This paper describes a network reduction technique to reveal possibly hidden relational patterns in information diffusion networks of interlinked content published across different types of online media. Topic specific content items such as tweets (Twitter), web pages, or versions of Wikipedia articles can reference each other through hyperlinks, revisions, or retweet relationships, and thus, constitute a network that reflects the dissemination of information on the web. Beyond focusing on the structural linking of content items alone, the temporal aspect of information diffusion is explicitly taken into account by modelling the edge weight between two interlinked items according to the difference in their publication times. Non-negative matrix factorisation (NMF) is applied to decompose the resulting networks into groups of nodes occupying similar positions, which means that they have similar abilities to spread or receive information to or from other nodes. This allows for an easier observation of the basic underlying structure of cross-media information diffusion networks and their main information pathways. The utility of the approach and differences to other techniques will be demonstrated along two application scenarios related to two popular news stories and their dissemination in online media in 2016.

Web search is a crucial technology for the digital economy. Dominated by a few gatekeepers focused on commercial success, however, web publishers have to optimize their content for these gatekeepers, resulting in a closed ecosystem of search engines as well as the risk of publishers sacrificing quality. To encourage an open search ecosystem and offer users genuine choice among alternative search engines, we propose the development of an Open Web Index (OWI). We outline six core principles for developing and maintaining an open index, based on open data principles, legal compliance, and collaborative technology development. The combination of an open index with what we call declarative search engines will facilitate the development of vertical search engines and innovative web data products (including, e.g., large language models), enabling a fair and open information space. This framework underpins the EU‐funded project OpenWebSearch.EU, marking the first step towards realizing an Open Web Index.

We replicate and analyze the topic model which was commissioned to King’s College and Digital Science for the Research Evaluation Framework (REF 2014) in the United Kingdom: 6,638 case descriptions of societal impact were submitted by 154 higher-education institutes. We compare the Latent Dirichlet Allocation (LDA) model with Principal Component Analysis (PCA) of document-term matrices using the same data. Since topic models are almost by definition applied to text corpora which are too large to read, validation of the results of these models is hardly possible; furthermore the models are irreproducible for a number of reasons. However, removing a small fraction of the documents from the sample—a test for reliability—has on average a larger impact in terms of decay on LDA than on PCA-based models. The semantic coherence of LDA models outperforms PCA-based models. In our opinion, results of the topic models are statistical and should not be used for grant selections and micro decision-making about research without follow-up using domain-specific semantic maps.

Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing. The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time. We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs. Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.