@article {28485,
	title = {The connectivity network underlying the German{\textquoteright}s Twittersphere: a testbed for investigating information spreading phenomena},
	journal = {Scientific Reports},
	volume = {12},
	year = {2022},
	month = {Jan-12-2022},
	publisher = {Nature Publishing Group},
	abstract = {Online social networks are ubiquitous, have billions of users, and produce large amounts of data. While platforms like Reddit are based on a forum-like organization where users gather around topics, Facebook and Twitter implement a concept in which individuals represent the primary entity of interest. This makes them natural testbeds for exploring individual behavior in large social networks. Underlying these individual-based platforms is a network whose {\textquotedblleft}friend{\textquotedblright} or {\textquotedblleft}follower{\textquotedblright} edges are of binary nature only and therefore do not necessarily reflect the level of acquaintance between pairs of users. In this paper,we present the network of acquaintance {\textquotedblleft}strengths{\textquotedblright} underlying the German Twittersphere. To that end, we make use of the full non-verbal information contained in tweet{\textendash}retweet actions to uncover the graph of social acquaintances among users, beyond pure binary edges. The social connectivity between pairs of users is weighted by keeping track of the frequency of shared content and the time elapsed between publication and sharing. Moreover, we also present a preliminary topological analysis of the German Twitter network. Finally, making the data describing the weighted German Twitter network of acquaintances, we discuss how to apply this framework as a ground basis for investigating spreading phenomena of particular contents.},
	doi = {10.1038/s41598-022-07961-3},
	url = {https://www.nature.com/articles/s41598-022-07961-3},
	author = {Daniel Thilo {Schroeder} and Langguth, Johannes and Burchard, Luk and Pogorelov, Konstantin and Pedro G. {Lind}}
}
@article {42552,
	title = {COVID-19 and 5G conspiracy theories: Long term observation of a digital wildfire},
	journal = {International Journal of Data Science and Analytics },
	year = {2022},
	publisher = { Springer},
	author = {Langguth, Johannes and Filkukova, Petra and Brenner, Stefan and Daniel Thilo {Schroeder} and Pogorelov, Konstantin}
}
@inproceedings {langedal_et_al:LIPIcs.SEA.2022.12,
	title = {Efficient Minimum Weight Vertex Cover Heuristics Using Graph Neural Networks},
	journal = {20th International Symposium on Experimental Algorithms (SEA 2022)},
	volume = {233},
	year = {2022},
	pages = {12:1{\textendash}12:17},
	publisher = {Schloss Dagstuhl {\textendash} Leibniz-Zentrum f{\"u}r Informatik},
	address = {Dagstuhl, Germany},
	isbn = {978-3-95977-251-8},
	issn = {1868-8969},
	doi = {10.4230/LIPIcs.SEA.2022.12},
	url = {https://drops.dagstuhl.de/opus/volltexte/2022/16546},
	author = {Langedal, Kenneth and Langguth, Johannes and Manne, Fredrik and Daniel Thilo {Schroeder}},
	editor = {Schulz, Christian and U{\c c}ar, Bora}
}
@phdthesis {43132,
	title = {Explaining News Spreading Phenomena in Social Networks},
	volume = {PhD},
	year = {2022},
	author = {Daniel Thilo {Schroeder}}
}
@inproceedings {42953,
	title = {Implementing Spatio-Temporal Graph Convolutional Networks on Graphcore IPUs},
	journal = {2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2022},
	pages = {45-54},
	publisher = {IEEE},
	address = {Lyon, France},
	doi = {10.1109/IPDPSW55747.2022.00016},
	url = {https://ieeexplore.ieee.org/document/9835385/http://xplorestaging.ieee.org/ielx7/9835152/9835156/09835385.pdf?arnumber=9835385},
	author = {Moe, Johannes and Pogorelov, Konstantin and Daniel Thilo {Schroeder} and Langguth, Johannes}
}
@inproceedings {42693,
	title = {A Streaming System for Large-scale Temporal Graph Mining of Reddit Data},
	journal = {2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2022},
	pages = {1153-1162},
	publisher = {IEEE},
	address = {Lyon, France},
	doi = {10.1109/IPDPSW55747.2022.00189},
	url = {https://ieeexplore.ieee.org/document/9835250/http://xplorestaging.ieee.org/ielx7/9835152/9835156/09835250.pdf?arnumber=9835250},
	author = {Huber, Andreas and Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Griwodz, Carsten and Langguth, Johannes}
}
@article {27739,
	title = {Don{\textquoteright}t Trust Your Eyes: Image Manipulation in the Age of DeepFakes},
	journal = {Frontiers in Communication},
	volume = {6},
	year = {2021},
	publisher = {Frontiers Media SA},
	address = {Lausanne, Switzerland },
	doi = {10.3389/fcomm.2021.632317},
	url = {https://www.frontiersin.org/articles/10.3389/fcomm.2021.632317/full},
	author = {Langguth, Johannes and Pogorelov, Konstantin and Brenner, Stefan and Filkukova, Petra and Daniel Thilo {Schroeder}}
}
@misc {28043,
	title = {Explaining News Spreading Phenomena in Social Networks },
	howpublished = {H{\"a}ndlerlogo BI Norwegian Business School},
	year = {2021},
	abstract = {Digital wildfires are fast spreading online misinformation phenomena with the potential to cause harm in the physical world. They have been identified as a considerable risk to developed societies which raised the need to better understand online misinformation phenomena to mitigate that risk. We approach the problem from an interdisciplinary angle with the aim of using large scale analysis of social network data to test hypotheses about the behavior of social network users interacting with misinformation. We discuss state of the art techniques for capturing large volumes of communication data from social networks such as Twitter as well as collections of news such as GDELT. Based on that we describe new methods on how the reach as well as the typical target audience of media and social network participants can be measured. Doing so allows the testing of hypotheses such as the existence of filter bubbles through the use of large amounts of real-world data. Finally we discuss how the detection of anomalies in the typical news spreading patterns can be used to detect disinformation campaigns and digital wildfires.\&nbsp;},
	author = {Daniel Thilo {Schroeder}},
	editor = {Langguth, Johannes}
}
@inproceedings {28037,
	title = {iPUG: Accelerating Breadth-First Graph~Traversals Using Manycore Graphcore~IPUs},
	journal = {High Performance Computing. ISC High Performance 2021},
	volume = {LNCS, volume 12728},
	year = {2021},
	pages = {291-309},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {The Graphcore Intelligence Processing Unit (IPU) is a newly developed processor type whose architecture does not rely on the traditional caching hierarchies. Developed to meet the need for more and more data-centric applications, such as machine learning, IPUs combine a dedicated portion of SRAM with each of its numerous cores, resulting in high memory bandwidth at the price of capacity. The proximity of processor cores and memory makes the IPU a promising field of experimentation for graph algorithms since it is the unpredictable, irregular memory accesses that lead to performance losses in traditional processors with pre-caching.This paper aims to test the IPU{\textquoteright}s suitability for algorithms with hard-to-predict memory accesses by implementing a breadth-first search (BFS) that complies with the Graph500 specifications. Precisely because of its apparent simplicity, BFS is an established benchmark that is not only subroutine for a variety of more complex graph algorithms, but also allows comparability across a wide range of architectures.We benchmark our IPU code on a wide range of instances and compare its performance to state-of-the-art CPU and GPU codes. The results indicate that the IPU delivers speedups of up\&nbsp;to\&nbsp;4{\texttimes}4{\texttimes}\&nbsp;over the fastest competing result on an NVIDIA V100 GPU, with typical speedups of about\&nbsp;1.5{\texttimes}1.5{\texttimes}\&nbsp;on most test instances.},
	keywords = {BFS, Graph500, IPU, Performance optimization},
	isbn = {978-3-030-78712-7},
	issn = {0302-9743},
	doi = {10.1007/978-3-030-78713-4},
	url = {https://link.springer.com/10.1007/978-3-030-78713-4},
	author = {Burchard, Luk and Moe, Johannes and Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Langguth, Johannes},
	editor = {Bradford L. {Chamberlain} and Varbanescu, Ana-Lucia and Ltaief, Hatem and Luszczek, Piotr}
}
@misc {28105,
	title = {Spreading Online Misinformation},
	howpublished = {Data-SKUP 2021},
	year = {2021},
	abstract = {Digital wildfires are fast spreading online misinformation phenomena with the potential to cause harm in the physical world. They have been identified as a considerable risk to developed societies which raised the need to better understand online misinformation phenomena to mitigate that risk. We approach the problem from an interdisciplinary angle with the aim of using large scale analysis of social network data to test hypotheses about the behavior of social network users interacting with misinformation. We discuss state of the art techniques for capturing large volumes of communication data from social networks such as Twitter as well as collections of news such as GDELT. Based on that we describe new methods on how the reach as well as the typical target audience of media and social network participants can be measured. Doing so allows the testing of hypotheses such as the existence of filter bubbles through the use of large amounts of real-world data. Finally we discuss how the detection of anomalies in the typical news spreading patterns can be used to detect disinformation campaigns and digital wildfires.},
	author = {Daniel Thilo {Schroeder} and Langguth, Johannes}
}
@inproceedings {27562,
	title = {WICO Graph: a Labeled Dataset of Twitter Subgraphs based on Conspiracy Theory and 5G-Corona Misinformation Tweets},
	journal = {Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021)},
	volume = {2},
	year = {2021},
	pages = {257-266},
	publisher = {SCITEPRESS},
	abstract = {In the wake of the COVID-19 pandemic, a surge of misinformation has flooded social media and other internet channels, and some of it has the potential to cause real-world harm. To counteract this misinformation, reliably identifying it is a principal problem to be solved. However, the identification of misinformation poses a formidable challenge for language processing systems since the texts containing misinformation are short, work with insinuation rather than explicitly stating a false claim, or resemble other postings that deal with the same topic ironically. Accordingly, for the development of better detection systems, it is not only essential to use hand-labeled ground truth data and extend the analysis with methods beyond Natural Language Processing to consider the characteristics of the participant{\textquoteright}s relationships and the diffusion of misinformation. This paper presents a novel dataset that deals with a specific piece of misinformation: the idea that the 5G wireless network is causally connected to the COVID-19 pandemic. We have extracted the subgraphs of 3,000 manually classified Tweets from Twitter{\textquoteright}s follower network and distinguished them into three categories. First, subgraphs of Tweets that propagate the specific 5G misinformation, those that spread other conspiracy theories, and Tweets that do neither. We created the WICO (Wireless Networks and Coronavirus Conspiracy) dataset to support experts in machine learning experts, graph processing, and related fields in studying the spread of misinformation. Furthermore, we provide a series of baseline experiments using both Graph Neural Networks and other established classifiers that use simple graph metrics as features. The dataset is available at https://datasets.simula.no/wico-graph\&nbsp;},
	isbn = {978-989-758-484-8},
	doi = {10.5220/0010262802570266},
	author = {Daniel Thilo {Schroeder} and Schaal, Ferdinand and Filkukova, Petra and Pogorelov, Konstantin and Langguth, Johannes}
}
@inproceedings {28019,
	title = {WICO Text: A Labeled Dataset of Conspiracy Theory and 5G-Corona Misinformation Tweets},
	journal = {Proceedings of the 2021 Workshop on Open Challenges in Online Social Networks (OASIS {\textquoteright}21)},
	year = {2021},
	pages = {21-25},
	publisher = {ACM},
	author = {Pogorelov, Konstantin and Daniel Thilo {Schroeder} and Filkukova, Petra and Brenner, Stefan and Langguth, Johannes}
}
@misc {27470,
	title = {Will technical means help in preventing digital wildfires?},
	howpublished = {6th World Conference on Media and Mass Communication, Cagliari, Italy},
	year = {2021},
	author = {Langguth, Johannes and Filkukova, Petra and Daniel Thilo {Schroeder} and Pogorelov, Konstantin}
}
@inproceedings {27671,
	title = {Evaluating Standard Classifiers for Detecting COVID-19 related Misinformation},
	journal = {MediaEval 2020},
	year = {2020},
	publisher = {CEUR},
	author = {Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Langguth, Johannes}
}
@inproceedings {27672,
	title = {FakeNews: Corona Virus and 5G Conspiracy Task at MediaEval 2020},
	journal = {Media Eval Challange 2020},
	year = {2020},
	publisher = {CEUR},
	author = {Pogorelov, Konstantin and Daniel Thilo {Schroeder} and Burchard, Luk and Moe, Johannes and Brenner, Stefan and Filkukova, Petra and Langguth, Johannes},
	editor = {Larson, Martha}
}
@misc {27673,
	title = {A Framework for Interaction-based Propagation Analysis in Online Social Networks},
	year = {2020},
	address = {Complex Networks},
	author = {Daniel Thilo {Schroeder} and Lind, Pedro and Pogorelov, Konstantin and Langguth, Johannes}
}
@inproceedings {27564,
	title = {A Framework for Interaction-based Propagation Analysis in Online Social Networks},
	journal = {Complex Networks},
	year = {2020},
	note = {Extended abstract},
	publisher = {Springer},
	abstract = {Online social networks create a digital footprint of human interaction naturally by the way they function. Thus, they allow a large-scale analysis of human behavior which was previously infeasible for social scientists. Consequently, social networks have been studied intensely in the last decade. The core of most social networks is the relationship between users which can be described as a graph. The graph can be either undirected, as is the case for the friendship relation of Facebook, or directed, which is the case of the follower relation on Twitter. The relationship is readily visible, e.g. on the user interface of the social networks themselves. However, these edges are unweighted expressions of interest and reflect how individuals have chosen to relate to each other rather than how they actually interact with each other. For studying information propagation, comparing interaction properties is crucial and, therefore, using models based on connections that reflect different dimensions and strengths of acquaintance seems appropriate. Thus, there is a need for obtaining weighted edges from the communication that occurs on the social network. In this paper, we present a novel method to calculate an acquaintance score between pairs of Twitter users and use the resulting networks to enable the analysis of interaction based information propagation. By understanding the frequency and velocity with which individuals share content as a measure of acquaintance, it becomes possible to predict, compare communication patterns, and detect unusual communication. In contrast to previous work which assigns edge weights based on tie strength, our score considers the response time as a crucial factor and, therefore, enables time-based spreading comparisons.},
	author = {Daniel Thilo {Schroeder} and Lind, Pedro and Pogorelov, Konstantin and Langguth, Johannes}
}
@misc {27395,
	title = {Graph Structure Based Monitoring of Digital Wildfires},
	year = {2020},
	month = {07/2020},
	type = {Poster},
	address = {6th International Conference on Computational Social Science, Boston, MA, USA},
	keywords = {Digital wildfires, Graph neural networks, Large scale infrastructure, Misinformation, Social network analysis},
	url = {http://2020.ic2s2.org/program},
	author = {Langguth, Johannes and Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Filkukova, Petra}
}
@inproceedings {27560,
	title = {Resource Efficient Algorithms for Message Sampling in Online Social Networks},
	journal = {The Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS-2020)},
	year = {2020},
	publisher = {IEEE},
	abstract = {Sampling the network structure of online social networks is a widely discussed topic as it enables a wide variety of research in computational social science and associated fields. However, analyzing and sampling contentful messages still lacks effective solutions. Previous work for retrieving messages from social networks either used endpoints that are not available to the general research community or analyzed a predefined stream of messages. Our work uses features of the Twitter API that we utilize to construct a data structure that optimizes the efficiency of requests sent to the social network. Moreover, we present a strategy for selecting users to sample, which improves the effectiveness of our query optimizing data structure by leveraging existing models of user behavior. Combining our data structure with our proposed algorithm, we can achieve a 92\% sampling efficiency over long timeframes.},
	author = {Burchard, Luk and Daniel Thilo {Schroeder} and Becker, Soeren and Langguth, Johannes}
}
@inproceedings {27561,
	title = {A Scalable System for Bundling Online Social Network Mining Research},
	journal = {2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS)},
	year = {2020},
	pages = {1-6},
	publisher = {IEEE},
	abstract = {Online social networks such as Facebook and Twitter are part of the everyday life of millions of people. They are not only used for interaction but play an essential role when it comes to information acquisition and knowledge gain. The abundance and detail of the accumulated data in these online social networks open up new possibilities for social researchers and psychologists, allowing them to study behavior in a large test population. However, complex application programming interfaces (API) and data scraping restrictions are, in many cases, a limiting factor when accessing this data. Furthermore, research projects are typically granted restricted access based on quotas. Thus, research tools such as scrapers that access social network data through an API must manage these quotas. While this is generally feasible, it becomes a problem when more than one tool, or multiple instances of the same tool, is being used in the same research group. Since different tools typically cannot balance access to a shared quota on their own, additional software is needed to prevent the individual tools from overusing the shared quota. In this paper, we present a proxy server that manages several researchers{\textquoteright} data contingents in a cooperative research environment and thus enables a transparent view of a subset of Twitter{\textquoteright}s API. Our proxy scales linearly with the number of clients in use and incurs almost no performance penalties or implementation overhead to further layer or applications that need to work with the Twitter API. Thus, it allows seamless integration of multiple API accessing programs within the same research group.},
	author = {Burchard, Luk and Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Becker, Soeren and Dietrich, Emily and Filkukova, Petra and Langguth, Johannes}
}
@inproceedings {27396,
	title = {A System for High Performance Mining on GDELT Data},
	journal = {2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2020},
	month = {05/2020},
	publisher = {IEEE},
	abstract = {We design a system for efficient in-memory analysis of data from the GDELT database of news events. The specialization of the system allows us to avoid the inefficiencies of existing alternatives, and make full use of modern parallel high-performance computing hardware. We then present a series of experiments showcasing the system{\textquoteright}s ability to analyze correlations in the entire GDELT 2.0 database containing more than a billion news items. The results reveal large scale trends in the world of today{\textquoteright}s online news.},
	keywords = {Data mining, GDELT, High Performance Computing, Misinformation, Publishing},
	author = {Pogorelov, Konstantin and Daniel Thilo {Schroeder} and Filkukova, Petra and Langguth, Johannes}
}
@misc {27674,
	title = {EXA - A distributed computation environment},
	year = {2019},
	address = {Geilo Wintrerschool 2019},
	author = {Herb, Tobias and Daniel Thilo {Schroeder}}
}
@inproceedings {26680,
	title = {FACT: a Framework for Analysis and Capture of Twitter Graphs},
	journal = {The Sixth IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS-2019) },
	year = {2019},
	pages = {134-141},
	publisher = {IEEE},
	abstract = {In the recent years, online social networks have become an important source of news and the primary place for political debates for a growing part of the population. At the same time, the spread of fake news and digital wildfires (fast- spreading and harmful misinformation) has become a growing concern worldwide, and online social networks the problem is most prevalent. Thus, the study of social networks is an essential component in the understanding of the fake news phenomenon. Of particular interest is the network connectivity between participants, since it makes communication patterns visible. These patterns are hidden in the offline world, but they have a profound impact on the spread of ideas, opinions and news. Among the major social networks, Twitter is of special interest. Because of its public nature, Twitter offers the possibility to perform research without the risk of breaching the expectation of privacy. However, obtaining sufficient amounts of data from Twitter is a fundamental challenge for many researchers. Thus, in this paper, we present a scalable framework for gathering the graph structure of follower networks, posts and profiles. We also show how to use the collected data for high-performance social network analysis.\&nbsp;},
	doi = {10.1109/SNAMS.2019.8931870},
	author = {Daniel Thilo {Schroeder} and Pogorelov, Konstantin and Langguth, Johannes}
}
@inproceedings {26681,
	title = {Graph-based Feature Selection Filter Utilizing Maximal Cliques},
	journal = {The Sixth IEEE International Conference on Social Networks Analysis, Management and Security (SNAMS-2019) },
	year = {2019},
	pages = {297-302},
	publisher = {IEEE},
	abstract = {Huge amounts of data are collected every millisecond all around the world. This ranges from images and videos to an increasing amount of sensor data. Thus, it gets difficult for humans to decide on the most important features anymore. But reducing the feature vector is an important and necessary task to achieve higher precision in classification tasks. Detecting anomalies and classifying data points is crucial for a variety of objectives in many domains. Therefore, this work focuses on feature selection for binary decision problems (e.g. anomaly detection, binary classification). We propose a novel graph-based feature selection filter, which takes into account both the importance and correlation of features at the same time. The graph-based feature selection filter recommends a subset by applying a rating function onto the maximal cliques of the graph. The evaluation is based on a comparison of the accuracy of multiple machine learning algorithms and datasets between different baseline feature selection approaches and the proposed approach. Results show that the proposed approach delivers the highest accuracy in about 69\% of the cases compared to existing approaches, while reducing the number of features.\&nbsp;},
	author = {Daniel Thilo {Schroeder} and Styp-Rekowski, Kevin and Schmidt, Florian and Acker, Alexander and Kao, Odej}
}
