The first chapter, the past, is composed of two visual layers and comments on the capacity of the media’s ability to affect our view of reality.

The first layer is created through the reprocessing of point clouds that range from macroscopic visions, such as urban and rural environments, to the individual perspective, where human gestures represent the fundamental role of relationships in building individual trust. These representations are created with different techniques: internal reproduction of cities are mostly created with photogrammetric processes, as we started investigating in Treu. Earlier tests were carried out trying to extrapolate 3-dimensional information from historical images through deep-learning techniques performing monocular depth estimation. Moreover, aerial depictions of large portions of lands are created with the help of material provided by the United States Geological Survey (USGS). This content has been collected and then manipulated and re-processed inside openFrameworks to generate the final visualisation.

The second layer is composed of artificial newspaper text catalogued by historical period. They are the result of an algorithm trained to recognize newspaper articles containing the word “trust” dating between 1922 and 2021. We queried news.google.com/newspapers dataset with the keyword “trust” and filtered the results with only free resources. This allowed us to obtain a dataset of newspaper pages and articles about “trust”. We split this dataset into different time periods and used the resulting articles to train a neural network to generate similar articles. The perception of how the news can influence reality is symbolised by the fluctuations of the point clouds. 

The present is designed to be visually composed of a series of moments impressed on a film. The negative images scroll together with 750,000 tweets selected from 500,000,000 messages archived during the pandemic, starting from March 2020. All the selected tweets contain the word "trust" and have been categorised with a positive or negative label through a sentiment analysis algorithm.

By combining the monthly trust data taken from the ICS index and the real-time value of the Dow Jones index, the algorithm chooses which sentiment is the most appropriate for each specific moment. For each film frame, the system calculates an average sentiment of the 6 main emotions contained in the tweets and visualises these values on the film edge.

The final section, the future, is composed of parallel timelines interacting together according to the predicted trust value and its evolution into the future. The potential trends are the result of a predictive analysis carried out through a recurrent neural network trained on the basis of the historical series of financial data and confidence index.

During the entirety of the installation, the software analyses the combination of financial data in relation to the index of confidence, thereby generating infinite possible evolutions of the installation in the future. The audiovisual content is always connected to this live data stream and it depicts abstract generative scenery. In this section of the work, we were able to extract 3D models from bidimensional aerial images, populate the generated mesh with points and then feed the newly created point cloud in our custom-designed software for the whole animation process to begin.