Refine
Document Type
- Article (12)
- Conference Proceeding (5)
- Doctoral Thesis (5)
- Postprint (3)
- Monograph/Edited Volume (1)
Is part of the Bibliography
- yes (26) (remove)
Keywords
- social media (26) (remove)
Durch den demographischen Wandel wird das Erwerbspersonenpotential und damit die Anzahl erwerbstätiger Personen, insbesondere die Zahl der Fachkräfte in den kommen-den Jahren in Deutschland zurückgehen. Aufgrund dessen wird es für Arbeitgeber zukünftig schwieriger werden, qualifizierten Nachwuchs zu finden. Aufgrund seiner Alterstruktur und der zunehmenden Arbeitsverdichtung ist der öffentliche Dienst, sowie der Teilbereich der öffentlichen Verwaltung, stärker als andere Arbeitgeber mit der Notwendigkeit konfrontiert, mittelfristig externes Personal zu rekrutieren. In Anbetracht dessen ging die Arbeit der Frage nach, inwieweit die öffentliche Verwaltung das hierfür geeignete, innovative Instrument des Social - Media - Personalmarketings bereits imple-mentiert hat und wie sich das ermittelte Ergebnis erklären lässt. Hinsichtlich der aktuellen Anwendung konnte festgestellt werden, dass Social - Media - Personalmarketing erst vor Kurzem in der öffentlichen Verwaltung implementiert wurde und aufgrund dessen gegenwärtig primär zur operativen Personalgewinnung genutzt wird. Als erklärende Einflussfaktoren konnten im Rahmen einer empirischen Untersuchung die mangelnde Relevanz des Personalmarketings als Aufgabe der öffentlichen Verwaltung, der aktuelle Per-sonalbestand und dessen digitale Kompetenzen, sowie die hierarchisch geprägten Kommunikationswege innerhalb der öffentlichen Verwaltung ermittelt werden. Mit Ausnahme der Kommunikationswege decken die Faktoren sich mit denen der Privatwirtschaft. Die öffentliche Verwaltung ist dazu angehalten, den aktuellen Ausprägungsgrad der Amtshierarchie kritisch zu hinterfragen, um das volle Potential des Social - Media - Personalmarketings zukünftig zu heben.
Strategic social media use positively influences organizational goals such as the long-term accrual of social capital, and thus social media information governance has become an increasingly important organizational objective. It is particularly important for humanitarian nongovernmental organizations (HNGOs), whose work relies on accurate and timely information regarding socially altruistic behavior (donations, volunteerism, etc.). Despite the potential of social media for increasing social capital, tensions in governing social media information across an organization's different operational levels (regional, intermediate, and national) pose a difficult challenge. Prominent governance frameworks offer little guidance, as their focus on control and incremental policymaking is largely incompatible with the processes, roles, standards, and metrics needed for managing self-governing social media. This study offers a notion of dynamic and co-evolutionary process management of multi-level organizations as a means of conceptualizing social media information governance for the accrual of organizational social capital. Based on interviews with members of HNGOs, this study reveals tensions that emerge within eight focus areas of accruing social capital in multi-level organizations, explains how dynamic process management can ease those tensions, and proposes corresponding strategy recommendations.
Individuals have an intrinsic need to express themselves to other humans within a given community by sharing their experiences, thoughts, actions, and opinions. As a means, they mostly prefer to use modern online social media platforms such as Twitter, Facebook, personal blogs, and Reddit. Users of these social networks interact by drafting their own statuses updates, publishing photos, and giving likes leaving a considerable amount of data behind them to be analyzed. Researchers recently started exploring the shared social media data to understand online users better and predict their Big five personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. This thesis intends to investigate the possible relationship between users’ Big five personality traits and the published information on their social media profiles. Facebook public data such as linguistic status updates, meta-data of likes objects, profile pictures, emotions, or reactions records were adopted to address the proposed research questions. Several machine learning predictions models were constructed with various experiments to utilize the engineered features correlated with the Big 5 Personality traits. The final predictive performances improved the prediction accuracy compared to state-of-the-art approaches, and the models were evaluated based on established benchmarks in the domain. The research experiments were implemented while ethical and privacy points were concerned. Furthermore, the research aims to raise awareness about privacy between social media users and show what third parties can reveal about users’ private traits from what they share and act on different social networking platforms.
In the second part of the thesis, the variation in personality development is studied within a cross-platform environment such as Facebook and Twitter platforms. The constructed personality profiles in these social platforms are compared to evaluate the effect of the used platforms on one user’s personality development. Likewise, personality continuity and stability analysis are performed using two social media platforms samples. The implemented experiments are based on ten-year longitudinal samples aiming to understand users’ long-term personality development and further unlock the potential of cooperation between psychologists and data scientists.
Examining the dissemination of evidence on social media, we analyzed the discourse around eight visible scientists in the context of COVID-19. Using manual (N = 1,406) and automated coding (N = 42,640) on an account-based tracked Twitter/X dataset capturing scientists’ activities and eliciting reactions over six 2-week periods, we found that visible scientists’ tweets included more scientific evidence. However, public reactions contained more anecdotal evidence. Findings indicate that evidence can be a message characteristic leading to greater tweet dissemination. Implications for scientists, including explicitly incorporating scientific evidence in their communication and examining evidence in science communication research, are discussed.
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.
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.