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Sheffield University
A Social Scientist's Point of View in Navigating Computational Methods Against Disinformation

As a social scientist studying the intersection of disinformation and social media, I have participated in the SoBigData++ TNA program at the Department of Computer Science (University of Sheffield). 

PSE
Studies of international migration: TNA visit in Paris 2024

During my three-week stay from March 4th to March 22nd, 2024, I had the privilege of residing in the vibrant city of Paris, hosted at the Paris School of Economics. Collaborating closely with Prof. Hillel Rapoport, my time was dedicated to immersive research endeavours and active participation in various academic seminars and events. The stay provided an invaluable opportunity to engage in stimulating discussions and exchange ideas with fellow scholars, enriching my understanding of contemporary economic theories and methodologies.

data strategy cover
Data Strategy 2024 Conference

SoBigData event, Empowering Enterprises through BigData and AI - The European Data Strategy 2024, organised by the Consiglio Nazionale delle Ricerche - Institute of Information Science and Technologies, in cooperation with Re-Imagine Europa, took place on the morning of Tuesday 13th February, in Brussels.  

Big Data: Innovation and Efficiency in Business Transformation

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.

Race Affinity Mark Cavendish: Official Race Profiles 2023 Tour of Oman Stage 1 (most favorable; left panel), and 2023 Giro dItalia Stage 18 (least favorable; right panel). Our simple initial approach was already capable of detecting which type of races the sprinter Mark Cavendish would thrive in and which races he would have difficulties in
A Sports Data Science TNA visit in Pisa

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. 

Aalto University
Complexity at Aalto University: TNA Visit to Aalto 2023

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.

Saravanan
Big Data and Inclusion: Fostering Diversity and Innovation in the Computer Science Community

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.

Unmasking the True Nature of New Twitter Accounts

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.

Text to Time Series Representations
Text to Time Series Representations: Towards Interpretable Predictive Models

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.