Best Data Science Open and Sharing Practices

Fringe communities are often the sources of conspiracy theories and extreme ideas. Niche online platforms hosting such communities represent suitable incubators for reinforcing questionable stories and to ultimately push them into the mainstream. Over the years, information pathways from fringe to mainstream media have significantly increased, enabling the proliferation of harmful content. This project aims at developing novel network- and AI-based models for identifying and characterizing information pathways that enable the proliferation of potentially harmful content on online media channels. Mainstream social media (Twitter, Facebook, and Instagram), video streaming platforms (YouTube and Bitchute), niche platforms (Gab, 4chan, and Parler), and messaging apps (Telegram) will be considered to investigate how harmful narratives flow across diverse platforms and predict those that will gain traction on mainstream media.

Data science projects require knowledge of software that changes rapidly. As a result, scientists spend hours reading long documentations and manuals instead of advancing their scientific fields. In this project, we aim to automatically extract relevant aspects of scientific software (e.g., what does it do, how to install it, how to operate with it or how to cite it) from documentation and code using machine learning techniques. The students will build on an existing baseline of classifiers and try to improve the existing results.

This project will result in an open-source software tool that will have general applicability for scientific publications. Papers with hundreds of authors are not uncommon in science, and it often takes many weeks to compile an author list in the desired order with proper affiliations and acknowledgments. We have implemented an algorithm that generates the author information for a paper based on the type of contribution of each author within the ENIGMA neuroscience consortium. This project would extend this software to read in compiled spreadsheets or forms and extract information about universities and other institutions from structured web sources, to interoperate with widely-used frameworks such as Wikidata.