Im echten (analogen) Leben sind wir eher selten offenen Beleidigungen oder Hass ausgesetzt. Im Internet hingegen lesen wir deutlich häufiger wütende, hasserfüllte Kommentare, Snaps, Tweets und Posts gegen uns oder gegen andere. Für diesen Hass im Netz gibt es den Begriff "Hate Speech". Hate Speech ist ein Phänomen dem rund zwei Drittel (67 Prozent) der Onliner schon einmal begegnet sind, wie eine forsa-Umfrage im Auftrag der Landesanstalt für Medien NRW (LfM) zeigt.

What about Hate Speech

The smart home is considered as an essential domain in Internet of Things (IoT) applications, it is an interconnected home where all types of things interact with each other via the Internet. This helps to automate the home by making it smart and interconnected. However, at the same time, it raises a great concern of the privacy and security for the users due to its capability to be controlled remotely. Hence, the rapid technologically growth of IoT raises abundant challenges such as how to provide the home users with safe and secure services keeping privacy in the account and how to manage the smart home successfully under the controlled condition to avoid any further secrecy or theft of personal data. A number of the research papers are available to address these critical issues, researchers presented different approaches to overcome these stated issues. This research review will analyze smart home approaches, challenges and will suggest possible solutions for them and illustrate open issues that still need to be addressed.

A review on smart home present state and challenges: linked to context-awareness internet of things (IoT)

Machine learning is recently used to detect hate speech and other forms of abusive language in online platforms. However, a notable weakness of machine learning models is their vulnerability to bias, which can impair their performance and fairness. One type is annotator bias caused by the subjective perception of the annotators. In this work, we investigate annotator bias using classification models trained on data from demographically distinct annotator groups. To do so, we sample balanced subsets of data that are labeled by demographically distinct annotators. We then train classifiers on these subsets, analyze their performances on similarly grouped test sets, and compare them statistically. Our findings show that the proposed approach successfully identifies bias and that demographic features, such as first language, age, and education, correlate with significant performance differences.

/pdf/identifying-and-measuring-annotator-bias-based-on-annotators-1gbxzox9gy.pdf

Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics

Analysis of subjective texts like offensive content or hate speech is a great challenge, especially regarding annotation process. Most of current annotation procedures are aimed at achieving a high level of agreement in order to generate a high quality reference source. However, the annotation guidelines for subjective content may restrict the annotators’ freedom of decision making. Motivated by a moderate annotation agreement in offensive content datasets, we hypothesize that personalized approaches to offensive content identification should be in place. Thus, we propose two novel perspectives of perception: group-based and individual. Using demographics of annotators as well as embeddings of their previous decisions (annotated texts), we are able to train multimodal models (including transformer-based) adjusted to personal or community profiles. Based on the agreement of individuals and groups, we experimentally showed that annotator group agreeability strongly correlates with offensive content recognition quality. The proposed personalized approaches enabled us to create models adaptable to personal user beliefs rather than to agreed offensiveness understanding. Overall, our individualized approaches to offensive content classification outperform classic data-centric methods that generalize offensiveness perception and it refers to all six tested models. Additionally, we developed requirements for annotation procedures, personalization and content processing to make the solutions human-centered.

Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach

One challenge that social media platforms are facing nowadays is hate speech. Hence, automatic hate speech detection has been increasingly researched in recent years - in particular with the rise of deep learning. A problem of these models is their vulnerability to undesirable bias in training data. We investigate the impact of political bias on hate speech classification by constructing three politically-biased data sets (left-wing, right-wing, politically neutral) and compare the performance of classifiers trained on them. We show that (1) political bias negatively impairs the performance of hate speech classifiers and (2) an explainable machine learning model can help to visualize such bias within the training data. The results show that political bias in training data has an impact on hate speech classification and can become a serious issue.

/pdf/impact-of-politically-biased-data-on-hate-speech-1z48sgyemc.pdf

Impact of politically biased data on hate speech classification

A challenge that many online platforms face is hate speech or any other form of online abuse. To cope with this, hate speech detection systems are developed based on machine learning to reduce manual work for monitoring these platforms. Unfortunately, machine learning is vulnerable to unintended bias in training data, which could have severe consequences, such as a decrease in classification performance or unfair behavior (e.g., discriminating minorities). In the scope of this study, we want to investigate annotator bias — a form of bias that annotators cause due to different knowledge in regards to the task and their subjective perception. Our goal is to identify annotation bias based on similarities in the annotation behavior from annotators. To do so, we build a graph based on the annotations from the different annotators, apply a community detection algorithm to group the annotators, and train for each group classifiers whose performances we compare. By doing so, we are able to identify annotator bias within a data set. The proposed method and collected insights can contribute to developing fairer and more reliable hate speech classification models.

/pdf/investigating-annotator-bias-with-a-graph-based-approach-2y041bhuug.pdf

Investigating Annotator Bias with a Graph-Based Approach.

Over the last few years mobile technology has gained enormous growth in the field of human-computer interaction as it became an essential part of our society's everyday life. Specific mobile characteristics such as availability, computational power, or high-resolution displays make these devices very useful, even in a business environment. However, usability plays a crucial role when it comes to software design for human-computer interaction with mobile devices. In order to achieve a high level of usability in user interfaces of application software, it is essential to ensure usability quality during the development process. Therefore, we have developed a specific questionnaire for evaluating the usability of mobile business apps as well as a corresponding web-based software tool for simplifying the assessment. In this paper, we follow a design science research approach and evaluate our designed artifacts in expert interviews where we demonstrate the utility and applicability of the questionnaire and tool.

/pdf/enhanced-human-computer-interaction-for-business-moznrjeetq.pdf

Enhanced Human-Computer Interaction for Business Applications on Mobile Devices: A Design-Oriented Development of a Usability Evaluation Questionnaire

Smart homes have gained attraction for several years in people's daily lives and are still increasing. They provide a fruitful foundation for the creation of social network services as they escort habits and regular activities of people at home, along their way or even at work. Smart homes and their current development stage are not only able to support people by intelligent functionality but also to predict the behavior or even the interaction with the social network of a person. For that, smart home technology makes strong use of big data techniques that help to master the vast amount of data generated continuously. In this paper we conduct a literature research that investigates the current state-of-the-art of smart homes and the related generation of social network services in a big data driven environment. We reveal future research opportunities that emerge from the combination of mastering big data in smart homes and, hence, provide design recommendations for social network services.

/pdf/enrichment-of-smart-home-services-by-integrating-social-3cyuim8lvq.pdf

Maximilian Wich

Papers

Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics

Impact of politically biased data on hate speech classification

Investigating Annotator Bias with a Graph-Based Approach.

Enhanced Human-Computer Interaction for Business Applications on Mobile Devices: A Design-Oriented Development of a Usability Evaluation Questionnaire

Enrichment of Smart Home Services by Integrating Social Network Services and Big Data Analytics