We provide a german corpus consisting of 7,061 posts authored by users of social media platforms. A group of volunteers annotated each post according to hatespeech and misogynistic/misogynous hatespeech in a binary fashion. The interrater reliability over all annotators according to Fleiss’ Kappa is 0.6409 for hatespeech and 0.8258 for misogynistic hatespeech. Furthermore, baseline measurements with machine learning based text classification with BERT are presented. Initial experiments with the corpus achieve macro average F1-scores up to 0.79 for hatespeech and 0.75 for misogynistic hatespeech. The dataset of the corpus on German Misogynistic Hatespeech Posts (GMHP7k) is publicly available.

Efficiently synchronizing data with external sources, such as social media feeds, while minimizing well-timed requests is a challenge in various domains. This research investigates prediction algorithms for determining appropriate update intervals for Facebook and Twitter feeds, considering metrics such as the delay (time between a post's publication and retrieval) and requests per post. Due to variations in update intervals, different algorithms yield diverse results. Selecting the most suitable algorithm for each feed is a time-consuming but crucial task for achieving optimal resource usage. We propose three strategies for algorithm selection: baseline (using a single algorithm per feed), optimum (calculating the best algorithm for each feed), and classification (identifying algorithms through classification). Real-world data from Facebook and Twitter are used to evaluate the strategies, comprehensively assessing their strengths and weaknesses. Findings demonstrate that the strategy optimum identifies the best algorithms, while the strategy classification selects fairly good algorithms at significantly reduced computational effort.

Complex objects are often described by several attributes of arbitrary types. To calculate the similarity between complex objects, we propose the CoSim process, which provides a composed similarity function. CoSim applies existing similarity functions for each data type of the object. To calculate the overall similarity, we weight these type-specific similarities. We demonstrate the practical application of our approach by using a simple example of objects. The example involves numerical and categorical attributes. However, our presented idea is applicable to objects with attributes of arbitrary types.

The synchronization of data against external sources with as few well-timed requests as possible is a challenge in several domains, that also applies to online social networks (OSNs). This paper examines algorithms that can be used to predict appropriate update intervals for feeds on Facebook and Twitter. The metrics to be optimized are the delay, meaning the time between publication and retrieval of the posts, as well as the requests per post. The approaches examined include static and adaptive algorithms as well as Poisson processes. The different strategies are first described, then applied to real-world data from Facebook and Twitter to finally compare and discuss the measurements. The various algorithms have different strengths and weaknesses. Hence, we show that with Poisson processes, the most fitting update intervals can be found, which keep both the post delay and the number of requests in a good ratio.

There have been numerous studies that have examined the performance of distribution frameworks. Most of these studies deal with the processing of large amounts of data. This work compares two of these frameworks for their ability to implement CPU-intensive distributed algorithms. As a case study for our experiments we used a simple but computationally intensive puzzle. To find all solutions using brute-force search, 15! permutations had to be calculated and tested against the solution rules. Our experimental application was implemented in the Java programming language using a simple algorithm and having two distributed solutions with the paradigms MapReduce (Apache Hadoop) and RDD (Apache Spark). The implementations were benchmarked in Amazon-EC2/EMR clusters for performance and scalability measurements, where the processing time of both solutions scaled approximately linearly. However, according to our experiments, the number of tasks, hardware utilization and other aspects should also be taken into consideration when assessing scalability. The comparison of the solutions with MapReduce (Apache Hadoop) and RDD (Apache Spark) under Amazon EMR showed that the processing time measured in CPU minutes with Spark was up to 30 % lower, while the performance of Spark especially benefits from an increasing number of tasks. Considering the efficiency of using the EC2 resources, the implementation via Apache Spark was even more powerful than a comparable multithreaded Java solution.