How Loud Rumbles Hit Newsstands: A Data Analysis of Coverage and Spatial Bias in German News about Landslides Around the World

• URL: http://arxiv.org/abs/2605.18105v1
• Date: Mon, 18 May 2026 09:17:05 GMT
• Title: How Loud Rumbles Hit Newsstands: A Data Analysis of Coverage and Spatial Bias in German News about Landslides Around the World
• Abstract summary: This paper focuses on how landslides around the world are reported in German newspapers.<n>We analyse almost 60k news articles about 5.5k news events in a 25-year period.
• Score: 4.607829363710452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Landslides often hit newsstands due to their destructive and potentially fatal effects. News are a valuable source of information for creating or enriching disaster databases and for expediting media-based studies of the dynamics of media attention. To accomplish that, news datasets must be filtered, geolocated and validated. This paper focuses on how landslides around the world are reported in German newspapers. We analyse almost 60k news articles about 5.5k news events in a 25-year period, compare it with external measures of countries' susceptibility to landslides and provide insights, e.g.~the overreporting of Southern and Western Europe, to foment further studies on inequalities in media attention to international disasters.

Related papers

• The Newsworthiness of Brazilian Distress: A Peak Analysis on Time Series of International Media Attention to Disasters in Brazil [4.607829363710452]
  This paper presents a peak analysis of 2k news about Brazilian fires and landslides in German newspapers from 2000 to 2024.<n>Using time series segmentation to detect news event peaks, we examine the extent to which they can be temporally aligned with observations in national and global disaster databases.
  arXiv  Detail & Related papers  (2026-05-06T06:57:18Z)
• MediaSpin: Exploring Media Bias Through Fine-Grained Analysis of News Headlines [12.636213065708318]
  This study introduces the MediaSpin dataset, the first to characterize the bias in how prominent news outlets editorialize news headlines after publication.<n>We discuss the linguistic insights it affords and show its applications for bias prediction and user behavior analysis.
  arXiv  Detail & Related papers  (2024-12-03T08:41:13Z)
• 3DLNews: A Three-decade Dataset of US Local News Articles [49.1574468325115]
  3DLNews is a novel dataset with local news articles from the United States spanning the period from 1996 to 2024. It contains almost 1 million URLs (with HTML text) from over 14,000 local newspapers, TV, and radio stations across all 50 states.
  arXiv  Detail & Related papers  (2024-08-08T18:33:37Z)
• A Multilingual Similarity Dataset for News Article Frame [14.977682986280998]
  We introduce an extended version of a large labeled news article dataset with 16,687 new labeled pairs. Our method frees the work of manual identification of frame classes in traditional news frame analysis studies. Overall we introduce the most extensive cross-lingual news article similarity dataset available to date with 26,555 labeled news article pairs across 10 languages.
  arXiv  Detail & Related papers  (2024-05-22T01:01:04Z)
• 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)
• Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines [63.52264764099532]
  We use a large dataset of 1.8 million news headlines from major U.S. media outlets spanning from 2014 to 2022. We quantify the fine-grained thematic discrepancy related to four prominent topics - domestic politics, economic issues, social issues, and foreign affairs. Our findings indicate that on domestic politics and social issues, the discrepancy can be attributed to a certain degree of media bias.
  arXiv  Detail & Related papers  (2023-03-28T03:31:37Z)
• Predicting Sentence-Level Factuality of News and Bias of Media Outlets [10.925648034990306]
  This paper introduces a large sentence-level dataset, titled "FactNews", composed of 6,191 sentences expertly annotated according to factuality and media bias definitions proposed by AllSides. We use FactNews to assess the overall reliability of news sources, by formulating two text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets.
  arXiv  Detail & Related papers  (2023-01-27T16:56:24Z)
• 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)
• Misinfo Belief Frames: A Case Study on Covid & Climate News [49.979419711713795]
  We propose a formalism for understanding how readers perceive the reliability of news and the impact of misinformation. We introduce the Misinfo Belief Frames (MBF) corpus, a dataset of 66k inferences over 23.5k headlines. Our results using large-scale language modeling to predict misinformation frames show that machine-generated inferences can influence readers' trust in news headlines.
  arXiv  Detail & Related papers  (2021-04-18T09:50:11Z)
• News Information Decoupling: An Information Signature of Catastrophes in Legacy News Media [0.0]
  During the first half of 2020, legacy news media became "corona news" following national outbreak and crises management patterns. We use legacy print media to empirically derive the principle News Information Decoupling (NID) that functions as an information signature of culturally significant catastrophic event.
  arXiv  Detail & Related papers  (2021-01-08T10:56:30Z)
• 365 Dots in 2019: Quantifying Attention of News Sources [69.50862982117125]
  We measure the overlap of topics of online news articles from a variety of sources. We score news stories according to the degree of attention in near-real time. This can enable multiple studies, including identifying topics that receive the most attention.
  arXiv  Detail & Related papers  (2020-03-22T20:32:47Z)

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.