attempt to predict personalities as well, or even better than humans. The internet provides a vast amount of data regarding personal information about its users - to so-called digital footprint. Especially social networks offer personal data in a very condensed form, the social-media footprint. Social media networks, which are online platforms, where people create a profile of themselves and communicate with other users or artificial persons like newspaper, offer a wide range of personal data to the broad community, as well as the network and its developers. In the year 2014 49.7 % of the German internet participated in social media networks (Statistisches Bundesamt 3/16/2015) with an upward trend. Furthermore, social media networks, like Facebook, provide the possibility to ´like´ something, which means at first: the user starts to follow a certain page and therefore receives updates and messages from the page and secondly: that the user publicly declares that he or she likes the page, visible to other users. However, it has been shown that the profile of a social network user indeed reflects the individual user and his personality and not an ´idealized´ version of 5 themselves (Back et al. 2010). Hence, these profiles seem to be unbiased, or at least as biased as the personality tests themselves. On the other side are the Facebook pages. A page in this case can be related to anything that a user started, let it be a political attitude, an artificial person, a company or a special kind of food. Any page can be created, and every user can give it a ´Like´. Facebook, as the biggest social media network as of today (Statista 2017) offers the possibility to collect data about a user´s Facebook likes, if the user agrees to the request. Due to the generic nature of Facebook likes and the relevance of personality assessment as a crucial part of social living, this paper focuses onto machine personality prediction based on Facebook likes. However, listening to music from a certain group in a web browser or reading a certain online newspaper can be easily translated into the Facebook like analogy and vice versa, which means that findings from this study are unlikely limited to the domain of Facebook likes.

Die Agenda 2030 der Vereinten Nationen und diverse daraus abgeleitete Nachhaltigkeitsstrategien wie beispielsweise die Deutsche Nachhaltigkeitsstrategie (Neuauflage 2016) setzen einen zeitlich fest definierten Rahmen bis in das Jahr 2030. Für die Umsetzung dieser Strategien sind zwei Aspekte entscheidend - ein fundiertes inhaltliches Verständnis von politischer Strategie und ein darauf aufbauendes praxistaugliches Instrument, das den politischen Strategen bei einer erfolgreichen Umsetzung und Implementierung einer Strategie unterstützt. Diese Arbeit möchte zu beiden Aspekten einen Beitrag leisten. Zum einen soll aus der Zusammenführung von Theorie und Praxis die Natur des inhaltlichen politischen Strategiebegriffs erforscht werden und zum anderen soll darauf aufbauend ein praxis- und erfahrungsorientiertes Anwendungsmodell entwickelt werden, das typische potentielle Herausforderungen und entsprechende potentielle Handlungsoptionen je Strategieabschnitt benennt. Durch die Subsumierung von Erfahrungswissen unterstützt das Modell dabei vor allem das praktische Urteilsvermögen des politischen Strategen bei der Erreichung politikstrategischer Ziele. Um zu dem praxisrelevanten Anwendungsmodell zu gelangen ist es notwendig, sich zuvor Klarheit über die besonderen Anforderungen und Herausforderungen des speziellen politischen Strategiebegriffs zu verschaffen. Die politische Umwelt mit ihren spezifischen Mechanismen unterscheidet sich fundamental von den bisher bekannten Strategiefeldern Militär und Betriebswirtschaft. Da eine Strategie immer die konkrete Umweltsituation und die individuellen Situationsmerkmale berücksichtigen muss, ist - wie diese Arbeit zeigen wird - eine simple Übertragung strategischer Allgemeinplätze aus Militär und Ökonomie von vornherein zum Scheitern verurteilt.

The emission of anthropogenic trace substances into the aquatic environment continuously poses challenges to water suppliers. The contamination of raw waters with organic trace substances requires complex water treatment processes to secure drinking water quality. The routine monitoring of these raw waters as well as the behavior and fate of organic trace substances during different treatment processes is of great interest to recognize and counter potential dangers at an early stage. Chromatographic separation techniques coupled to triple quadrupole mass spectrometers are conventionally used for the reliable monitoring of traces of known polar substances. However, such analytical techniques usually fail to recognize unknown compounds. This weakness presents a serious restriction with regard to the monitoring of treatment processes, since transformation products are often not - or not sufficiently - characterized and are thus only detected sporadically. Non-target screening using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) allows the detection of thousands of compounds within a single run and covers known as well as unknown substances. Compared to the established analytical techniques, this is a decisive advantage for the monitoring of raw and process waters during water treatment. While the analytical technique LC-HRMS has undergone significant developments in recent years, the algorithms for data processing reveal clear weaknesses. This dissertation therefore deals with reliable processing strategies for LC-HRMS data. The first part of this work seeks to highlight the problematics of false positive and false negative findings. Based on repeated measurements, various strategies of data processing were assessed with regard to the repeatability of the results. To ensure that real peaks were barely or not removed by the filtering procedure, samples were spiked with isotope-labeled standards. The results emphasize that the processing of sample triplicates results in sufficient repeatability and that the signal fluctuation across the triplicates emerged as a powerful filtering criteria. The number of false positives and false negatives could be significantly reduced by the developed strategies which consequently improve the validity of the data. The second part of this thesis addresses the development of processing strategies particularly aimed at assessing water treatment processes. The detected signals were tracked across the treatment process and classified based on their fold changes. A more reliable signal classification was achieved by implementing a recursive integration approach. Special integration algorithms allow a reliable signal classification even though the signal to be compared was below the intensity threshold. Different combinations of replicates of process influents and effluents were processed for evaluating the repeatability. The good repeatability was indicated by the results of both the plausibility checks and the ozonation process (ozonation of pretreated river water) and thus points to high reliability. The developed procedure enables the assessment of water treatment processes based on the changes in the pattern of all detected signals and offers a more comprehensive picture of the treatment efficiency. Particularly with regard to transformation products, existing knowledge gaps can be reduced by this approach, albeit the entire variety of chemicals cannot be covered completely. The applicability of the developed strategies to real world applications is demonstrated in the last part of this work. Besides the prioritization of the generated results, the main focus was the identification of recognized compounds. The developed strategies clearly improve the validity of the underlying data. The combination of LC-HRMS analysis with reliable processing strategies opens up multiple possibilities for a more comprehensive monitoring of water resources and for the assessment of water treatment processes. The processing strategies and validation concepts may be easily transferred to other research fields.