One Million Dreams

Dreaming on social networks.

One Million Dreams is a generative video that lasts around 200 hours which shows, instant by instant, a whole year of dreams captured from social networks.

This is a short preview.

You had a dream last night, and you published something about it on major social networks? A software captured it and put it on a database.

In the video, dreams are captured and processed using natural language analysis to understand people’s emotions regarding the various dreams, and the topic it deals with.

For the time being, we have isolated a limited number of topics: “job”, “love”, “school”, “money”, “environment”, “relations”, “family”, “memories” and “future”.

The video will be soon implemented as an exhibit, in which dreams will be captured and visualized in real-time.

One Million Dreams Exhibit

One Million Dreams Exhibit

Then they will be archived, to obtain the largest dream archive of the planet: a global library of the dreams of all humanity in which it will be possible to contribute, search for dreams and extract them (the archive of real-time dreams will form a source of real-time Open Data) and research, play, make apps with them.

One Million Dreams has been produced through the Human Ecosystems project.

9 thoughts on “One Million Dreams

  1. Pingback: One Million Dreams | Design Interaction

  2. check this out, too: http://arxiv.org/abs/1402.2297

    Connecting Dream Networks Across Cultures

    Onur Varol, Filippo Menczer
    (Submitted on 10 Feb 2014)
    Many species dream, yet there remain many open research questions in the study of dreams. The symbolism of dreams and their interpretation is present in cultures throughout history. Analysis of online data sources for dream interpretation using network science leads to understanding symbolism in dreams and their associated meaning. In this study, we introduce dream interpretation networks for English, Chinese and Arabic that represent different cultures from various parts of the world. We analyze communities in these networks, finding that symbols within a community are semantically related. The central nodes in communities give insight about cultures and symbols in dreams. The community structure of different networks highlights cultural similarities and differences. Interconnections between different networks are also identified by translating symbols from different languages into English. Structural correlations across networks point out relationships between cultures. Similarities between network communities are also investigated by analysis of sentiment in symbol interpretations. We find that interpretations within a community tend to have similar sentiment. Furthermore, we cluster communities based on their sentiment, yielding three main categories of positive, negative, and neutral dream symbols

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