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Together with Prof. Dr. Mirco Schönfeld, Junior Professor of Data Modelling & Interdisciplinary Knowledge Generation, Faculty of Languages and Literatures, UBT, we want to investigate what social media data can tell us about nutritional behaviour in African countries and whether the well-known social media platforms and their algorithms recommend different nutritional information in African countries than they do in Germany.

Are you interested and do you have some experience in Computational Social Science Methods? Then get in touch with us here?

Topic 1: What can Social Media Data tell us about Dietary Behaviour and Food Environments in African Countries?

Social media platforms such as Instagram, Twitter, Reddit or Tumblr are increasingly being used by individuals on a global scale to keep the public informed about everyday issues, such as decisions about what, how and where to eat, or to express opinions on how certain food issues are viewed and evaluated. Marketing research has long been making use of this freely available “behavioural log data” (Trattner & Elsweiler, 2019) to continuously monitor and mine consumer behaviour and preferences (Salampasis, Paltoglou & Giachanou, 2014). These data also do not go unnoticed in nutrition research (Trattner & Elsweiler, 2019). With the desire to derive insights into dietary behaviour from these data, a few researchers have set about analysing them in an exploratory manner (Bartelmeß, 2020). Thus, certain attitudes towards nutritional issues (Shah, Srivastava, Savage & Mago, 2019), understandings of a healthy diet (Saura, Reyes-Menendez & Thomas, 2019), the detecting and characterizing of eating-disorder communities (Moessner, Feldhege, Wolf & Bauer, 2018; Wang, Brede, Ianni & Mentzakis, 2017), the description of food environments (Lynch, Knezevic & Mah, 2022; Nguyen, Q. C. et al., 2017) and the localisation of obesogenic environments have been revealed by using visual, text and geotagged social media data and social computational analysis methods.

One region for which little nutrition-related behavioural data is generally available is Africa. The master's thesis aims to find out which social media nutrition data is available for which African countries and what statements can be derived from this data about dietary behaviour and food environments by using social computational analysis methods.

References

• Bartelmeß, T. (2020). Möglichkeiten der Analyse von Social-Media-Daten für die Ernährungskommunikation. In J. Godemann & T. Bartelmeß (Hrsg.), Ernährungskommunikation (S. 1-25). Wiesbaden: Springer.
• Lynch, M., Knezevic, I. & Mah, C. L. (2022). Exploring food shopping behaviours through a study of Ottawa social media. Appetite, 168, 105695.
• Moessner, M., Feldhege, J., Wolf, M. & Bauer, S. (2018). Analyzing big data in social media: Text and network analyses of an eating disorder forum. The International journal of eating disorders, 51 (7), 656-667.
• Nguyen, Q. C., Meng, H., Li, D., Kath, S., McCullough, M., Paul, D. et al. (2017). Social media indicators of the food environment and state health outcomes. Public health, 148, 120-128.
• Salampasis, M., Paltoglou, G. & Giachanou, A. (2014). Using social media for continuous monitoring and mining of consumer behaviour. International Journal of Electronic Business, 11 (1), 85.
• Saura, J. R., Reyes-Menendez, A. & Thomas, S. B. (2019). Healthy Diet in Social Media: Utilizing Networks and User Generated Content to Gain a Deeper Understanding of Nutrition Behavior Management.
• Shah, N., Srivastava, G., Savage, D. W. & Mago, V. (2019). Assessing Canadians Health Activity and Nutritional Habits Through Social Media. Frontiers in public health, 7, 400.
• Trattner, C. & Elsweiler, D. (2019). What online data say about eating habits. Nature Sustainability, 2 (7), 545-546.
• Wang, T., Brede, M., Ianni, A. & Mentzakis, E. (2017). Detecting and Characterizing Eating-Disorder Communities on Social Media. In M. de Rijke, M. Shokouhi, A. Tomkins & M. Zhang (Hrsg.), Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (S. 91-100). New York, NY, USA: ACM.

Topic 2: A Comparative Analysis of Nutrition-related Social Media Recommendations in African Countries

What we see on Social Media is highly tailored to our needs. Recommendation algorithms compose and order our content streams or timelines in order to keep us interested in the platform and increase interaction with the platform. What is picked and ranked high up in the stream is influenced by our interests, our past interaction with the platform, and certain context factors. This master's thesis sets out to evaluate different factors that might influence the composition of content in a Social Media profile.

In an experimental design the same information search behaviour is carried out on the profiles. However, the profiles will be created and accessed from different geographical locations with a special emphasis on African countries. Thematically, the focus is on nutrition topics - on healthy and sustainable nutrition. The master's thesis aims to provide answers to the question of what social media does to profiles in different geographical regions that display identical information-seeking behaviour and leave identical usage traces. Furthermore, the master thesis will compare which ideas of healthy and sustainable nutrition are constructed by the algorithms in the different geographical regions.