@inproceedings{BatzelBaum,
  author    = {Batzel, Katharina and Baum, Katharina},
  title     = {Exploring information flow on twitter: social network analysis on gender-specific sedicine},
  series = {AMCIS Proceedings 2022},
  booktitle = {AMCIS Proceedings 2022},
  number    = {1548},
  publisher = {AIS},
  address   = {Atlanta},
  isbn      = {978-1-958200-00-1},
  abstract  = {To date, sex and gender differences play only a minor role in medical research and practice, thereby putting individuals' health at risk. Gender-specific medicine, or the practice of taking these differences into account when conducting research and treating patients so far is being discussed primarily by experts. With people increasingly using social media such as Twitter for sharing and searching for health-related information online, Twitter can potentially educate about gender-specific medicine. However, little is known about the information circulation and the structure of interactions on the Twitter network discussing this topic. Results of a network analysis show that the network exhibits a community-structure, with information exchange being limited and concentrated in silos. This indicates that there is untapped potential for acquiring new information by users through interacting with individuals outside their community. Public health officials may benefit from this insight and tailor online campaigns to enhance awareness on gender-specific medicine.},
  language  = {en}
}
@article{AbramovaBatzelModesti2022,
  author    = {Abramova, Olga and Batzel, Katharina and Modesti, Daniela},
  title     = {Collective response to the health crisis among German Twitter users},
  series = {International Journal of Information Management Data Insights},
  volume    = {2},
  journal   = {International Journal of Information Management Data Insights},
  number    = {2},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2667-0968},
  doi       = {10.1016/j.jjimei.2022.100126},
  pages     = {13},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@inproceedings{AbramovaBatzelModesti2022,
  author    = {Abramova, Olga and Batzel, Katharina and Modesti, Daniela},
  title     = {Coping and regulatory responses on social media during health crisis},
  series = {Proceedings of the 55th Hawaii International Conference on System Sciences},
  booktitle = {Proceedings of the 55th Hawaii International Conference on System Sciences},
  publisher = {HICSS Conference Office University of Hawaii at Manoa},
  address   = {Honolulu},
  isbn      = {978-0-9981331-5-7},
  pages     = {10},
  year      = {2022},
  abstract  = {During a crisis event, social media enables two-way communication and many-to-many information broadcasting, browsing others' posts, publishing own content, and public commenting. These records can deliver valuable insights to approach problematic situations effectively. Our study explores how social media communication can be analyzed to understand the responses to health crises better. Results based on nearly 800 K tweets indicate that the coping and regulation foci framework holds good explanatory power, with four clusters salient in public reactions: 1) "Understanding" (problem-promotion); 2) "Action planning" (problem-prevention); 3) "Hope" (emotion-promotion) and 4) "Reassurance" (emotion-prevention). Second, the inter-temporal analysis shows high volatility of topic proportions and a shift from self-centered to community-centered topics during the course of the event. The insights are beneficial for research on crisis management and practicians who are interested in large-scale monitoring of their audience for well-informed decision-making.},
  language  = {en}
}