The inaugural session of the SoBigData Digital Coffee webinar series, titled Big Data: Innovation and Efficiency in Business Transformation, took place on February 28th from 5 to 6 pm (CET time).  

This event, organized by the Institute of Management of Scuola Superiore Sant'Anna in collaboration with CNR and the SoBigData Research Infrastructure, marked the beginning of a series of four webinars, each exploring various aspects of Big Data and its implications for innovation, entrepreneurship, ethics, and impact.

A TNA experience report by Bram Janssens from Ghent University, Belgium. The goal of the stay was to combine cycling analytics with geospatial analytics, as geospatial analytics can heavily improve the current solutions in the field. 

The past two months, from October 1, 2023 to November 30, 2023, I had the privileged opportunity to spend two months at the Knowledge Discovery and Data Mining Laboratory at the Consiglio Nazionale delle Ricerche (CNR) in Pisa, Italy. It has been a truly inspiring period. 

Last Summer I could make a TNA visit to the group of Prof. Kimmo Kaski at Aalto University, which turned out to be a major opportunity to learn and catch up with the most recent developments in complex systems science.

Authored by Dr. Vijayalakshmi Saravanan, Assistant Professor in Computer Science at the University of South Dakota and promoter of diversity in the field of big data and computer science.

With over 300 million monthly active users, Twitter sees thousands of new accounts created daily. But how many of these new accounts are genuine, and how many are ephemeral users who quickly cease interactions with the platform? Adopting an innovative approach, we produced a dataset comprising over 500,000 new Twitter accounts created in April 2020. Identified immediately after registration, these accounts were closely monitored for 21 days, with a final status check conducted two years post-registration.

This article introduces the concept of integrating Time Series Analysis (TSA) with Natural Language Processing (NLP) to create a new representation, TOTS (Text tO Time Series). TOTS converts text data into time series, preserving the sequential structure of the text. The conversion process includes tokenization, feature extraction, and aggregation. The article explores various feature extraction techniques, such as linguistic features and sentence embeddings, and aggregation methods like average and max aggregation.