Prta: A System to Support the Analysis of Propaganda Techniques in the News

• URL: http://arxiv.org/abs/2005.05854v1
• Date: Tue, 12 May 2020 15:20:55 GMT
• Title: Prta: A System to Support the Analysis of Propaganda Techniques in the News
• Authors: Giovanni Da San Martino, Shaden Shaar, Yifan Zhang, Seunghak Yu, Alberto Barr\'on-Cede\~no, Preslav Nakov
• Abstract summary: Prta allows users to explore the articles crawled on a regular basis by highlighting the spans in which propaganda techniques occur. The system further reports statistics about the use of such techniques, overall and over time, or according to filtering criteria specified by the user. It allows users to analyze any text or URL through a dedicated interface or via an API.
• Score: 34.61449860876045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent events, such as the 2016 US Presidential Campaign, Brexit and the COVID-19 "infodemic", have brought into the spotlight the dangers of online disinformation. There has been a lot of research focusing on fact-checking and disinformation detection. However, little attention has been paid to the specific rhetorical and psychological techniques used to convey propaganda messages. Revealing the use of such techniques can help promote media literacy and critical thinking, and eventually contribute to limiting the impact of "fake news" and disinformation campaigns. Prta (Propaganda Persuasion Techniques Analyzer) allows users to explore the articles crawled on a regular basis by highlighting the spans in which propaganda techniques occur and to compare them on the basis of their use of propaganda techniques. The system further reports statistics about the use of such techniques, overall and over time, or according to filtering criteria specified by the user based on time interval, keywords, and/or political orientation of the media. Moreover, it allows users to analyze any text or URL through a dedicated interface or via an API. The system is available online: https://www.tanbih.org/prta

Related papers

• PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent [71.20471076045916]
  Propaganda plays a critical role in shaping public opinion and fueling disinformation. Propainsight systematically dissects propaganda into techniques, arousal appeals, and underlying intent. Propagaze combines human-annotated data with high-quality synthetic data.
  arXiv  Detail & Related papers  (2024-09-19T06:28:18Z)
• 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)
• Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation [105.20743048379387]
  We propose a novel framework for generating training examples informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.
  arXiv  Detail & Related papers  (2022-03-10T14:24:19Z)
• How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption? [8.586057042714698]
  We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis. Our recommender detects and reveals substantial frames that are actually present in individual news articles. Our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.
  arXiv  Detail & Related papers  (2021-10-18T10:13:23Z)
• Interpretable Propaganda Detection in News Articles [30.192497301608164]
  We propose to detect and to show the use of deception techniques as a way to offer interpretability. Our interpretable features can be easily combined with pre-trained language models, yielding state-of-the-art results.
  arXiv  Detail & Related papers  (2021-08-29T09:57:01Z)
• Dataset of Propaganda Techniques of the State-Sponsored Information Operation of the People's Republic of China [0.0]
  This research aims to bridge the information gap by providing a multi-labeled propaganda techniques dataset in Mandarin based on a state-backed information operation dataset provided by Twitter. In addition to presenting the dataset, we apply a multi-label text classification using fine-tuned BERT.
  arXiv  Detail & Related papers  (2021-06-14T16:11:13Z)
• Cross-Domain Learning for Classifying Propaganda in Online Contents [67.10699378370752]
  We present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step.
  arXiv  Detail & Related papers  (2020-11-13T10:19:13Z)
• 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.