Extracting and Connecting Scientific Knowledge from Texts

This work explores and evaluates text and graph mining methods for open domain concept and relation discovery in scientific literature. First results indicate that several different approaches have to be combined to detect a sufficient amount of concepts and meaningful relationships in an open domain corpus

Characterising Learners in Online Communities Based on Actor-Artefact Relations

Online communities are of huge interest in terms of learning and knowledge creation because of the potential to distribute knowledge among possibly large audience independently from time and place. In this context, various forms of online learning have developed over time ranging from small learning groups to massive open online courses (MOOCs) with thousands of participants. In order to support learning in those settings an increased understanding of specific characteristics of learners in online communities is necessary. Thus, dedicated means to gather valuable information from data produced in online learning environments have to be developed. This cumulative dissertation includes five publications aiming to make progress in this direction with a particular focus on the advancement of methods to analyse activity and interaction data of learners. The methodological foundation of the work is (social) network analysis, which provides a well-grounded set of methods for structural analysis of relational data. Network analysis is especially suited since the collected data about actors (in this thesis mostly learners) who create and consume digital content (artefacts) can be modelled as actor-artefact networks. Those actor-artefact networks denote the starting point of all analyses presented in this dissertation, which target different aspects of learning in online communities, in particular the usage of learning resources, emergence of interest profiles, and information exchange between learners. In the course of this work, stable artefacts that are not assumed to have changing content over time are distinguished from time-evolving dynamic artefacts (typically user generated content). In the case of stable artefacts, affiliations of learners to learning resources in online courses are analysed by identifying mixed clusters of learners and resources using network clustering algorithms. The evolution of these learner-resource clusters over time is investigated in detail leading to discoveries of typical resource access patterns that characterise learners regarding their interests in provided learning materials. The approach is further extended and combined with content analysis techniques to analyse thematic development in discussion forums. Discussion forums are also the subject of two other studies investigating information exchange between learners in MOOCs. The evolving discussion threads are considered as dynamically evolving artefacts that are used to extract social networks reflecting information exchange between forum users. These networks are analysed to uncover different roles of forum users with respect to their positions in the network. For this task different approaches are described that are capable of modelling structural characteristics of the information exchange network over time and further take discussion topics as additional information into account.Online-Gemeinschaften sind aufgrund der Möglichkeiten Wissen zeit- und ortsunabhängig unter einer großen Menge von Adressaten zu verbreiten von großem Interesse bezüglich Lernens und Wissenskonstruktion. In diesem Kontext haben sich über die Zeit verschiedene Formen des Online-Lernens entwickelt, von kleinen Lerngruppen bis zu “Massive Open Online Courses” (MOOCs) mit tausenden von Teilnehmern. Um das Lernen in diesen Bereichen zu unterstützen, ist ein besseres Verständnis spezifischer Charakteristiken von Lernenden in Online-Gemeinschaften notwendig. Um nützliche Informationen aus Daten zu gewinnen, die in Online-Umgebungen anfallen, sind dezidierte Methoden wichtig. Diese kumulative Dissertation beinhaltet Publikationen die auf Fortschritte in diesem Bereich abzielen. Ein besonderer Fokus liegt dabei auf der Weiterentwicklung von Methoden zur Analyse von Aktivitäts- und Interaktionsdaten von Lernern in Online-Gemeinschaften. Das methodische Fundament ist dabei die (Soziale) Netzwerkanalyse, welche fundierte Methoden zur strukturellen Analyse von relationalen Daten bereitstellt. Netzwerkanalyse ist besonders geeignet, da Daten über Akteure (hier meistens Lernende), die digitale Inhalte (Artefakte) erstellen und konsumieren, als Akteur-Artefakt-Netzwerk modelliert werden können. Solche Akteur-Artefakt-Netzwerke sind der Ausgangspunkt aller in dieser Dissertation vorgestellten Analysen, die auf verschiedene Aspekte des Lernens in Online-Gemeinschaften abzielen, insbesondere die Nutzung von Lernressourcen, die Entwicklung von Interessensprofilen und Wissensaustausch zwischen Lernenden. Im Verlauf dieser Arbeit wird zwischen stabilen Artefakten, die sich über die Zeit nicht verändern und sich über die Zeit entwickelnden dynamischen Artefakten (typischerweise Nutzergenerierte Inhalte) unterschieden. Im Fall von statischen Artefakten werden Affiliationen von Lernenden zu Lernressourcen in Online-Kursen untersucht, indem gemischte Cluster aus Lernenden und Lernressourcen mittels Netzwerk-Clusteringalgorithmen identifiziert werden. Die Evolution dieser Lerner-Ressourcen-Cluster wird eingehend untersucht, woraus Erkenntnisse über typische Ressourcennutzungsmuster gewonnen werden, die die Lernenden in Online-Gemeinschaften bezüglich ihrer Präferenzen zu Lernmaterialien charakterisieren. Dieser Ansatz wird zudem weiterentwickelt und mit Techniken der Inhaltsanalyse kombiniert, um thematische Entwicklungen in Diskussionsforen zu analysieren. Diskussionsforen sind auch Gegenstand zweier weiterer Studien, die den Austausch von Informationen zwischen Lernenden in MOOCs zu untersuchen. Die einzelnen Diskussionsstränge werden dabei als dynamische Artefakte angesehen, die dann dazu genutzt werden um soziale Netzwerke zu extrahieren, die den Informationsaustausch zwischen Lernenden abbilden. Diese Netzwerke werden dahingehend analysiert, unterschiedliche Rollen von Forumsnutzern bezüglich ihrer Position in dem Netzwerk zu identifizieren. Dazu werden verschiedene Ansätze vorgestellt, die die strukturellen Charakteristiken des Informationsaustauschnetzwerks über die Zeit darstellen, sowie Diskussionsthemen als zusätzliche Informationen berücksichtigen

Towards Open Domain Literature Based Discovery

Appeared in: Open Search Symposium 2021, 11-13 October 2021, CERN, Geneva, Switzerland

Gradual Network Sparsification and Georeferencing for Location-Aware Event Detection in Microblogging Services

Event detection in microblogging services such as Twitter has become a challenging research topic within the fields of social network analysis and natural language processing. Many works focus on the identification of general events with event types ranging from political news and soccer games to entertainment. However, in application contexts like crisis management, traffic planning, or monitoring people’s mobility during pandemic scenarios, there is a high need for detecting localisable physical events. To address this need, this paper introduces an extension of an existing event detection framework by combining machine learning-based geo-localisation of tweets and network analysis to reveal events from Twitter distributed in time and space. Gradual network sparsification is introduced to improve the detection events of different granularity and to derive a hierarchical event structure. Results show that the proposed method is able to detect meaningful events including their geo-locations. This constitutes a step towards using social media data to inform, for example, traffic demand models, inform about infection risks in certain places, or the identification of points of interest

Global and local community memberships for estimating spreading capability of nodes in social networks

The analysis of spreading processes within complex networks can offer many important insights for the application in contexts such as epidemics, information dissemination or rumours. Particularly, structural factors of the network which either contribute or hinder the spreading are of interest, as they can be used to control or predict such processes. In social networks, the community structure is especially relevant, as actors usually participate in different densely connected social groups which emerge from various contexts, potentially allowing them to inject the spreading process into many different communities quickly. This paper extends our recent findings on the community membership of nodes and how it can be used to predict their individual spreading capability (Krukowski & Hecking, 2020) by further evaluating it on additional networks (both real-world networks and artificially generated networks), while additionally introducing a new local measure to identify influential spreaders that—in contrast to most other measures, does not rely on knowledge of the global network structure. The results confirm our recent findings, showing that the community membership of nodes can be used as a predictor for their spreading capability, while also showing that especially the local measure proves to be a good predictor, effectively outperforming the global measure in many cases. The results are discussed with regard to real-world use cases, where knowledge of the global structure is often not given, yet a prediction regarding the spreading capability highly desired (e.g., contact-tracing apps)

Predicting Winning Regions in Parity Games via Graph Neural Networks

Solving parity games is a major building block for numerous applications in reactive program verification and synthesis. While they can be solved efficiently in practice, no known approach has a polynomial worst-case runtime complexity. We present a incomplete polynomial-time approach to determining the winning regions of parity games via graph neural networks. Our evaluation on 900 randomly generated parity games shows that this approach is effective and efficient in practice. It correctly determines the winning regions of ∼60% of the games in our data set and only incurs minor errors in the remaining ones. We believe that this approach can be extended to efficiently solve parity games as well

AngeLA: Putting the Teacher in Control of Student Privacy in the Online Classroom

Learning analytics (LA) is often considered as a means to improve learning and learning environments by measuring student behaviour, analysing the tracked data and acting upon the results. The use of LA tools implies recording and processing of student activities conducted on software platforms. This paper proposes a flexible, contextual and intuitive way to provide the teacher with full control over student activity tracking in online learning environments. We call this approach AngeLA, inspired by an angel guarding over LA privacy. AngeLA mimics in a virtual space the privacy control mechanism that works well in a physical room: if a person is present in a room, she is able to observe all activities happening in the room. AngeLA serves two main purposes: (1) it increases the awareness of teachers about the activity tracking and (2) provides an intuitive way to manage the activity tracking permissions. This approach can be applied to various learning environments and social media platforms. We have implemented AngeLA in Graasp, a social platform that fosters collaborative activities

Exploring the Relationship Between Social Networking Site Usage and Participation in Protest Activities

A methodological approach is developed for exploring the relationship between the use of social networking sites and participation in protest activities. Although a recent meta-analysis study demonstrated that there is a positive association between the two, little work examining this association further appears to have been published. The methodology proposed here studies the patterns of the relationship between nine social media and five types of protest activity using the techniques of multiple correspondence analysis, hierarchical cluster analysis and induction of decision rules. The results give insights into the relationship in different segments of individuals' profiles defined as non-activist, offline activist, social media user (two types) and online activist. Significantly, this last segment proves to be a small and heterogeneous group. The results also show that the proposed approach is useful for exploring the patterns of the relationship in a low-dimensional space. Limitations of the methodology and possible extensions are discussed