The Newsworthiness of Brazilian Distress: A Peak Analysis on Time Series of International Media Attention to Disasters in Brazil

• URL: http://arxiv.org/abs/2605.04552v1
• Date: Wed, 06 May 2026 06:57:18 GMT
• Title: The Newsworthiness of Brazilian Distress: A Peak Analysis on Time Series of International Media Attention to Disasters in Brazil
• Abstract summary: 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.
• Score: 4.607829363710452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Media coverage influences disaster response, yet the drivers of international media attention to local events remain unevenly understood. Brazil offers a compelling case: some of its natural and technological disasters occasionally hit the international headlines. However, systematic analyses of what makes these events be discussed abroad are still missing. Addressing this gap requires representative, validated and country-specific news datasets. This paper presents a peak analysis of 2k news about Brazilian fires and landslides in German newspapers from 2000 to 2024. 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.

Related papers

• How Loud Rumbles Hit Newsstands: A Data Analysis of Coverage and Spatial Bias in German News about Landslides Around the World [4.607829363710452]
  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.
  arXiv  Detail & Related papers  (2026-05-18T09:17:05Z)
• Identifying and Investigating Global News Coverage of Critical Events Such as Disasters and Terrorist Attacks [7.356090574846918]
  We introduce an AI-powered method for identifying news articles based on an event FINGERPRINT.<n>The method achieves state-of-the-art performance and scales to massive databases of tens of millions of news articles.<n>We use FAME to identify 27,441 articles that cover natural disaster and terrorist attack events that happened in 2020.
  arXiv  Detail & Related papers  (2025-06-15T17:50:08Z)
• When it Rains, it Pours: Modeling Media Storms and the News Ecosystem [26.645261253849117]
  We create a comprehensive corpus of media storms over a nearly two year period. We validate claims about storm evolution and topical distribution. We provide empirical support for previously hypothesized patterns of influence of storms on media coverage and intermedia agenda setting.
  arXiv  Detail & Related papers  (2023-12-04T18:49:06Z)
• CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification [51.58605842457186]
  We present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2023-10-23T07:01:09Z)
• 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)
• 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)
• TriggerCit: Early Flood Alerting using Twitter and Geolocation -- a comparison with alternative sources [0.2603110718989132]
  Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. We propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021.
  arXiv  Detail & Related papers  (2022-02-24T10:55:49Z)
• 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)
• Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh [0.26651200086513094]
  This study analyzed a large set of Bangladeshi newspaper data related to CO-19 pandemic. This analysis will help the government and other organizations to figure out the challenges that have arisen in society due to this pandemic.
  arXiv  Detail & Related papers  (2021-01-24T07:46:17Z)
• 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)
• Event-Related Bias Removal for Real-time Disaster Events [67.2965372987723]
  Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time. We train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.
  arXiv  Detail & Related papers  (2020-11-02T02:03:07Z)
• Tracking, exploring and analyzing recent developments in German-language online press in the face of the coronavirus crisis: cOWIDplus Analysis and cOWIDplus Viewer [62.997667081978825]
  The coronavirus pandemic may be the largest crisis the world has had to face since World War II. It does not come as a surprise that it is also having an impact on language as our primary communication tool. We present three inter-connected resources that are designed to capture and illustrate these effects on a subset of the German language.
  arXiv  Detail & Related papers  (2020-05-27T12:21:36Z)

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.