TY  - JOUR
A1  - Abramova, Olga
A1  - Batzel, Katharina
A1  - Modesti, Daniela
T1  - Collective response to the health crisis among German Twitter users
BT  - a structural topic modeling approach
JF  - International Journal of Information Management Data Insights
N2  - We used structural topic modeling to analyze over 800,000 German tweets about COVID-19 to answer the questions: What patterns emerge in tweets as a response to a health crisis? And how do topics discussed change over time? The study leans on the goals associated with the health information seeking (GAINS) model, discerning whether a post aims at tackling and eliminating the problem (i.e., problem-focused) or managing the emotions (i.e., emotion-focused); whether it strives to maximize positive outcomes (promotion focus) or to minimize negative outcomes (prevention focus). The findings indicate four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Public communication is volatile over time, and a shift is evidenced from self-centered to community-centered topics within 4.5 weeks. Our study illustrates social media text mining's potential to quickly and efficiently extract public opinions and reactions. Monitoring fears and trending topics enable policymakers to rapidly respond to deviant behavior, like resistive attitudes toward containment measures or deteriorating physical health. Healthcare workers can use the insights to provide mental health services for battling anxiety or extensive loneliness from staying home.
KW  - social media
KW  - Twitter
KW  - modeling
KW  - regulatory focus theory
KW  - crisis management
KW  - text mining
Y1  - 2022
U6  - https://doi.org/10.1016/j.jjimei.2022.100126
SN  - 2667-0968
VL  - 2
IS  - 2
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Krestel, Ralf
A1  - Chikkamath, Renukswamy
A1  - Hewel, Christoph
A1  - Risch, Julian
T1  - A survey on deep learning for patent analysis
JF  - World patent information
N2  - Patent document collections are an immense source of knowledge for research and innovation communities worldwide. The rapid growth of the number of patent documents poses an enormous challenge for retrieving and analyzing information from this source in an effective manner. Based on deep learning methods for natural language processing, novel approaches have been developed in the field of patent analysis. The goal of these approaches is to reduce costs by automating tasks that previously only domain experts could solve. In this article, we provide a comprehensive survey of the application of deep learning for patent analysis. We summarize the state-of-the-art techniques and describe how they are applied to various tasks in the patent domain. In a detailed discussion, we categorize 40 papers based on the dataset, the representation, and the deep learning architecture that were used, as well as the patent analysis task that was targeted. With our survey, we aim to foster future research at the intersection of patent analysis and deep learning and we conclude by listing promising paths for future work.
KW  - deep learning
KW  - patent analysis
KW  - text mining
KW  - natural language processing
Y1  - 2021
U6  - https://doi.org/10.1016/j.wpi.2021.102035
SN  - 0172-2190
SN  - 1874-690X
VL  - 65
PB  - Elsevier
CY  - Amsterdam
ER  -