New/s/leak 2.0 – Multilingual Information Extraction and Visualization for Investigative Journalism

Wiedemann, Gregor; Yimam, Seid Muhie; Biemann, Chris

doi:10.1007/978-3-030-01159-8_30

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…For example, netflower is a visual exploration tool that supports journalists in the analysis of quantitative data flows [16]. Similarly, the tool newsleak [17] was designed to support investigative journalists to make sense of leak data.…”

Section: Data Visualization In Journalismmentioning

confidence: 99%

From Shock to Shift: Data Visualization for Constructive Climate Journalism

Morini,

Eschenbacher,

Hartmann

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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We present a multi-dimensional, multi-level, and multi-channel approach to data visualization for the purpose of constructive climate journalism. Data visualization has assumed a central role in environmental journalism and is often used in data stories to convey the dramatic consequences of climate change and other ecological crises. However, the emphasis on the catastrophic impacts of climate change tends to induce feelings of fear, anxiety, and apathy in readers. Climate mitigation, adaptation, and protection-all highly urgent in the face of the climate crisis-are at risk of being overlooked. These topics are more difficult to communicate as they are hard to convey on varying levels of locality, involve multiple interconnected sectors, and need to be mediated across various channels from the printed newspaper to social media platforms. So far, there has been little research on data visualization to enhance affective engagement with data about climate protection as part of solution-oriented reporting of climate change. With this research we characterize the unique challenges of constructive climate journalism for data visualization and share findings from a research and design study in collaboration with a national newspaper in Germany. Using the affordances and aesthetics of travel postcards, we present Klimakarten, a data journalism project on the progress of climate protection at multiple spatial scales (from national to local), across five key sectors (agriculture, buildings, energy, mobility, and waste), and for print and online use. The findings from quantitative and qualitative analysis of reader feedback confirm our overall approach and suggest implications for future work.

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Section: Data Visualization In Journalismmentioning

confidence: 99%

From Shock to Shift: Data Visualization for Constructive Climate Journalism

Morini,

Eschenbacher,

Hartmann

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…In investigative journalism, tools like New/s/leak 2.0 [103], as used by Der SPIEGEL, use models trained on public data like Wikipedia for discovering named entities in textual data (e.g., persons or company names). Similarity, the industry-standard spacy [48] uses public corpora and increasingly open web information for model training.…”

Section: Digital Communication Analysis and Employed Technologymentioning

confidence: 99%

“…One of the most prevailing domains for such systems is the analysis of communications data for intelligence purposes, namely in criminal investigations, lawsuits, matters of national and international security and in investigative journalism. Specialized systems are used, for example, by the National Security Agency (NSA) as part of global spying operations [77] or law enforcement against organized crime [23], by lawyers for analyzing case-relevant documents [2], but also by journalists working on [103] large data leaks such as the Panama Papers. During these operations, large amounts of communication data, like e-mails, chats, posts, or calls are collected, along with associated documents (e.g., attachments) and meta-data like timestamps, locations, and contact networks.…”

mentioning

confidence: 99%

Promoting Ethical Awareness in Communication Analysis: Investigating Potentials and Limits of Visual Analytics for Intelligence Applications

Fischer,

Hirsbrunner,

Jentner

et al. 2022

Preprint

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Digital systems for analyzing human communication data have become prevalent in recent years. This may be related to the increasing abundance of data that can be harnessed but can hardly be managed manually. Intelligence analysis of communications data in investigative journalism, criminal intelligence, and law present particularly interesting cases, as they must take into account the often highly sensitive properties of the underlying operations and data. At the same time, these are areas where increasingly automated, sophisticated approaches and tailored systems can be particularly useful and relevant, especially in terms of Big Data manageability.However, by the shifting of responsibilities, this also poses dangers . In addition to privacy concerns, these dangers relate to uncertain or poor data quality, leading to discrimination and potentially misleading insights. Other problems relate to a lack of transparency and traceability, making it difficult to accurately identify problems and determine appropriate remedial strategies.Visual analytics combines machine learning methods with interactive visual interfaces to enable human sense-and decision-making. This technique can be key for designing and operating meaningful interactive communication analysis systems that consider these ethical challenges. In this interdisciplinary work, a joint endeavor of computer scientists, ethicists, and scholars in Science & Technology Studies, we investigate and evaluate opportunities and risks involved in using Visual analytics approaches for communication analysis in intelligence applications in particular. We introduce, at first, the common technological systems used in communication analysis, with a special focus on intelligence analysis in criminal investigations, further discussing the domain-specific ethical implications, tensions, and risks involved. We then make the case of how tailored Visual Analytics approaches may reduce and mitigate the described problems, both theoretically and through practical examples. Offering interactive analysis capabilities and what-if explorations while facilitating guidance, provenance generation, and bias awareness (through nudges, for example) can improve analysts' understanding of their data, increasing trustworthiness, accountability, and generating knowledge. We show that finding Visual Analytics design solutions for ethical issues is not a mere optimization task with an ideal final solution. Design solutions for specific ethical problems (e.g., privacy) often trigger new ethical issues (e.g., accountability) in other areas. Balancing out and negotiating these trade-offs has, as we argue, to be an integral aspect of the system design process from the outset. Finally, our work identifies existing gaps and highlights research opportunities, further describing how our results can be transferred to other domains. With this contribution, we aim at informing more ethically-aware approaches to communication analysis in intelligence operations.CCS Concepts: • Computing methodologies → Visua...

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“…Due to the micro-service architecture, which communicates over HTTP, our NER docker container can be easily be used in parallel NLP processing chains. We use it, for instance, in the information extraction pipeline of our "new/s/leak" project (Wiedemann et al, 2018), to create visualizations of co-occurrence networks of named entities.…”

Section: Micro-servicementioning

confidence: 99%

microNER: A Micro-Service for German Named Entity Recognition based on BiLSTM-CRF

Wiedemann¹,

Jindal²,

Biemann³

2018

Preprint

Self Cite

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For named entity recognition (NER), bidirectional recurrent neural networks became the state-of-the-art technology in recent years. Competing approaches vary with respect to pre-trained word embeddings as well as models for character embeddings to represent sequence information most effectively. For NER in German language texts, these model variations have not been studied extensively. We evaluate the performance of different word and character embeddings on two standard German datasets and with a special focus on out-of-vocabulary words. With F-Scores above 82% for the GermEval'14 dataset and above 85% for the CoNLL'03 dataset, we achieve (near) state-of-the-art performance for this task. We publish several pre-trained models wrapped into a microservice based on Docker to allow for easy integration of German NER into other applications via a JSON API.

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New/s/leak 2.0 – Multilingual Information Extraction and Visualization for Investigative Journalism

Cited by 7 publications

References 10 publications

From Shock to Shift: Data Visualization for Constructive Climate Journalism

From Shock to Shift: Data Visualization for Constructive Climate Journalism

Promoting Ethical Awareness in Communication Analysis: Investigating Potentials and Limits of Visual Analytics for Intelligence Applications

microNER: A Micro-Service for German Named Entity Recognition based on BiLSTM-CRF

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