Emo:on-driven design inspects people’s emotional experiences in a targeted context and study the concerns behind these emo:ons. The is connected to an understanding to people’s personal experiences. Social media is playing an increasingly important role in daily life, as the technologies to do social media mining is developing, it is becoming a great database for learning about people. These data from social media is becoming a valuable dataset that is scaled, updated in every moment, and its access is being increasingly democratized. These rich data are bringing a lot of opportunities for emo:on designers to scale up research and design. They form a dynamic dataset of people’s emo:ons related to their preferences, opinions, geographic locations, communities as well as social news and events. However, social media mining has not yet been employed for emo:on-driven design. Currently there are various tools and methodologies that support design conceptualization, but with no tools yet to employee the dynamic social media data as a material. This project investigate the role of the user- generated content on social media to emo:on- driven design, bridging the two with meaningful stories that reflect people’s subjective experiences in emotional events. Later on, experiments and tests were done to explore a workflow of how can designers discover the meaningful stories from Instagram. This resulted in a basic approach and a set of data exploration techniques. Therefore, the goal was set to developed a data exploration tool for designers, which facilitate them to do controllable explorations and flexible searches with with the qualitative, unstructured social media data. The target group is designers who are interested to look for meaningful stories in social media but have less experiences in data mining. In the end, a data exploration tool is developed , and it is evaluated by designers on the effectiveness aspect and experience aspect.
The evaluation indicated that the story exploration in social media with the tool could sensitize the designers with their target users’ daily life emotional experience, and can broaden the designer's horizon by having an overview on the possible experiences. The tool is recommended to be used in the very early phase of emo:on-driven design.

Many active citizens that work on improving their neighborhoods have in-depth local knowledge (small data), but are oftentimes not directly involved in the decision-making processes of city officials. A top-down push is witnessed to make smart use of open data for the development of social solutions and services. However, this creates a gap for those citizens that are willing to work on social innovations, but lack technical skills and people that do have these required skills. Numbers and facts have taken a leading role in the decision-making process, which makes data concrete building blocks for argumentation to make knowledge claims about the world (D’Ignazio, 2017). To also include those active citizens in the decision-making process, this graduation project explores how data can support active citizens in their participatory activities and how they can make a valuable connection to their local knowledge by means of design. In Delfshaven Rotterdam, active citizens have taken over the control of public parks and have realized many green initiatives to improve the livability of the neighborhoods. Organizing and managing these parks themselves does not come without facing issues. The context of these green initiatives is taken as a case study for the project to research what problems and obstacles occur in self-managing and organizing these parks and to understand how data can be off support. The conducted research with the green initiatives revealed that they can find common ground with the municipality to show that they are working on mutual goals to improve the liveability of the neighborhoods. Furthermore, combining their deep-rooted insights (small data) gained, by being actively involved in the neighborhoods, with factual data can create a more complete picture of the problems that occur in the neighborhoods. Literature review substantiated that in order to form a complete picture of these real-world problems, both numbers and stories should be combined (Wang, 2016). Standing stronger in political debate happens when stories can be combined with numbers which compels others. In order for factual data to support their initiatives, the challenges the initiators face should be clearly defined. To support citizens to work with data, hackathons can be used as a means to facilitate this process. A more human-centered approach has been identified for such an event by focussing on collective issue articulation as a design practice (DiSalvo & Lotado, 2011) to support defining and analyzing a problem through the sharing of local stories. Stimulating multiple stakeholders to bring their perspectives on a problem into a discussion helps to gain an understanding of the underlying, factors, relations, consequences and actors. This ‘data-analysis process’ can be seen as story-finding, which has a more appropriate approach for non-technical citizens to work with data (Bhargava et al., 2017). To explore the potential of story-finding as data analysis process, several workshop have been conducted that all included data explorations. For these workshops, tools were prototyped as means to facilitate the process. The insights gained showed that in order to find relevant data to explore, first problems that are faced should be clearly defined and communicated. This lead to the the design vision: “A co-creation/ participatory process that helps expert initiators to translate neighborhood problems in a structured way into local cases by combining their local perspectives with factual data to tell a compelling story.” This projected resulted in a process toolkit ‘Blik op Buurt’ which proposes a clear structure to collaboratively surface and define problems in the neighborhood and helps to select potential partners and communicate these insights to them. By going through a story-finding process, initiators are stimulated to make use of their contextual data (small data) about the neighborhood problem in order to reveal missing information and knowledge. This process is facilitated by a set of tools. A collective setting not only stimulates shared ownership to commit to tackle these problems, but also support the self-organization in how they can break down the problems so that their stories can be comprehended and be supported with data from different perspectives. To evaluate the finale outcome and validate the effect, the Blik op Buurt process and tools should be tested by a citizen initiative through conducting a problem case study.