## 4 Case Studies
### Re-Contextualizing Digital CH: The Legacy of Social Media Data
In our case study, we developed and explored a smart web interface (see Figure 5) that assists individuals in managing their social media image inheritance across platforms by contextualizing and customizing the data for and with users. We applied two artificial intelligence (AI) tools, YOLOv8 ([[https://yolov8.com/](https://yolov8.com/)]([https://yolov8.com/](https://yolov8.com/)), last accessed 4 March 2024) to enable contextualized image search, and DeepFace ([[https://pypi.org/project/deepface/0.053/](https://pypi.org/project/deepface/0.053/)]([https://pypi.org/project/deepface/0.053/](https://pypi.org/project/deepface/0.053/)), last accessed 4 March 2024), for face, person, and limited emotion recognition. Additionally, users could create their own filters and image clusters, customizing the data display according to their needs and understanding in collaboration with the AI. In this way, they prepared data access for other individuals or larger groups in the case of their death. The application further aims to support users in managing their data nowadays across social media platforms. For example, if a picture should be deleted, users can define which platforms it should be deleted from. However, the possibilities of contextualizing the images were limited and depended on available and reliable tools and users' mental models of meaningful image clusters. This highlights a challenge of digital data legacy because the created clusters might not correspond to the \"heir's\" mental model and thus cause misunderstanding and non-transparency when handling the data inheritance. However, our tool focused solely on the data creator's perspective, excluding the heir's user experience. We identified more challenges when testing the interface with users and their personal social media images. For one, there is also a lack of awareness of the consequences and understanding why maintaining a digital data legacy is important. Further, data maintenance and death preparation are also associated with serious, low-fun activities that some users prefer to avoid. In similar situations, HCI research applies nudges [136; 137] or gamified approaches [138; 139] to motivate users implicitly into action-taking and behavior-changing. In the context of digital data legacy for CH re-contextualization, such approaches can foster multiperspectivity and sensemaking but are ethically questionable. Additionally, non-prepared digital data legacy triggers questions about the remembrance of users regarding what will be remembered and shared of an individual's digital identity after their death or how the data and digital identities will be used continuously by platform owners and other members of the person's network. We see further outstanding questions about how digital data will contribute to shaping CH or how online community cultures, e.g., gaming communities, could support the re-contextualization of analog CH. Our example also emphasized AI's efficiency in supporting sensemaking and facilitating access to one's and other's data. However, this also questions how much we want independent AI systems to define our digital CH and legacy and to what extent multiperspectivity, inclusion, and sensemaking play a part in it. This last case study focused on technologically enabled digital social and cultural developments that increasingly develop parallel to analog CH and that must be considered in CH re-contextualization. Figure 5: Creating a digital legacy based on social media images: This image shows the AI-supported interface with meaningful filters created by the AI and self-created clusters by the user to pass on to their heirs.