---
_id: '45270'
abstract:
- lang: eng
  text: Clinical depression is a serious mental disorder that poses challenges for
    both personal and public health. Millions of people struggle with depression each
    year, but for many, the disorder goes undiagnosed or untreated. Over the last
    decade, early depression detection on social media emerged as an interdisciplinary
    research field. However, there is still a gap in detecting hesitant, depression-susceptible
    individuals with minimal direct depressive signals at an early stage. We, therefore,
    take up this open point and leverage posts from Reddit to fill the addressed gap.
    Our results demonstrate the potential of contemporary Transformer architectures
    in yielding promising predictive capabilities for mental health research. Furthermore,
    we investigate the model’s interpretability using a surrogate and a topic modeling
    approach. Based on our findings, we consider this work as a further step towards
    developing a better understanding of mental eHealth and hope that our results
    can support the development of future technologies.
author:
- first_name: Haya
  full_name: Halimeh, Haya
  id: '87673'
  last_name: Halimeh
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Halimeh H, Caron M, Müller O. Early Depression Detection with Transformer
    Models: Analyzing the Relationship between Linguistic and Psychology-Based Features.
    In: <i>Hawaii International Conference on System Sciences</i>. ; 2023.'
  apa: 'Halimeh, H., Caron, M., &#38; Müller, O. (2023). Early Depression Detection
    with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
    Features. <i>Hawaii International Conference on System Sciences</i>. Hawaii International
    Conference on System Sciences.'
  bibtex: '@inproceedings{Halimeh_Caron_Müller_2023, title={Early Depression Detection
    with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
    Features}, booktitle={Hawaii International Conference on System Sciences}, author={Halimeh,
    Haya and Caron, Matthew and Müller, Oliver}, year={2023} }'
  chicago: 'Halimeh, Haya, Matthew Caron, and Oliver Müller. “Early Depression Detection
    with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based
    Features.” In <i>Hawaii International Conference on System Sciences</i>, 2023.'
  ieee: 'H. Halimeh, M. Caron, and O. Müller, “Early Depression Detection with Transformer
    Models: Analyzing the Relationship between Linguistic and Psychology-Based Features,”
    presented at the Hawaii International Conference on System Sciences, 2023.'
  mla: 'Halimeh, Haya, et al. “Early Depression Detection with Transformer Models:
    Analyzing the Relationship between Linguistic and Psychology-Based Features.”
    <i>Hawaii International Conference on System Sciences</i>, 2023.'
  short: 'H. Halimeh, M. Caron, O. Müller, in: Hawaii International Conference on
    System Sciences, 2023.'
conference:
  end_date: 2023-01-06
  name: Hawaii International Conference on System Sciences
  start_date: 2023-01-03
date_created: 2023-05-25T10:25:21Z
date_updated: 2024-01-10T15:16:37Z
department:
- _id: '195'
- _id: '196'
keyword:
- Social Media and Healthcare Technology
- early depression detection
- liwc
- mental health
- transfer learning
- transformer architectures
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://scholarspace.manoa.hawaii.edu/items/2ddab486-5d2f-4302-8de3-a8b24017da3d
oa: '1'
publication: Hawaii International Conference on System Sciences
publication_status: published
related_material:
  link:
  - relation: confirmation
    url: https://hdl.handle.net/10125/103046
status: public
title: 'Early Depression Detection with Transformer Models: Analyzing the Relationship
  between Linguistic and Psychology-Based Features'
type: conference
user_id: '60721'
year: '2023'
...
---
_id: '50437'
abstract:
- lang: eng
  text: The humanitarian crisis resulting from the Russian invasion of Ukraine has
    led to millions of displaced individuals across Europe. Addressing the evolving
    needs of these refugees is crucial for hosting countries and humanitarian organizations.
    This study leverages social media analytics to supplement traditional surveys,
    providing real-time insights into refugee needs by analyzing over two million
    messages from Telegram, a vital platform for Ukrainian refugees in Germany. We
    employ Natural Language Processing techniques, including language identification,
    sentiment analysis, and topic modeling, to identify well-defined topic clusters
    such as housing, financial and legal assistance, language courses, job market
    access, and medical needs. Our findings also reveal changes in topic occurrence
    and nature over time. To support practitioners, we introduce an interactive web-based
    dashboard for continuous analysis of refugee needs.
author:
- first_name: Raphael
  full_name: Reimann, Raphael
  last_name: Reimann
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
citation:
  ama: 'Reimann R, Caron M. Analyzing the Needs of Ukrainian Refugees on Telegram
    in Real-Time: A Machine Learning Approach. In: <i>Wirtschaftsinformatik</i>. ;
    2023.'
  apa: 'Reimann, R., &#38; Caron, M. (2023). Analyzing the Needs of Ukrainian Refugees
    on Telegram in Real-Time: A Machine Learning Approach. <i>Wirtschaftsinformatik</i>.
    Wirtschaftsinformatik, Paderborn, Germany.'
  bibtex: '@inproceedings{Reimann_Caron_2023, title={Analyzing the Needs of Ukrainian
    Refugees on Telegram in Real-Time: A Machine Learning Approach}, booktitle={Wirtschaftsinformatik},
    author={Reimann, Raphael and Caron, Matthew}, year={2023} }'
  chicago: 'Reimann, Raphael, and Matthew Caron. “Analyzing the Needs of Ukrainian
    Refugees on Telegram in Real-Time: A Machine Learning Approach.” In <i>Wirtschaftsinformatik</i>,
    2023.'
  ieee: 'R. Reimann and M. Caron, “Analyzing the Needs of Ukrainian Refugees on Telegram
    in Real-Time: A Machine Learning Approach,” presented at the Wirtschaftsinformatik,
    Paderborn, Germany, 2023.'
  mla: 'Reimann, Raphael, and Matthew Caron. “Analyzing the Needs of Ukrainian Refugees
    on Telegram in Real-Time: A Machine Learning Approach.” <i>Wirtschaftsinformatik</i>,
    2023.'
  short: 'R. Reimann, M. Caron, in: Wirtschaftsinformatik, 2023.'
conference:
  end_date: 2023-09-21
  location: Paderborn, Germany
  name: Wirtschaftsinformatik
  start_date: 2023-09-18
date_created: 2024-01-10T15:15:19Z
date_updated: 2024-01-10T15:20:13Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/wi2023/100/
publication: Wirtschaftsinformatik
publication_status: published
status: public
title: 'Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine
  Learning Approach'
type: conference
user_id: '60721'
year: '2023'
...
---
_id: '44383'
author:
- first_name: Peter
  full_name: Dieter, Peter
  id: '88592'
  last_name: Dieter
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
- first_name: Guido
  full_name: Schryen, Guido
  id: '72850'
  last_name: Schryen
citation:
  ama: 'Dieter P, Caron M, Schryen G. Integrating driver behavior into last-mile delivery
    routing: Combining machine learning and optimization in a hybrid decision support
    framework. <i>European Journal of Operational Research (EJOR)</i>. 2023;311(1):283-300.
    doi:<a href="https://doi.org/10.1016/j.ejor.2023.04.043">https://doi.org/10.1016/j.ejor.2023.04.043</a>'
  apa: 'Dieter, P., Caron, M., &#38; Schryen, G. (2023). Integrating driver behavior
    into last-mile delivery routing: Combining machine learning and optimization in
    a hybrid decision support framework. <i>European Journal of Operational Research
    (EJOR)</i>, <i>311</i>(1), 283–300. <a href="https://doi.org/10.1016/j.ejor.2023.04.043">https://doi.org/10.1016/j.ejor.2023.04.043</a>'
  bibtex: '@article{Dieter_Caron_Schryen_2023, title={Integrating driver behavior
    into last-mile delivery routing: Combining machine learning and optimization in
    a hybrid decision support framework}, volume={311}, DOI={<a href="https://doi.org/10.1016/j.ejor.2023.04.043">https://doi.org/10.1016/j.ejor.2023.04.043</a>},
    number={1}, journal={European Journal of Operational Research (EJOR)}, author={Dieter,
    Peter and Caron, Matthew and Schryen, Guido}, year={2023}, pages={283–300} }'
  chicago: 'Dieter, Peter, Matthew Caron, and Guido Schryen. “Integrating Driver Behavior
    into Last-Mile Delivery Routing: Combining Machine Learning and Optimization in
    a Hybrid Decision Support Framework.” <i>European Journal of Operational Research
    (EJOR)</i> 311, no. 1 (2023): 283–300. <a href="https://doi.org/10.1016/j.ejor.2023.04.043">https://doi.org/10.1016/j.ejor.2023.04.043</a>.'
  ieee: 'P. Dieter, M. Caron, and G. Schryen, “Integrating driver behavior into last-mile
    delivery routing: Combining machine learning and optimization in a hybrid decision
    support framework,” <i>European Journal of Operational Research (EJOR)</i>, vol.
    311, no. 1, pp. 283–300, 2023, doi: <a href="https://doi.org/10.1016/j.ejor.2023.04.043">https://doi.org/10.1016/j.ejor.2023.04.043</a>.'
  mla: 'Dieter, Peter, et al. “Integrating Driver Behavior into Last-Mile Delivery
    Routing: Combining Machine Learning and Optimization in a Hybrid Decision Support
    Framework.” <i>European Journal of Operational Research (EJOR)</i>, vol. 311,
    no. 1, 2023, pp. 283–300, doi:<a href="https://doi.org/10.1016/j.ejor.2023.04.043">https://doi.org/10.1016/j.ejor.2023.04.043</a>.'
  short: P. Dieter, M. Caron, G. Schryen, European Journal of Operational Research
    (EJOR) 311 (2023) 283–300.
date_created: 2023-05-03T08:55:42Z
date_updated: 2023-06-20T07:00:58Z
ddc:
- '000'
department:
- _id: '277'
doi: https://doi.org/10.1016/j.ejor.2023.04.043
file:
- access_level: open_access
  content_type: application/pdf
  creator: mateskam
  date_created: 2023-05-03T08:48:57Z
  date_updated: 2023-05-03T22:01:15Z
  file_id: '44388'
  file_name: Driver_Behavior_in_last_mile_delivery_EJOR_Final.pdf
  file_size: 1162912
  relation: main_file
file_date_updated: 2023-05-03T22:01:15Z
has_accepted_license: '1'
intvolume: '       311'
issue: '1'
language:
- iso: eng
oa: '1'
page: 283-300
publication: European Journal of Operational Research (EJOR)
status: public
title: 'Integrating driver behavior into last-mile delivery routing: Combining machine
  learning and optimization in a hybrid decision support framework'
type: journal_article
user_id: '72850'
volume: 311
year: '2023'
...
---
_id: '42631'
abstract:
- lang: eng
  text: In recent years, many cases of deep neural networks failing dramatically when
    faced with adversarial or real-world examples have been reported. Such failures,
    which are quite hard to detect, are often related to a generalization problem
    known as shortcut learning. Yet, with state-of-the-art transformer models now
    being ubiquitous in financial text mining, one cannot help but wonder how reliable
    the results conveyed in the ever-growing literature genuinely are. Against this
    background, we expose, in this work, how vulnerable contemporary financial text
    mining approaches are to shortcut learning. Focussing on the common learning task
    of financial sentiment classification, we assess, using two entity-based sampling
    strategies and our publicly-available dataset, the discrepancies between i.i.d.
    and o.o.d. performance estimates of four transformer models. Our results reveal
    that o.o.d. performance estimates are consistently weaker than those of their
    i.i.d. counterparts, with the error rate increasing by as much as 29.7%, thus,
    demonstrating how this issue can, when overlooked, lead to misleading evaluations.
    Moreover, we show how additional preprocessing steps, such as entity removal and
    vocabulary filtering, can help reduce the effects of shortcut learning by filtering
    out entity-related linguistic cues.
author:
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
citation:
  ama: 'Caron M. Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic
    Performance Estimates of Text Classification Models under Distribution Shift.
    In: <i>2022 IEEE International Conference on Big Data (Big Data)</i>. IEEE; 2022.
    doi:<a href="https://doi.org/10.1109/bigdata55660.2022.10020933">10.1109/bigdata55660.2022.10020933</a>'
  apa: 'Caron, M. (2022). Shortcut Learning in Financial Text Mining: Exposing the
    Overly Optimistic Performance Estimates of Text Classification Models under Distribution
    Shift. <i>2022 IEEE International Conference on Big Data (Big Data)</i>. 2022
    IEEE International Conference on Big Data (Big Data), Osaka, Japan. <a href="https://doi.org/10.1109/bigdata55660.2022.10020933">https://doi.org/10.1109/bigdata55660.2022.10020933</a>'
  bibtex: '@inproceedings{Caron_2022, title={Shortcut Learning in Financial Text Mining:
    Exposing the Overly Optimistic Performance Estimates of Text Classification Models
    under Distribution Shift}, DOI={<a href="https://doi.org/10.1109/bigdata55660.2022.10020933">10.1109/bigdata55660.2022.10020933</a>},
    booktitle={2022 IEEE International Conference on Big Data (Big Data)}, publisher={IEEE},
    author={Caron, Matthew}, year={2022} }'
  chicago: 'Caron, Matthew. “Shortcut Learning in Financial Text Mining: Exposing
    the Overly Optimistic Performance Estimates of Text Classification Models under
    Distribution Shift.” In <i>2022 IEEE International Conference on Big Data (Big
    Data)</i>. IEEE, 2022. <a href="https://doi.org/10.1109/bigdata55660.2022.10020933">https://doi.org/10.1109/bigdata55660.2022.10020933</a>.'
  ieee: 'M. Caron, “Shortcut Learning in Financial Text Mining: Exposing the Overly
    Optimistic Performance Estimates of Text Classification Models under Distribution
    Shift,” presented at the 2022 IEEE International Conference on Big Data (Big Data),
    Osaka, Japan, 2022, doi: <a href="https://doi.org/10.1109/bigdata55660.2022.10020933">10.1109/bigdata55660.2022.10020933</a>.'
  mla: 'Caron, Matthew. “Shortcut Learning in Financial Text Mining: Exposing the
    Overly Optimistic Performance Estimates of Text Classification Models under Distribution
    Shift.” <i>2022 IEEE International Conference on Big Data (Big Data)</i>, IEEE,
    2022, doi:<a href="https://doi.org/10.1109/bigdata55660.2022.10020933">10.1109/bigdata55660.2022.10020933</a>.'
  short: 'M. Caron, in: 2022 IEEE International Conference on Big Data (Big Data),
    IEEE, 2022.'
conference:
  end_date: 2022-12-20
  location: Osaka, Japan
  name: 2022 IEEE International Conference on Big Data (Big Data)
  start_date: 2022-12-17
date_created: 2023-02-28T08:29:30Z
date_updated: 2024-01-15T12:32:06Z
department:
- _id: '196'
doi: 10.1109/bigdata55660.2022.10020933
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/10020933
publication: 2022 IEEE International Conference on Big Data (Big Data)
publication_identifier:
  eisbn:
  - 978-1-6654-8045-1
publication_status: published
publisher: IEEE
status: public
title: 'Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic
  Performance Estimates of Text Classification Models under Distribution Shift'
type: conference
user_id: '60721'
year: '2022'
...
---
_id: '25113'
abstract:
- lang: eng
  text: Our world is more connected than ever before. Sadly, however, this highly
    connected world has made it easier to bully, insult, and propagate hate speech
    on the cyberspace. Even though researchers and companies alike have started investigating
    this real-world problem, the question remains as to why users are increasingly
    being exposed to hate and discrimination online. In fact, the noticeable and persistent
    increase in harmful language on social media platforms indicates that the situation
    is, actually, only getting worse. Hence, in this work, we show that contemporary
    ML methods can help tackle this challenge in an accurate and cost-effective manner.
    Our experiments demonstrate that a universal approach combining transfer learning
    methods and state-of-the-art Transformer architectures can trigger the efficient
    development of toxic language detection models. Consequently, with this universal
    approach, we provide platform providers with a simplistic approach capable of
    enabling the automated moderation of user-generated content, and as a result,
    hope to contribute to making the web a safer place.
author:
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
- first_name: Frederik S.
  full_name: Bäumer, Frederik S.
  last_name: Bäumer
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Caron M, Bäumer FS, Müller O. Towards Automated Moderation: Enabling Toxic
    Language Detection with Transfer Learning and Attention-Based Models. In: <i>55th
    Hawaii International Conference on System Sciences (HICSS)</i>. ; 2022.'
  apa: 'Caron, M., Bäumer, F. S., &#38; Müller, O. (2022). Towards Automated Moderation:
    Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models.
    <i>55th Hawaii International Conference on System Sciences (HICSS)</i>. 55th Hawaii
    International Conference on System Sciences (HICSS), Online.'
  bibtex: '@inproceedings{Caron_Bäumer_Müller_2022, title={Towards Automated Moderation:
    Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models},
    booktitle={55th Hawaii International Conference on System Sciences (HICSS)}, author={Caron,
    Matthew and Bäumer, Frederik S. and Müller, Oliver}, year={2022} }'
  chicago: 'Caron, Matthew, Frederik S. Bäumer, and Oliver Müller. “Towards Automated
    Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based
    Models.” In <i>55th Hawaii International Conference on System Sciences (HICSS)</i>,
    2022.'
  ieee: 'M. Caron, F. S. Bäumer, and O. Müller, “Towards Automated Moderation: Enabling
    Toxic Language Detection with Transfer Learning and Attention-Based Models,” presented
    at the 55th Hawaii International Conference on System Sciences (HICSS), Online,
    2022.'
  mla: 'Caron, Matthew, et al. “Towards Automated Moderation: Enabling Toxic Language
    Detection with Transfer Learning and Attention-Based Models.” <i>55th Hawaii International
    Conference on System Sciences (HICSS)</i>, 2022.'
  short: 'M. Caron, F.S. Bäumer, O. Müller, in: 55th Hawaii International Conference
    on System Sciences (HICSS), 2022.'
conference:
  end_date: 2022-01-07
  location: Online
  name: 55th Hawaii International Conference on System Sciences (HICSS)
  start_date: 2022-01-03
date_created: 2021-09-29T10:06:24Z
date_updated: 2024-01-15T12:37:10Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- url: http://hdl.handle.net/10125/79428
publication: 55th Hawaii International Conference on System Sciences (HICSS)
publication_status: published
status: public
title: 'Towards Automated Moderation: Enabling Toxic Language Detection with Transfer
  Learning and Attention-Based Models'
type: conference
user_id: '60721'
year: '2022'
...
---
_id: '41486'
abstract:
- lang: eng
  text: Now accounting for more than 80% of a firm's worth, brands have become essential
    assets for modern organizations. However, methods and techniques for the monetary
    valuation of brands are still under-researched. Hence, the objective of this study
    is to evaluate the utility of explanatory statistical models and machine learning
    approaches for explaining and predicting brand value. Drawing upon the case of
    the most valuable English football brands during the 2016/17 to 2020/21 seasons,
    we demonstrate how to operationalize Aaker's (1991) theoretical brand equity framework
    to collect meaningful qualitative and quantitative feature sets. Our explanatory
    models can explain up to 77% of the variation in brand valuations across all clubs
    and seasons, while our predictive approach can predict out-of-sample observations
    with a mean absolute percentage error (MAPE) of 14%. Future research can build
    upon our results to develop domain-specific brand valuation methods while enabling
    managers to make better-informed investment decisions.
author:
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
- first_name: Christian
  full_name: Bartelheimer, Christian
  id: '49160'
  last_name: Bartelheimer
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Caron M, Bartelheimer C, Müller O. Towards a Reliable &#38; Transparent Approach
    to Data-Driven Brand Valuation. In: <i>Proceeding of the 28th Americas Conference
    on Information Systems (AMCIS)</i>. ; 2022.'
  apa: Caron, M., Bartelheimer, C., &#38; Müller, O. (2022). Towards a Reliable &#38;
    Transparent Approach to Data-Driven Brand Valuation. <i>Proceeding of the 28th
    Americas Conference on Information Systems (AMCIS)</i>. 28th Americas Conference
    on Information Systems (AMCIS), Minneapolis, USA.
  bibtex: '@inproceedings{Caron_Bartelheimer_Müller_2022, place={Minneapolis, USA},
    title={Towards a Reliable &#38; Transparent Approach to Data-Driven Brand Valuation},
    booktitle={Proceeding of the 28th Americas Conference on Information Systems (AMCIS)},
    author={Caron, Matthew and Bartelheimer, Christian and Müller, Oliver}, year={2022}
    }'
  chicago: Caron, Matthew, Christian Bartelheimer, and Oliver Müller. “Towards a Reliable
    &#38; Transparent Approach to Data-Driven Brand Valuation.” In <i>Proceeding of
    the 28th Americas Conference on Information Systems (AMCIS)</i>. Minneapolis,
    USA, 2022.
  ieee: M. Caron, C. Bartelheimer, and O. Müller, “Towards a Reliable &#38; Transparent
    Approach to Data-Driven Brand Valuation,” presented at the 28th Americas Conference
    on Information Systems (AMCIS), Minneapolis, USA, 2022.
  mla: Caron, Matthew, et al. “Towards a Reliable &#38; Transparent Approach to Data-Driven
    Brand Valuation.” <i>Proceeding of the 28th Americas Conference on Information
    Systems (AMCIS)</i>, 2022.
  short: 'M. Caron, C. Bartelheimer, O. Müller, in: Proceeding of the 28th Americas
    Conference on Information Systems (AMCIS), Minneapolis, USA, 2022.'
conference:
  end_date: 2022-08-14
  location: Minneapolis, USA
  name: 28th Americas Conference on Information Systems (AMCIS)
  start_date: 2022-08-10
date_created: 2023-02-02T13:34:49Z
date_updated: 2023-02-28T08:59:38Z
department:
- _id: '195'
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/amcis2022/conf_theme/conf_theme/10/
place: Minneapolis, USA
publication: Proceeding of the 28th Americas Conference on Information Systems (AMCIS)
publication_status: published
status: public
title: Towards a Reliable & Transparent Approach to Data-Driven Brand Valuation
type: conference
user_id: '60721'
year: '2022'
...
---
_id: '24547'
abstract:
- lang: eng
  text: 'Over the last years, several approaches for the data-driven estimation of
    expected possession value (EPV) in basketball and association football (soccer)
    have been proposed. In this paper, we develop and evaluate PIVOT: the first such
    framework for team handball. Accounting for the fast-paced, dynamic nature and
    relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep
    learning architecture that relies solely on tracking data. This efficient approach
    is capable of predicting the probability that a team will score within the near
    future given the fine-grained spatio-temporal distribution of all players and
    the ball over the last seconds of the game. Our experiments indicate that PIVOT
    is able to produce accurate and calibrated probability estimates, even when trained
    on a relatively small dataset. We also showcase two interactive applications of
    PIVOT for valuing actual and counterfactual player decisions and actions in real-time.'
author:
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
- first_name: Michael
  full_name: Döring, Michael
  last_name: Döring
- first_name: Tim
  full_name: Heuwinkel, Tim
  last_name: Heuwinkel
- first_name: Jochen
  full_name: Baumeister, Jochen
  id: '46'
  last_name: Baumeister
  orcid: 0000-0003-2683-5826
citation:
  ama: 'Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious
    End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking
    Data. In: <i>8th Workshop on Machine Learning and Data Mining for Sports Analytics
    (ECML PKDD 2021)</i>.'
  apa: 'Müller, O., Caron, M., Döring, M., Heuwinkel, T., &#38; Baumeister, J. (n.d.).
    PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
    in Handball using Tracking Data. <i>8th Workshop on Machine Learning and Data
    Mining for Sports Analytics (ECML PKDD 2021)</i>. European Conference on Machine
    Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021),
    Online.'
  bibtex: '@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT:
    A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
    using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining
    for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew
    and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }'
  chicago: 'Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen
    Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player
    Actions in Handball Using Tracking Data.” In <i>8th Workshop on Machine Learning
    and Data Mining for Sports Analytics (ECML PKDD 2021)</i>, n.d.'
  ieee: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT:
    A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
    using Tracking Data,” presented at the European Conference on Machine Learning
    and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.'
  mla: 'Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework
    for Valuing Player Actions in Handball Using Tracking Data.” <i>8th Workshop on
    Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021)</i>.'
  short: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop
    on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
conference:
  end_date: 2021-09-17
  location: Online
  name: European Conference on Machine Learning and Principles and Practice of Knowledge
    Discovery (ECML PKDD 2021)
  start_date: 2021-09-13
date_created: 2021-09-16T08:33:04Z
date_updated: 2023-02-28T08:58:24Z
department:
- _id: '196'
- _id: '172'
keyword:
- expected possession value
- handball
- tracking data
- time series classification
- deep learning
language:
- iso: eng
main_file_link:
- url: https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf
publication: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
  (ECML PKDD 2021)
publication_status: inpress
status: public
title: 'PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
  in Handball using Tracking Data'
type: conference
user_id: '60721'
year: '2021'
...
---
_id: '25029'
abstract:
- lang: eng
  text: In early 2021, the finance world was taken by storm by the dramatic price
    surge of the GameStop Corp. stock. This rise is being, at least in part, attributed
    to a group of Redditors belonging to the now-famous r/wallstreetbets (WSB) subreddit
    group. In this work, we set out to address if user activity on the WSB subreddit
    is associated with the trading volume of the GME stock. Leveraging a unique dataset
    containing more than 4.9 million WSB posts and comments, we assert that user activity
    is associated with the trading volume of the GameStop stock. We further show that
    posts have a significantly higher predictive power than comments and are especially
    helpful for predicting unusually high trading volume. Lastly, as recent events
    have shown, we believe that these findings have implications for retail and institutional
    investors, trading platforms, and policymakers, as these can have disruptive potential.
author:
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
- first_name: Maryna
  full_name: Gulenko, Maryna
  id: '64226'
  last_name: Gulenko
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Caron M, Gulenko M, Müller O. To the Moon! Analyzing the Community of “Degenerates”
    Engaged in the Surge of the GME Stock. In: <i>42nd International Conference on
    Information Systems (ICIS 2021)</i>. ; 2021.'
  apa: Caron, M., Gulenko, M., &#38; Müller, O. (2021). To the Moon! Analyzing the
    Community of “Degenerates” Engaged in the Surge of the GME Stock. <i>42nd International
    Conference on Information Systems (ICIS 2021)</i>. 42nd International Conference
    on Information Systems (ICIS 2021), Austin, Texas.
  bibtex: '@inproceedings{Caron_Gulenko_Müller_2021, title={To the Moon! Analyzing
    the Community of “Degenerates” Engaged in the Surge of the GME Stock}, booktitle={42nd
    International Conference on Information Systems (ICIS 2021)}, author={Caron, Matthew
    and Gulenko, Maryna and Müller, Oliver}, year={2021} }'
  chicago: Caron, Matthew, Maryna Gulenko, and Oliver Müller. “To the Moon! Analyzing
    the Community of ‘Degenerates’ Engaged in the Surge of the GME Stock.” In <i>42nd
    International Conference on Information Systems (ICIS 2021)</i>, 2021.
  ieee: M. Caron, M. Gulenko, and O. Müller, “To the Moon! Analyzing the Community
    of ‘Degenerates’ Engaged in the Surge of the GME Stock,” presented at the 42nd
    International Conference on Information Systems (ICIS 2021), Austin, Texas, 2021.
  mla: Caron, Matthew, et al. “To the Moon! Analyzing the Community of ‘Degenerates’
    Engaged in the Surge of the GME Stock.” <i>42nd International Conference on Information
    Systems (ICIS 2021)</i>, 2021.
  short: 'M. Caron, M. Gulenko, O. Müller, in: 42nd International Conference on Information
    Systems (ICIS 2021), 2021.'
conference:
  end_date: 2021-12-15
  location: Austin, Texas
  name: 42nd International Conference on Information Systems (ICIS 2021)
  start_date: 2021-12-12
date_created: 2021-09-24T09:51:35Z
date_updated: 2023-02-28T08:58:16Z
department:
- _id: '196'
keyword:
- Retail investors
- GameStop
- Social Networks
- Reddit
- WallStreetBets
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/icis2021/social_media/social_media/13/
publication: 42nd International Conference on Information Systems (ICIS 2021)
publication_status: published
status: public
title: To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge
  of the GME Stock
type: conference
user_id: '60721'
year: '2021'
...
---
_id: '21563'
abstract:
- lang: eng
  text: Historically, the field of financial forecasting almost exclusively relied
    on so-called hard information – i.e., numerical data with well-defined and unambiguous
    meaning. Over the last few decades, however, researchers and practitioners alike
    have, following the advances in natural language understanding, started recognizing
    the benefits of integrating soft information into financial modelling. In line
    with the above, this paper examines whether contemporary attention-based sequence-to-sequence
    models, known as Transformers, can help improve stock return volatility prediction
    when applied to corporate annual reports. Using a publicly available benchmark
    dataset, we show, in an empirical analysis, that out-of-the-box Transformer models
    have the ability to outmatch current state-of-the-art results and, more importantly,
    that our proposed feature-based Transformer approach can outperform a robust numerical
    baseline. To the best of our knowledge, this is the first empirical study focusing
    on stock return volatility prediction (1) to ever experiment with state-of-the-art
    Transformer architectures and (2) to demonstrate that a model based solely on
    soft information can surpass its numerical counterpart. Furthermore, we show that
    by including an additional numerical feature into our best text-only model, we
    can push the performance of our model even further, suggesting that soft and hard
    information contain different predictive signals.
author:
- first_name: Matthew
  full_name: Caron, Matthew
  id: '60721'
  last_name: Caron
- first_name: Oliver
  full_name: Müller, Oliver
  id: '72849'
  last_name: Müller
citation:
  ama: 'Caron M, Müller O. Hardening Soft Information: A Transformer-Based Approach
    to Forecasting Stock Return Volatility. In: <i>2020 IEEE International Conference
    on Big Data (Big Data)</i>. ; 2020:4383-4391. doi:<a href="https://doi.org/10.1109/BigData50022.2020.9378134">10.1109/BigData50022.2020.9378134</a>'
  apa: 'Caron, M., &#38; Müller, O. (2020). Hardening Soft Information: A Transformer-Based
    Approach to Forecasting Stock Return Volatility. <i>2020 IEEE International Conference
    on Big Data (Big Data)</i>, 4383–4391. <a href="https://doi.org/10.1109/BigData50022.2020.9378134">https://doi.org/10.1109/BigData50022.2020.9378134</a>'
  bibtex: '@inproceedings{Caron_Müller_2020, title={Hardening Soft Information: A
    Transformer-Based Approach to Forecasting Stock Return Volatility}, DOI={<a href="https://doi.org/10.1109/BigData50022.2020.9378134">10.1109/BigData50022.2020.9378134</a>},
    booktitle={2020 IEEE International Conference on Big Data (Big Data)}, author={Caron,
    Matthew and Müller, Oliver}, year={2020}, pages={4383–4391} }'
  chicago: 'Caron, Matthew, and Oliver Müller. “Hardening Soft Information: A Transformer-Based
    Approach to Forecasting Stock Return Volatility.” In <i>2020 IEEE International
    Conference on Big Data (Big Data)</i>, 4383–91, 2020. <a href="https://doi.org/10.1109/BigData50022.2020.9378134">https://doi.org/10.1109/BigData50022.2020.9378134</a>.'
  ieee: 'M. Caron and O. Müller, “Hardening Soft Information: A Transformer-Based
    Approach to Forecasting Stock Return Volatility,” in <i>2020 IEEE International
    Conference on Big Data (Big Data)</i>, Online, 2020, pp. 4383–4391, doi: <a href="https://doi.org/10.1109/BigData50022.2020.9378134">10.1109/BigData50022.2020.9378134</a>.'
  mla: 'Caron, Matthew, and Oliver Müller. “Hardening Soft Information: A Transformer-Based
    Approach to Forecasting Stock Return Volatility.” <i>2020 IEEE International Conference
    on Big Data (Big Data)</i>, 2020, pp. 4383–91, doi:<a href="https://doi.org/10.1109/BigData50022.2020.9378134">10.1109/BigData50022.2020.9378134</a>.'
  short: 'M. Caron, O. Müller, in: 2020 IEEE International Conference on Big Data
    (Big Data), 2020, pp. 4383–4391.'
conference:
  end_date: 2020-12-13
  location: Online
  name: 2020 IEEE International Conference on Big Data (Big Data)
  start_date: 2020-12-10
date_created: 2021-03-24T13:09:55Z
date_updated: 2024-01-15T12:32:37Z
department:
- _id: '196'
doi: 10.1109/BigData50022.2020.9378134
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9378134
page: 4383-4391
publication: 2020 IEEE International Conference on Big Data (Big Data)
publication_identifier:
  eisbn:
  - 978-1-7281-6251-5
publication_status: published
status: public
title: 'Hardening Soft Information: A Transformer-Based Approach to Forecasting Stock
  Return Volatility'
type: conference
user_id: '60721'
year: '2020'
...