Data Science for Digital Collaboration

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    Federated Learning for Credit Risk Assessment
    ( 2023-01-03) Lee, Chul Min ; Delgado Fernandez, Joaquin ; Potenciano Menci, Sergio ; Rieger, Alexander ; Fridgen, Gilbert
    Credit risk assessment is a standard procedure for financial institutions (FIs) when estimating their credit risk exposure. It involves the gathering and processing quantitative and qualitative datasets to estimate whether an individual or entity will be able to make future required payments. To ensure effective processing of this data, FIs increasingly use machine learning methods. Large FIs often have more powerful models as they can access larger datasets. In this paper, we present a Federated Learning prototype that allows smaller FIs to compete by training in a cooperative fashion a machine learning model which combines key data derived from several smaller datasets. We test our prototype on an historical mortgage dataset and empirically demonstrate the benefits of Federated Learning for smaller FIs. We conclude that smaller FIs can expect a significant performance increase in their credit risk assessment models by using collaborative machine learning.
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    On Left and Right: Understanding the Discourse of Presidential Election in Social Media Communities
    ( 2023-01-03) Zhou, Lina ; Tao, Jie ; Wang, Kanlun
    As a promising platform for political discourse, social media becomes a battleground for presidential candidates as well as their supporters and opponents. Stance detection is one of the key tasks in the understanding of political discourse. However, existing methods are dominated by supervised techniques, which require labeled data. Previous work on stance detection is largely conducted at the post or user level. Despite that some studies have considered online political communities, they either only select a few communities or assume the stance coherence of these communities. Political party extraction has rarely been addressed explicitly. To address the limitations, we developed an unsupervised learning approach to political party extraction and stance detection from social media discourse. We also analyzed and compared (sub)communities with respect to their characteristics of political stances and parties. We further explored (sub)communities’ shift in political stance after the 2020 US presidential election.
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    Introduction to the Minitrack on Data Science for Digital Collaboration
    ( 2023-01-03) Zhou, Lina ; Paul, Souren ; Schwade, Florian