News

New Publication on the Evaluation of Text and Picture Cues in LinkedIn in Media Psychology

Social networks for the professional context, such as LinkedIn, are becoming increasingly popular and play an important role in assessing the expertise of job applicants.
[Translate to English:]

[Translate to English:]

The presentation of applicants in their LinkedIn profiles is done through self-generated image- and text-based information shown together in the LinkedIn profiles. In this interdisciplinary collaboration between communication science and psychology, we systematically analyzed participants’ expertise evaluation of job applicants based on self-generated textual and pictorial cues in LinkedIn profiles. The results of three experiments showed a textual primacy in expertise evaluation, as textual expertise was decisive independently of picture expertise. However, picture expertise was particularly important when text expertise was high. Placeholders always resulted in more negative judgments than high expertise pictures and sometimes even had the same effect as low expertise pictures. We discuss implications for theory building and practical consequences for self-presentation in LinkedIn.

 

Domahidi, E., Merkt, M., Thiersch, C., Utz, S. & Schüler, A. (2021). You Want This Job? Influence and Interplay of Self-Generated Text and Picture Cues in Professional Networking Service Profiles on Expertise Evaluation. Media Psychology, DOI: 10.1080/15213269.2021.1927104

 

Computational Communication Science Group

The Computational Communication Science Group is dedicated to the analysis of digital media content and communication processes as well as the associated changes for individuals and society.

The research group operates at the combination between communication science and computer science, following a strong interdisciplinary approach. The main research interest lies in the fields of (Cognitive) Biases in Digital Media and Social Consequences of Online Media Use.

In addition to traditional methods of communication science, computational methods will be applied, improved, and evaluated.

Research

The Computational Communication Science Group primarily investigates the usage of digital media and its effects on individuals and society. In addition to classical communication science methods, the group focuses on the application and evaluation of computational approaches in communication studies.

Cognitive and Algorithmic Biases in Digital Media

Social Consequences of Online Media Use

iStockphoto / monsitj
TU Ilmenau / Michael Reichel (ari)

Teaching

We offer students a sound education in communication science with close reference to how computational methods can open up insights into topics in communication and social science. This includes theoretical and practical work on methodological and algorithmic challenges that arise in the analysis of digital data and e.g., social media. In doing so, we emphasize high-quality, internationally oriented teaching, including English-language courses and degree programs.

Publications

Wilms, L., Gerl, K., Stoll, A., & Ziegele, M.(2024).Technology acceptance and transparency demands for toxic language classification – interviews with moderators of public online discussion fora.Human–Computer Interaction.https://doi.org/10.1080/07370024.2024.2307610

Andrich, A., Weidner, F., & Broll, W. (2023). Zeitgebers, Time Judgments, and VR: A Constructive Replication Study. 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 1-2). IEEE. https://doi.org/10.1109/ISMAR-Adjunct60411.2023.00007

Haim, M., Hase, V., Schindler, J., Bachl, M., & Domahidi, E. (2023). Editorial to the Special Issue:(Re) Establishing quality criteria for content analysis: A critical perspective on the field’s core method. SCM Studies in Communication and Media, 12(4), 277-288. https://doi.org/10.5771/2192-4007-2023-4-277

Binder, A., Matthes, J., Domahidi, E., & Bachl, M. (2023). Moving from Offline to Online: How COVID-19 Affected Research in the Social and Behavioral Sciences. American Behavioral Scientist, 0(0). https://doi.org/10.1177/00027642231205761

Jost, P., Heft, A., Buehling, K., Zehring, M., Schulze, H., Bitzmann, H., & Domahidi, E. (2023). Mapping a Dark Space: Challenges in Sampling and Classifying Non- Institutionalized Actors on Telegram. Medien & Kommunikationswissenschaft, 71(3–4), 212–229. https://doi.org/10.5771/1615-634X-2023-3-4-212

 
iStockphoto

Team

Head of Group

Prof. Dr. Emese Domahidi

 

Secretary

Leonie Kühn

 

Staff

Aliya Andrich, M.A.

Felipe Barreto de Souza Martins , M.A.

Christine Wendo King'ang'i, M.A.

Max Schindler, M.A.

Dr. Anke Stoll

Dr. Jingyuan Yu

Maximilian Zehring, M.A.

iStockphoto / metamorworks