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Classification, prediction and evaluation of graph neural networks on online social media platforms
(2024)
The vast amount of data generated on social media platforms have made them a valuable source of information for businesses, governments and researchers. Social media data can provide insights into user behavior, preferences, and opinions. In this work, we address two important challenges in social media analytics. Predicting user engagement with online content has become a critical task for content creators to increase user engagement and reach larger audiences. Traditional user engagement prediction approaches rely solely on features derived from the user and content. However, a new class of deep learning methods based on graphs captures not only the content features but also the graph structure of social media networks.
This thesis proposes a novel Graph Neural Network (GNN) approach to predict user interaction with tweets. The proposed approach combines the features of users, tweets and their engagement graphs. The tweet text features are extracted using pre-trained embeddings from language models, and a GNN layer is used to embed the user in a vector space. The GNN model then combines the features and graph structure to predict user engagement. The proposed approach achieves an accuracy value of 94.22% in classifying user interactions, including likes, retweets, replies, and quotes.
Another major challenge in social media analysis is detecting and classifying social bot accounts. Social bots are automated accounts used to manipulate public opinion by spreading misinformation or generating fake interactions. Detecting social bots is critical to prevent their negative impact on public opinion and trust in social media. In this thesis, we classify social bots on Twitter by applying Graph Neural Networks. The proposed approach uses a combination of both the features of a node and an aggregation of the features of a node’s neighborhood to classify social bot accounts. Our final results indicate a 6% improvement in the area under the curve score in the final predictions through the utilization of GNN.
Overall, our work highlights the importance of social media data and the potential of new methods such as GNNs to predict user engagement and detect social bots. These methods have important implications for improving the quality and reliability of information on social media platforms and mitigating the negative impact of social bots on public opinion and discourse.
Lust auf Verzicht
(2024)
Der globale Klimawandel nimmt weiter bedrohlich zu. Hitzewellen, Flutkatastrophen und Waldbrände gehören fast schon zum Alltag. Trotzdem ist die Bereitschaft in der deutschen Bevölkerung gering, selbst einen Beitrag zum Klimaschutz zu leisten. Das überrascht, denn in Umfragen bekennt sich immer eine große Mehrheit zum Klimaschutz. Allerdings haben wohl viele einen Klimaschutz im Kopf, der persönlich nichts kosten und nichts verändern darf.
Ingo Balderjahn setzt sich mit diesem Widerspruch aus verhaltenswissenschaftlicher Sicht auseinander und legt offen, warum viele weiterhin ungebremst verschwenderisch und klimaschädlich konsumieren. Andererseits gibt es durchaus Menschen, die deutlich weniger konsumieren, als sie sich finanziell leisten könnten. Diese Minderheit in Deutschland verzichtet freiwillig auf eher unnötige und kurzlebige Güter - ohne irgendetwas dabei zu entbehren. Im Gegenteil: Genügsame Konsumgewohnheiten stärken die persönliche Selbstbestimmung, Unabhängigkeit und Zufriedenheit.
The icosahedral non-hydrostatic large eddy model (ICON-LEM) was applied around the drift track of the Multidisciplinary Observatory Study of the Arctic (MOSAiC) in 2019 and 2020. The model was set up with horizontal grid-scales between 100m and 800m on areas with radii of 17.5km and 140 km. At its lateral boundaries, the model was driven by analysis data from the German Weather Service (DWD), downscaled by ICON in limited area mode (ICON-LAM) with horizontal grid-scale of 3 km.
The aim of this thesis was the investigation of the atmospheric boundary layer near the surface in the central Arctic during polar winter with a high-resolution mesoscale model. The default settings in ICON-LEM prevent the model from representing the exchange processes in the Arctic boundary layer in accordance to the MOSAiC observations. The implemented sea-ice scheme in ICON does not include a snow layer on sea-ice, which causes a too slow response of the sea-ice surface temperature to atmospheric changes. To allow the sea-ice surface to respond faster to changes in the atmosphere, the implemented sea-ice parameterization in ICON was extended with an adapted heat capacity term.
The adapted sea-ice parameterization resulted in better agreement with the MOSAiC observations. However, the sea-ice surface temperature in the model is generally lower than observed due to biases in the downwelling long-wave radiation and the lack of complex surface structures, like leads. The large eddy resolving turbulence closure yielded a better representation of the lower boundary layer under strongly stable stratification than the non-eddy-resolving turbulence closure. Furthermore, the integration of leads into the sea-ice surface reduced the overestimation of the sensible heat flux for different weather conditions.
The results of this work help to better understand boundary layer processes in the central Arctic during the polar night. High-resolving mesoscale simulations are able to represent temporally and spatially small interactions and help to further develop parameterizations also for the application in regional and global models.
Social institutions
(2024)
Social institutions are a system of behavioral and relationship patterns that are densely interwoven and enduring and function across an entire society. They order and structure the behavior of individuals in core areas of society and thus have a strong impact on the quality of life of individuals. Institutions regulate the following: (a) family and relationship networks carry out social reproduction and socialization; (b) institutions in the realm of education and training ensure the transmission and cultivation of knowledge, abilities, and specialized skills; (c) institutions in the labor market and economy provide for the production and distribution of goods and services; (d) institutions in the realm of law, governance, and politics provide for the maintenance of the social order; (e) while cultural, media, and religious institutions further the development of contexts of meaning, value orientations, and symbolic codes.
With Arctic ground as a huge and temperature-sensitive carbon reservoir, maintaining low ground temperatures and frozen conditions to prevent further carbon emissions that contrib-ute to global climate warming is a key element in humankind’s fight to maintain habitable con-ditions on earth. Former studies showed that during the late Pleistocene, Arctic ground condi-tions were generally colder and more stable as the result of an ecosystem dominated by large herbivorous mammals and vast extents of graminoid vegetation – the mammoth steppe. Characterised by high plant productivity (grassland) and low ground insulation due to animal-caused compression and removal of snow, this ecosystem enabled deep permafrost aggrad-ation. Now, with tundra and shrub vegetation common in the terrestrial Arctic, these effects are not in place anymore. However, it appears to be possible to recreate this ecosystem local-ly by artificially increasing animal numbers, and hence keep Arctic ground cold to reduce or-ganic matter decomposition and carbon release into the atmosphere.
By measuring thaw depth, total organic carbon and total nitrogen content, stable carbon iso-tope ratio, radiocarbon age, n-alkane and alcohol characteristics and assessing dominant vegetation types along grazing intensity transects in two contrasting Arctic areas, it was found that recreating conditions locally, similar to the mammoth steppe, seems to be possible. For permafrost-affected soil, it was shown that intensive grazing in direct comparison to non-grazed areas reduces active layer depth and leads to higher TOC contents in the active layer soil. For soil only frozen on top in winter, an increase of TOC with grazing intensity could not be found, most likely because of confounding factors such as vertical water and carbon movement, which is not possible with an impermeable layer in permafrost. In both areas, high animal activity led to a vegetation transformation towards species-poor graminoid-dominated landscapes with less shrubs. Lipid biomarker analysis revealed that, even though the available organic material is different between the study areas, in both permafrost-affected and sea-sonally frozen soils the organic material in sites affected by high animal activity was less de-composed than under less intensive grazing pressure. In conclusion, high animal activity af-fects decomposition processes in Arctic soils and the ground thermal regime, visible from reduced active layer depth in permafrost areas. Therefore, grazing management might be utilised to locally stabilise permafrost and reduce Arctic carbon emissions in the future, but is likely not scalable to the entire permafrost region.
Organic-inorganic hybrids based on P3HT and mesoporous silicon for thermoelectric applications
(2024)
This thesis presents a comprehensive study on synthesis, structure and thermoelectric transport properties of organic-inorganic hybrids based on P3HT and porous silicon. The effect of embedding polymer in silicon pores on the electrical and thermal transport is studied. Morphological studies confirm successful polymer infiltration and diffusion doping with roughly 50% of the pore space occupied by conjugated polymer. Synchrotron diffraction experiments reveal no specific ordering of the polymer inside the pores. P3HT-pSi hybrids show improved electrical transport by five orders of magnitude compared to porous silicon and power factor values comparable or exceeding other P3HT-inorganic hybrids. The analysis suggests different transport mechanisms in both materials. In pSi, the transport mechanism relates to a Meyer-Neldel compansation rule. The analysis of hybrids' data using the power law in Kang-Snyder model suggests that a doped polymer mainly provides charge carriers to the pSi matrix, similar to the behavior of a doped semiconductor. Heavily suppressed thermal transport in porous silicon is treated with a modified Landauer/Lundstrom model and effective medium theories, which reveal that pSi agrees well with the Kirkpatrick model with a 68% percolation threshold. Thermal conductivities of hybrids show an increase compared to the empty pSi but the overall thermoelectric figure of merit ZT of P3HT-pSi hybrid exceeds both pSi and P3HT as well as bulk Si.
Vergangenheit ist vergangen, Geschichte wird gemacht. An diesem Konstruktionsprozess sind nicht nur die historischen Akteur:innen und deren Quellen, sondern in besonderem Maße auch die Historiker:innen, die sich mit diesen auseinandersetzen, beteiligt. Sie sind es, die die Quellen erst zum Sprudeln bringen. Was dabei zutage tritt, ist somit in hohem Maße von den Forschenden selbst, von ihren Vorannahmen und Methoden aber auch von ihren sozialen, kulturellen und biografischen Prägungen abhängig. Das hier vorgestellte Prozessmodell versucht, diese als Einflussfaktoren zu fassen und sichtbar zu machen, um auf dieser Basis eine erweiterte wissenschaftliche (Selbst-)Reflexion zu ermöglichen.
This dissertation examines the lack of clarity in the scientific literature regarding gender and negotiation performance. It is often claimed that men negotiate better than women, yet it is simultaneously emphasized that results strongly depend on context. Through the use of qualitative methods such as content analysis and critical mixed-methods review, the research question: "Are women truly inferior negotiators compared to men?" is addressed. The study comprises a descriptive and an interpretive part. The descriptive section illuminates various interpretations of gender-specific negotiation theory among citing authors, with 67% arguing for a general superiority of men. However, given the high variance in gender-specific differences, the focus should instead be on the context-dependency of negotiation performance. Generalized statements can be made within contexts, but not across them. In the interpretive section, several factors contributing to this misinterpretation are highlighted, including discrepancies in the definition of negotiation performance and distortions in research communication.. From a scientific perspective, this study underscores the need for a nuanced sociological analysis and warns against the one-sided acceptance of inaccurate scientific interpretations. From a practical standpoint, it amplifies the voices of women affected by biased research paradigms. Overall, the dissertation clarifies the theory of gender-specific negotiation performance and advocates for the elimination of biases in scientific discourse.
Efficiently managing large state is a key challenge for data management systems. Traditionally, state is split into fast but volatile state in memory for processing and persistent but slow state on secondary storage for durability. Persistent memory (PMem), as a new technology in the storage hierarchy, blurs the lines between these states by offering both byte-addressability and low latency like DRAM as well persistence like secondary storage. These characteristics have the potential to cause a major performance shift in database systems.
Driven by the potential impact that PMem has on data management systems, in this thesis we explore their use of PMem. We first evaluate the performance of real PMem hardware in the form of Intel Optane in a wide range of setups. To this end, we propose PerMA-Bench, a configurable benchmark framework that allows users to evaluate the performance of customizable database-related PMem access. Based on experimental results obtained with PerMA-Bench, we discuss findings and identify general and implementation-specific aspects that influence PMem performance and should be considered in future work to improve PMem-aware designs. We then propose Viper, a hybrid PMem-DRAM key-value store. Based on PMem-aware access patterns, we show how to leverage PMem and DRAM efficiently to design a key database component. Our evaluation shows that Viper outperforms existing key-value stores by 4–18x for inserts while offering full data persistence and achieving similar or better lookup performance. Next, we show which changes must be made to integrate PMem components into larger systems. By the example of stream processing engines, we highlight limitations of current designs and propose a prototype engine that overcomes these limitations. This allows our prototype to fully leverage PMem's performance for its internal state management. Finally, in light of Optane's discontinuation, we discuss how insights from PMem research can be transferred to future multi-tier memory setups by the example of Compute Express Link (CXL).
Overall, we show that PMem offers high performance for state management, bridging the gap between fast but volatile DRAM and persistent but slow secondary storage. Although Optane was discontinued, new memory technologies are continuously emerging in various forms and we outline how novel designs for them can build on insights from existing PMem research.