Christian Wolff

Michael Achmann-Denkler, Jakob Fehle, Mario Haim et al.

This study investigates the automated classification of Calls to Action (CTAs) within the 2021 German Instagram election campaign to advance the understanding of mobilization in social media contexts. We analyzed over 2,208 Instagram stories and 712 posts using fine-tuned BERT models and OpenAI's GPT-4 models. The fine-tuned BERT model incorporating synthetic training data achieved a macro F1 score of 0.93, demonstrating a robust classification performance. Our analysis revealed that 49.58% of Instagram posts and 10.64% of stories contained CTAs, highlighting significant differences in mobilization strategies between these content types. Additionally, we found that FDP and the Greens had the highest prevalence of CTAs in posts, whereas CDU and CSU led in story CTAs.

Nils Constantin Hellwig, Jakob Fehle, Markus Bink et al.

We present GERestaurant, a novel dataset consisting of 3,078 German language restaurant reviews manually annotated for Aspect-Based Sentiment Analysis (ABSA). All reviews were collected from Tripadvisor, covering a diverse selection of restaurants, including regional and international cuisine with various culinary styles. The annotations encompass both implicit and explicit aspects, including all aspect terms, their corresponding aspect categories, and the sentiments expressed towards them. Furthermore, we provide baseline scores for the four ABSA tasks Aspect Category Detection, Aspect Category Sentiment Analysis, End-to-End ABSA and Target Aspect Sentiment Detection as a reference point for future advances. The dataset fills a gap in German language resources and facilitates exploration of ABSA in the restaurant domain.

Maximilian Balluff, Peter Mandl, Christian Wolff

Cover songs are alternate versions of a song by a different artist. Long being a vital part of the music industry, cover songs significantly influence music culture and are commonly heard in public venues. The rise of online music platforms has further increased their prevalence, often as background music or video soundtracks. While current automatic identification methods serve adequately for original songs, they are less effective with cover songs, primarily because cover versions often significantly deviate from the original compositions. In this paper, we propose a novel method for cover song detection that utilizes the lyrics of a song. We introduce a new dataset for cover songs and their corresponding originals. The dataset contains 5078 cover songs and 2828 original songs. In contrast to other cover song datasets, it contains the annotated lyrics for the original song and the cover song. We evaluate our method on this dataset and compare it with multiple baseline approaches. Our results show that our method outperforms the baseline approaches.

Norbert Zsak, Christian Wolff

During the last years, many improvements were made to the hardware capability of mobile devices. As mobile software also became more interactive and data processing intensive, the increased power demand could not be compensated by the improvements on battery technology. Adaptive systems can help to balance the demand of applications with the limitations of battery resources. For effective systems, the influence of multimedia quality on power consumption of the components of mobile devices needs to be better understood. In this paper, we analyze the impact of video quality and wireless network type on the energy consumption of a mobile device. We have found that the additional power consumption is up to 38% higher when a movie is played over a WiFi network instead from internal memory and 64% higher in case of a mobile network (3G). We have also discovered that a higher movie quality not only affects the power consumption of the CPU but also the power consumption of the WiFi unit by up to 58% and up to 72% respectively on mobile networks.

Andreas Bauer, Christian Wolff

Web 2.0 applications like Twitter or Facebook create a continuous stream of information. This demands new ways of analysis in order to offer insight into this stream right at the moment of the creation of the information, because lots of this data is only relevant within a short period of time. To address this problem real time search engines have recently received increased attention. They take into account the continuous flow of information differently than traditional web search by incorporating temporal and social features, that describe the context of the information during its creation. Standard approaches where data first get stored and then is processed from a peristent storage suffer from latency. We want to address the fluent and rapid nature of text stream by providing an event based approach that analyses directly the stream of information. In a first step we want to define the difference between real time search and traditional search to clarify the demands in modern text filtering. In a second step we want to show how event based features can be used to support the tasks of real time search engines. Using the example of Twitter we present in this paper a way how to combine an event based approach with text mining and information filtering concepts in order to classify incoming information based on stream features. We calculate stream dependant features and feed them into a neural network in order to classify the text streams. We show the separative capabilities of event based features as the foundation for a real time search engine.