Online firestorms on Twitter are seemingly arbitrarily occurring outrages towards people, companies, media campaigns and politicians. Moral outrage can create an excessive collective aggressiveness against one single argument, one single word, or one action of a person resulting in hateful speech. With a collective “against the others” the negative dynamics often start. Using data from Twitter, we explored the starting points of several firestorm outbreaks. As a social media platform with hundreds of millions of users interacting in real-time on topics and events all over the world, Twitter serves as a social sensor for online discussions and is known for quick and often emotional disputes. The main question we pose in this article is whether we can detect the outbreak of a firestorm. Given 21 online firestorms on Twitter, the key questions regarding the anomaly detection are: 1) How can we detect changing points? 2) How can we distinguish the features that indicate a moral outrage? In this paper we examine these challenges developing a method to detect the point of change systematically spotting on linguistic cues of tweets. We are able to detect outbreaks of firestorms early and precisely only by applying linguistic cues. The results of our work can help detect negative dynamics and may have the potential for individuals, companies, and governments to mitigate hate in social media networks.

Linked Open Data (LOD) refers to freely available data on the WWW that are typically represented using Resource Description Framework (RDF). LOD is an invaluable source of rich and structured information, and enables a wide range of new applications, such as Social Network Analysis (SNA). In this paper, we address the extraction of social networks from LOD using SPARQL language, and we present various patterns to extract ego-centric networks. We also present two case studies: i) influence networks of intellectuals, and ii) co-acting networks, to demonstrate the applicability and usefulness of the approach.

All indoor positioning approaches face the challenge to deal with erroneous input data from sensors. Especially independent systems like dead reckoning rely on highly accurate input data from accelerometers and gyroscopes for an accurate prediction of the user's position. But with the widespread of affordable mobile devices equipped with low-cost sensors, the obtained input data is noisy and of poor quality. Since errors accumulate within a pedestrian dead reckoning (PDR) system, there is an inevitable need for recalibration on a regular basis. We propose a PDR system based on state-of-the-art particle filters, which is recalibrated using both anchor points and a pedestrian movement model. Our evaluation compares standard particle filters with a backtracking particle filter including information from indoor maps and our enhancements. We show that a combination of anchor point recalibration, error calculation of sensor bias, and a fine-tuned movement model can decrease the RMSE by 1.07 m.

A large-scale system hundreds of millions of people encounter every day is radio. While this “system” has a tremendous reach it also is technologically outdated. Technological constraints offer great protection of listeners' privacy but prevent radio stations from implementing modern business models like personalization and mining valuable user information, at the same time. This paper describes a fully distributed system that aims at overcoming current technological constraints in interconnecting radio stations and their listeners while retaining a comparable protection of sensitive user data. Namely, a peer-to-peer architecture with integrated data-mining-capabilities employing differential privacy is proposed. The system will offer personalizable radio programs to listeners and it will enable radio stations to gather valuable information about their listeners. Furthermore, this paper points out key challenges in deploying, bootstrapping and maintaining such a distributed system.

Alternative routes have found many applications in navigation scenarios. However, alternative routes have only been introduced recently for the indoor space due to the complexity of these environments. Furthermore, the number of alternative routes in buildings can be quite high. With this paper, we propose to organize sets of alternative routes by employing archetypal analysis on a feature space representation of routes and show results in which a set of hundreds of routes between the same start and end point has been compressed to only a few obviously different archetypal routes. Additionally, the framework allows for comparing routes with archetypes and with each other. This comparison does not reveal spatial similarity alone, but rather a measure of routes' similarity representing their inherent semantic character.