Social Data Science (SDS)
When people interact with one another online, data is generated. The same applies to interactions with systems, such as cloud service providers and artificial intelligence systems. The analysis of this data is part of decision-making processes. For example, organisations use social media data to evaluate the success of communication strategies, business use it to segment customers, and politicians use it to target voters. This data is also relevant for forecasting performance metrics (predictive analytics).
A wide variety of actors are not only trying to understand the users, but also to influence them. As an example, social bots can influence the political mood in social networks. Although the automated processing of data is partly necessary and socially desirable, for example to combat hate speech on the internet, it also gives rise to new questions on topics such as technology acceptance, trust, and privacy.
The Social Data Science team is dedicated to the scientific investigation of data from the interactions between humans and machines. This includes research into the methods with which the data can be interpreted, such as social network analysis and natural language processing. It also means that we critically reflect on the questions that arise from the analysis of this data by actors with various aims.
Central research questions include::
- How can social media data be analysed and used for targeted decision-making?
- What are the consequences of working and living with technologies such as cloud services and social media?
- Ross, Björn; Pilz, Laura; Cabrera, Benjamin; Brachten, Florian; Neubaum, German; Stieglitz, Stefan (2019). Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks. European Journal of Information Systems (EJIS).
- Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social Media Analytics – Challenges in Topic Discovery, Data Collection, and Data Preparation. International Journal of Information Management (IJIM) 39, pp. 156-168.
- Stieglitz, S., Meske, C., Ross, B. & Mirbabaie, M. (2018). Going Back in Time to Predict the Future – The Complex Role of the Data Collection Period in Social Media Analytics. Information Systems Frontiers (ISF).
- Ross, B., Potthoff, T., Majchrzak, T. A., Chakraborty, N. R., Lazreg, M. B. & Stieglitz, S. (2018). The Diffusion of Crisis-Related Communication on Social Media: An Empirical Analysis of Facebook Reactions. In Proceedings of the 51st Annual Hawaii International Conference on System Sciences (HICSS), Big Island, Hawaii, Januar 2018, pp. 2525-2534. (ausgezeichnet mit dem Best Paper Award des Tracks Electronic Government)
- Wilms, K., Stieglitz, S., Müller, B. (2018). Feeling Safe on a Fluffy Cloud – How Cloud Security and Commitment Affect Users’ Discontinuance Intention. In Proceedings of the 39th International Conference on Information Systems (ICIS).