Plattformen und neue Technologien im Journalismus: Ergebnisse einer Online-Befragung von Journalistinnen und Journalisten in Deutschland

• URL: http://arxiv.org/abs/2105.07881v1
• Date: Thu, 13 May 2021 10:05:56 GMT
• Title: Plattformen und neue Technologien im Journalismus: Ergebnisse einer Online-Befragung von Journalistinnen und Journalisten in Deutschland
• Authors: Benjamin Rech, Matthias Meyer
• Abstract summary: 385 journalists in Germany were surveyed about platforms in journalism. Freelancers have a higher commitment than employed journalists. For German journalists it is important that the platform is developed in Europe or Germany.
• Score: 0.5076419064097732
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In an online survey in December 2020, 385 journalists in Germany were surveyed about platforms in journalism and about their frequency of use and willingness to adopt emerging technologies. Journalists have a commitment to publish on a journalism platform on a full-time basis. Freelancers have a higher commitment than employed journalists. A platform subscription model is rated more attractive than advertising for a platform. Employed journalists on the other hand consider advertising more attractive than freelance journalists. For German journalists it is important that the platform is developed in Europe or Germany and that it sets high standards on data protection. Multimedia forms and interactive elements are used occasionally, often or always. Stories or Reels are predominantly not used. AI software as well as editorial analytics are rarely or never used. Apart from stories or reels, journalists intend to use multimedia forms and interactive elements more often in the future. They are receptive to software for research process documentation as well as to the analysis of indicators of their own publications. Software as a support for text production, image selection or headline suggestions is mostly rejected.

Related papers

• AI as a Tool for Fair Journalism: Case Studies from Malta [0.07499722271664144]
  Two projects focus on media monitoring and present tools designed to analyse potential biases in news articles and television news segments. The first project uses Computer Vision and Natural Language Processing techniques to analyse the coherence between images in news articles and their corresponding captions, headlines, and article bodies. The second project employs computer vision techniques to track individuals on-screen time or visual exposure in news videos, providing queryable data.
  arXiv  Detail & Related papers  (2024-07-08T15:02:39Z)
• PressProtect: Helping Journalists Navigate Social Media in the Face of Online Harassment [4.790507122630804]
  We conduct need-finding interviews with Asian American and Pacific Islander journalists to understand where existing platform tools and newsroom resources fall short in adequately protecting journalists. We build PressProtect, an interface that provides journalists greater agency when engaging with readers on Twitter/X. We conclude with a discussion of our findings and recommendations for social platforms hoping to build defensive defaults for journalists facing online harassment.
  arXiv  Detail & Related papers  (2024-01-19T21:16:41Z)
• Tracking the Newsworthiness of Public Documents [107.12303391111014]
  This work focuses on news coverage of local public policy in the San Francisco Bay Area by the San Francisco Chronicle. First, we gather news articles, public policy documents and meeting recordings and link them using probabilistic relational modeling. Second, we define a new task: newsworthiness prediction, to predict if a policy item will get covered.
  arXiv  Detail & Related papers  (2023-11-16T10:05:26Z)
• News and Misinformation Consumption in Europe: A Longitudinal Cross-Country Perspective [49.1574468325115]
  This study investigated information consumption in four European countries. It analyzed three years of Twitter activity from news outlet accounts in France, Germany, Italy, and the UK. Results indicate that reliable sources dominate the information landscape, although unreliable content is still present across all countries.
  arXiv  Detail & Related papers  (2023-11-09T16:22:10Z)
• Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper [1.450405446885067]
  We use ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations. We show that classifiers trained on this data are able to identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. We observe that, similarly to traditional newspapers, ChatGPT editorial line evolves with time and, being a data-driven system, the stance of the generated articles differs among languages.
  arXiv  Detail & Related papers  (2023-10-25T01:01:28Z)
• Pressmatch: Automated journalist recommendation for media coverage with Nearest Neighbor search [0.6118897979046375]
  Good media coverage ensures greater product reach and drives audience engagement for those products. Keeping up with journalist beats and curating a media contacts list is often a huge and time-consuming task. This study proposes a model to automate and expedite the process by recommending suitable journalists to run media coverage on the press releases provided by the user.
  arXiv  Detail & Related papers  (2023-09-02T13:41:29Z)
• Identifying Informational Sources in News Articles [109.70475599552523]
  We build the largest and widest-ranging annotated dataset of informational sources used in news writing. We introduce a novel task, source prediction, to study the compositionality of sources in news articles.
  arXiv  Detail & Related papers  (2023-05-24T08:56:35Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• DIANES: A DEI Audit Toolkit for News Sources [7.864608835252345]
  We present DIANES, a DEI Audit Toolkit for News Sources. It consists of a natural language processing pipeline on the backend to extract quotes, speakers, titles, and organizations from news articles in real time.
  arXiv  Detail & Related papers  (2022-03-21T23:03:04Z)
• Viable Threat on News Reading: Generating Biased News Using Natural Language Models [49.90665530780664]
  We show that publicly available language models can reliably generate biased news content based on an input original news. We also show that a large number of high-quality biased news articles can be generated using controllable text generation.
  arXiv  Detail & Related papers  (2020-10-05T16:55:39Z)
• Political audience diversity and news reliability in algorithmic ranking [54.23273310155137]
  We propose using the political diversity of a website's audience as a quality signal. Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 U.S. citizens, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards.
  arXiv  Detail & Related papers  (2020-07-16T02:13:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.