Crowdsourced audit of Twitter's recommender systems.

This research conducts an audit of Twitter's recommender system, aiming to examine the disparities between users' curated timelines and their subscription choices. Through the combined use of a browser extension and data collection via the Twitter API, our investigation reveals a high amplification of friends from the same community, a preference for amplifying emotionally charged and toxic tweets and an uneven algorithmic amplification across friends' political leaning. This audit emphasizes the importance of transparency, and increased awareness regarding the impact of algorithmic curation.

Mobile-based traceability system for sustainable food supply networks.

Traceability is key to ensure food quality and safety from farm to fork, yet high implementation costs and the complexity of the food supply chain pose challenges to its operation. Here we propose a mobile-based bidirectional tracing system for food products that integrates graph data and peer-to-peer architecture. Our system allows data synchronization to happen seamlessly between all connected nodes, as data are gathered through market transactions and all related product information is concatenated by scanning 2D product barcodes.

Reconstruction of the socio-semantic dynamics of political activist Twitter networks-Method and application to the 2017 French presidential election.

Background: Digital spaces, and in particular social networking sites, are becoming increasingly present and influential in the functioning of our democracies. In this paper, we propose an integrated methodology for the data collection, the reconstruction, the analysis and the visualization of the development of a country's political landscape from Twitter data.

Method: The proposed method relies solely on the interactions between Twitter accounts and is independent of the characteristics of the shared contents such as the language of the tweets.