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AbstractThe governments' mitigation measures to fight the COVID-19 pandemic are unprecedented in our post-war history. For overcoming this crisis, citizens were expected to act in compliance with these measures in order to control the spread of the virus and keep public health systems functional. This call for protecting the public health at the same time confronted citizens with several and severe limitations of their democratic freedoms and rights: confinement, restriction on freedoms of movement, religion, specific provisions for public protest and finally also limitations to the right of education by school closures. This paper analyzes how citizens perceive the threat the COVID-19 pandemic and especially the mitigation measures posed for democracy. We assume that pandemic waves and pandemic fatigue have an impact on the perception of threat. To see the overall societal picture, we exploit a large-scale archive of online discourse on Twitter out of which we extract democracy-related discourse with the same temporal and geospatial coverage for our investigation. From that data source, we apply computational methods to extract time series data reflecting aggregated opinions and their evolution over time concerned with the correlation of attitudes towards democracy. We them move deeper using a longitudinal panel survey we conducted in November/December 2020, March/April 2021, and July/August 2021. to have a view of the relationship between citizens' socio-economic status and basic political attitudes. Our multi-method analysis bases on the German case and covers the period from December 2020 to August 2021.

Research on environmental justice comprises health and well-being aspects, as well as topics related to general social participation. In this research field, among others, there is a need for an integrated use of social science survey data and spatial science data, e.g. for combining demographic information from survey data with data on pollution from spatial data. However, for researchers it is challenging to link both data sources, because (1) the interdisciplinary nature of both data sources is different, (2) both underlie different legal restrictions, in particular regarding data privacy, and (3) methodological challenges arise regarding the use of geo-information systems (GIS) for the processing and analysis of spatial data.

In this article, we present an infrastructure of distributed web services which supports researchers in the process of spatial linking. The infrastructure addresses the challenges researchers have to face during that process. We present an example case study on the investigation of environmental inequalities with regards to income and land use hazards in Germany by using georeferenced survey data of the GESIS Panel and the German Socio-economic Panel (SOEP), and by using spatial data from the Monitor of Settlement and Open Space Development (IOER Monitor). The results show that increasing income of survey respondents is associated with less exposure to land-use-related environmental hazards in Germany.

TweetsKB is a public RDF corpus of anonymized data for a large collection of annotated tweets. The dataset currently contains data for nearly 3.0 billion tweets, spanning more than 9 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions and URLs are exposed in RDF using established RDF/S vocabularies. For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets. For a list of the previous dataset parts, example queries and more information see the TweetsKB's home page: https://data.gesis.org/tweetskb/.

TweetsKB is a public RDF corpus of anonymized data for a large collection of annotated tweets. The dataset currently contains data for nearly 3.0 billion tweets, spanning more than 9 years (February 2013 - August 2022). Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions and URLs are exposed in RDF using established RDF/S vocabularies. For the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets. For a list of the previous dataset parts, example queries and more information see the TweetsKB's home page: https://data.gesis.org/tweetskb/.

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [.] This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner democratizing this mass amount of knowledge." Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by 9 teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and ...