Although the distribution of propaganda and hoaxes is not a new phenomenon, the explosion of the web and social networks has led to an unprecedented increase in the volume, speed of propagation and variety of "fake news". In fact, in recent years, numerous disinformation campaigns organized to destabilize society and affect citizens' right to receive truthful information have been detected. The COVID-19 epidemic has further amplified this problem, giving rise to the term infodemic to refer to the increase in information (in many cases not truthful) about an event (such as the pandemic) in a short time. In this scenario, Artificial Intelligence techniques can characterize, detect and predict the evolution of multiple types of disinformative contents (including fake, misleading, and out of context news, for instance.) Likewise, Artificial Intelligence can expand their reach and impact, facilitating the automatic synthesis of contents and creating manipulations of images and videos that are difficult to detect.

The I Workshop on Artificial Intelligence and Disinformation (AIDisinfo 2021) is a scientific forum at the intersection of Artificial Intelligence and the study of disinformation. The workshop will bring together researchers from various disciplines in the field of machine learning (social network analysis, natural language processing, automatic classification), big data processing (information retrieval, recommendation systems) and media analysis (verification of news, characterization of disinformation, ethical and legal aspects.) The objective is to present the latest advances in these areas and seek synergies between different work lines.

Contact: caepia20-21@easychair.org

Chairs

Topics

Topics of interest for AIDisinfo include, but are not limited to, the following:

  • Deep learning methods for disinformation
  • Social network analysis and disinformation
  • Natural language processing and disinformation
  • AI-supported fact checking and detection of disinformation campaigns
  • Generation and identification of fabricated and manipulated content (deep fakes)
  • Community detection in social networks (denialist, conspiracy, etc.)
  • Disinformation diffusion in social networks
  • Graph algorithms for disinformation
  • Profiling fake news spreaders
  • Bots characterisation and detection
  • Multimodal fake news detection
  • Recommendation systems and disinformation
  • Meta heuristics algorithms for disinformation
  • Data knowledge extraction (scrapping) for disinformation

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