As part of the Next Generation Internet Forward project, Michał Palińskiand Kristóf Gyódi have developed a new tool that facilitates the exploration of key technological challenges and related policy issues. The starting point of the study was a thesis about the movement of public debate participants within information bubbles on topics related to new technologies and related social challenges. Confronting this problem, the authors mapped articles, blog notes, and other types of content shared on social networks. The main focus was on topics such as ethical artificial intelligence, education during a pandemic, online privacy, and technological aspects of combating climate change.
Based on text-mining methodology, the authors researched and identified topics shared on social media platforms (they analyzed more than 110,000 articles on Twitter, Reddit, and Hacker News). The interactive report resulting from the analysis allows you to explore topics and articles on your own, based on your users» interests. In the report, the authors outlined 6 topic areas that they identified as key to the development of new digital technologies. In the study, they were particularly interested in the issues of inclusivity of new technologies, their ethicality, and their wide availability to users. A summary of the analysis of these areas can be found in the case study titled: „Access, Inclusion and Justice”. Within this topic area, they identified clusters of articles on such topics as the IT wage gap, ethical programming, and regulations such as the EU Copyright Directive and U.S. Section 230.
During the seminar Kristóf Gyódi and Michal Paliński will present the research methodology and data science tools used to enable efficient scraping of articles from web portals and blogs. They will also talk about the challenges of text clustering and researching the popularity of specific sources of articles shared on social networks. The meeting is especially directed to people interested in new technologies and their regulation, media analysis and widely understood text mining.