In this study, we evaluate the impact of gender-biased data from German-language physician reviews on the fairness of fine-tuned language models. For two different downstream tasks, we use data reported to be gender biased and aggregate it with annotations. First, we propose a new approach to aspect-based sentiment analysis that allows identifying, extracting, and classifying implicit and explicit aspect phrases and their polarity within a single model. The second task we present is grade prediction, where we predict the overall grade of a review on the basis of the review text. For both tasks, we train numerous transformer models and evaluate their performance. The aggregation of sensitive attributes, such as a physician’s gender and migration background, with individual text reviews allows us to measure the performance of the models with respect to these sensitive groups. These group-wise performance measures act as extrinsic bias measures for our downstream tasks. In addition, we translate several gender-specific templates of the intrinsic bias metrics into the German language and evaluate our fine-tuned models. Based on this set of tasks, fine-tuned models, and intrinsic and extrinsic bias measures, we perform correlation analyses between intrinsic and extrinsic bias measures. In terms of sensitive groups and effect sizes, our bias measure results show different directions. Furthermore, correlations between measures of intrinsic and extrinsic bias can be observed in different directions. This leads us to conclude that gender-biased data does not inherently lead to biased models. Other variables, such as template dependency for intrinsic measures and label distribution in the data, must be taken into account as they strongly influence the metric results. Therefore, we suggest that metrics and templates should be chosen according to the given task and the biases to be assessed.

This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.

When it comes to increased digitization in the health care domain, privacy is a relevant topic nowadays. This relates to patient data, electronic health records or physician reviews published online, for instance. There exist different approaches to the protection of individuals’ privacy, which focus on the anonymization and masking of personal information subsequent to their mining. In the medical domain in particular, measures to protect the privacy of patients are of high importance due to the amount of sensitive data that is involved (e.g. age, gender, illnesses, medication). While privacy breaches in structured data can be detected more easily, disclosure in written texts is more difficult to find automatically due to the unstructured nature of natural language. Therefore, we take a detailed look at existing research on areas related to privacy protection. Likewise, we review approaches to the automatic detection of privacy disclosure in different types of medical data. We provide a survey of several studies concerned with privacy breaches in the medical domain with a focus on Physician Review Websites (PRWs). Finally, we briefly develop implications and directions for further research.

This paper deals with online customer reviews of local multi-service providers. While many studies investigate product reviews and online labour markets with service providers delivering intangible products “over the wire”, we focus on websites where providers offer multiple distinct services that can be booked, paid and reviewed online but are performed locally offline. This type of service providers has so far been neglected in the literature. This paper analyses reviews and applies sentiment analysis. It aims to gain new insights into local multi-service providers’ performance. There is a broad literature range presented with regard to the topics addressed. The results show, among other things, that providers with good ratings continue to perform well over time. We find that many positive reviews seem to encourage sales. On average, quantitative star ratings and qualitative ratings in the form of review texts match. Further results are also achieved in this study.

The ability to openly evaluate products, locations and services is an achievement of the Web 2.0. It has never been easier to inform oneself about the quality of products or services and possible alternatives. Forming one’s own opinion based on the impressions of other people can lead to better experiences. However, this presupposes trust in one’s fellows as well as in the quality of the review platforms. In previous work on physician reviews and the corresponding websites, it was observed that there occurs faulty behavior by some reviewers and there were noteworthy differences in the technical implementation of the portals and in the efforts of site operators to maintain high quality reviews. These experiences raise new questions regarding what trust means on review platforms, how trust arises and how easily it can be destroyed.

In this paper, we present an IoT architecture which handles stream sensor data of air pollution. Particle pollution is known as a serious threat to human health. Along with developments in the use of wireless sensors and the IoT, we propose an architecture that flexibly measures and processes stream data collected in real-time by movable and low-cost IoT sensors. Thus, it enables a wide-spread network of wireless sensors that can follow changes in human behavior. Apart from stating reasons for the need of such a development and its requirements, we provide a conceptual design as well as a technological design of such an architecture. The technological design consists of Kaa and Apache Storm which can collect air pollution information in real-time and solve various problems to process data such as missing data and synchronization. This enables us to add a simulation in which we provide issues that might come up when having our architecture in use. Together with these issues, we state r easons for choosing specific modules among candidates. Our architecture combines wireless sensors with the Kaa IoT framework, an Apache Kafka pipeline and an Apache Storm Data Stream Management System among others. We even provide open-government data sets that are freely available.