TY - CHAP A1 - Batzel, Katharina A1 - Baum, Katharina T1 - Exploring information flow on twitter: social network analysis on gender-specific sedicine T2 - AMCIS Proceedings 2022 N2 - 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. Y1 - 2022 SN - 978-1-958200-00-1 IS - 1548 PB - AIS CY - Atlanta ER - TY - JOUR A1 - Baum, Katharina A1 - Baumann, Annika A1 - Batzel, Katharina T1 - Investigating innovation diffusion in gender-specific medicine BT - insights from social network analysis JF - Business & information systems engineering N2 - The field of healthcare is characterized by constant innovation, with gender-specific medicine emerging as a new subfield that addresses sex and gender disparities in clinical manifestations, outcomes, treatment, and prevention of disease. Despite its importance, the adoption of gender-specific medicine remains understudied, posing potential risks to patient outcomes due to a lack of awareness of the topic. Building on the Innovation Decision Process Theory, this study examines the spread of information about gender-specific medicine in online networks. The study applies social network analysis to a Twitter dataset reflecting online discussions about the topic to gain insights into its adoption by health professionals and patients online. Results show that the network has a community structure with limited information exchange between sub-communities and that mainly medical experts dominate the discussion. The findings suggest that the adoption of gender-specific medicine might be in its early stages, focused on knowledge exchange. Understanding the diffusion of gender-specific medicine among medical professionals and patients may facilitate its adoption and ultimately improve health outcomes. KW - health data KW - gender-specific medicine KW - social network analysis KW - diffusion of innovations KW - Twitter Y1 - 2024 U6 - https://doi.org/10.1007/s12599-024-00875-6 SN - 2363-7005 SN - 1867-0202 VL - 66 IS - 3 SP - 335 EP - 355 PB - Springer Fachmedien CY - Wiesbaden ER - 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 - CHAP A1 - Abramova, Olga A1 - Batzel, Katharina A1 - Modesti, Daniela T1 - Coping and regulatory responses on social media during health crisis BT - a large-scale analysis T2 - Proceedings of the 55th Hawaii International Conference on System Sciences N2 - 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. KW - Digital-Enabled Human-Information Interaction KW - big data KW - data mining KW - health crisis KW - social media Y1 - 2022 SN - 978-0-9981331-5-7 PB - HICSS Conference Office University of Hawaii at Manoa CY - Honolulu ER -