@article{WilkschAbramova2023,
  author    = {Wilksch, Moritz and Abramova, Olga},
  title     = {PyFin-sentiment},
  series = {International journal of information management data insights},
  volume    = {3},
  journal   = {International journal of information management data insights},
  number    = {1},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2667-0968},
  doi       = {10.1016/j.jjimei.2023.100171},
  pages     = {10},
  year      = {2023},
  abstract  = {Responding to the poor performance of generic automated sentiment analysis solutions on domain-specific texts, we collect a dataset of 10,000 tweets discussing the topics of finance and investing. We manually assign each tweet its market sentiment, i.e., the investor's anticipation of a stock's future return. Using this data, we show that all existing sentiment models trained on adjacent domains struggle with accurate market sentiment analysis due to the task's specialized vocabulary. Consequently, we design, train, and deploy our own sentiment model. It outperforms all previous models (VADER, NTUSD-Fin, FinBERT, TwitterRoBERTa) when evaluated on Twitter posts. On posts from a different platform, our model performs on par with BERT-based large language models. We achieve this result at a fraction of the training and inference costs due to the model's simple design. We publish the artifact as a python library to facilitate its use by future researchers and practitioners.},
  language  = {en}
}
@article{BaumgartBoosEckstein2023,
  author    = {Baumgart, Lene and Boos, Pauline and Eckstein, Bernd},
  title     = {Datafication and algorithmic contingency},
  series = {Work organisation, labour \& globalisation},
  volume    = {17},
  journal   = {Work organisation, labour \& globalisation},
  number    = {1},
  publisher = {Pluto Journals},
  address   = {London},
  issn      = {1745-641X},
  doi       = {10.13169/workorgalaboglob.17.1.0061},
  pages     = {61 -- 73},
  year      = {2023},
  abstract  = {In the context of persistent images of self-perpetuated technologies, we discuss the interplay of digital technologies and organisational dynamics against the backdrop of systems theory. Building on the case of an international corporation that, during an agile reorganisation, introduced an AI-based personnel management platform, we show how technical systems produce a form of algorithmic contingency that subsequently leads to the emergence of formal and informal interaction systems. Using the concept of datafication, we explain how these interactions are barriers to the self-perpetuation of data-based decision-making, making it possible to take into consideration further decision factors and complementing the output of the platform. The research was carried out within the scope of the research project 'Organisational Implications of Digitalisation: The Development of (Post-)Bureaucratic Organisational Structures in the Context of Digital Transformation' funded by the German Research Foundation (DFG).},
  language  = {en}
}