Graphryder in Treasure: Semantic Social Network Analysis


1. Working with qualitative data in a rigorous way

2. Scaling collective intelligence with Social Semantic Networks

3. Practical prospects

4. Annexes

5. Thank you and Contacts

1. Working with qualitative data in a rigorous way

Before we begin, a word about the design principles underpinning Graphryder, and SSNs themselves.

Graphryder enables practitioners of online ethnography to do visual analytics. About 30% of neurons in the human brain’s cortex are dedicated to processing visual input. The idea behind visual analytics is to use this capacity to process data. Of course, you can do network analysis using mathematical tools, but that is a slow, clunky process that requires substantial training. Proponents of visual analytics techniques suggest that you can deploy your visual cortex to see the analysis results, rather than derive them. This leads to a more immediate, deeper understanding of what the data are saying. The downside is that any glitch in the data representation, or in cognition itself can mislead the analyst into a wrong conclusion… that looks seductively right!

For this reason, rigour is essential when doing Social Semantic Network Analysis (SSNA). Using Graphryder does not require any knowledge of computer science of mathematics per se. Nevertheless, a SSN is a formal representation of an online conversation, in the mathematical sense. Full awareness of how the network was built is necessary for a rigorous interpretation of the results produced using Graphryder. Many ethnographers are not familiar with mathematical formalisms, and may be tempted by holistic, intuitive interpretations based on an incorrect understanding of what the data are actually saying. There is nothing wrong with intuition, but, in my experience, a sweeping synthesis or radical big idea is best pursued after the analyst is confident she or he understands what the data have to say.

The dangers of a lack of rigour are, unfortunately, amplified by the very thing that makes SSNA so effective: SSNs are networks. Networks are easy to visualise and intuitive to process using your eyes. They look familiar, but this familiarity is deceiving, because networks are abstractions. An untrained analyst might look at a SSN and exclaim “look, these two concepts are connected! It must mean something”. An experienced one has trained herself to state the same fact in a different way: “these two codes co-occur in the same posts. This means, to a first instance, that at least one person in the conversation has written something that refers to both codes in the same utterance”. There is nothing metaphysical about “connection”. In network science, the meaning of a connection has to be formally defined a priori. Network economist Fernando Vega-Redondo has a wonderful quip: when you talk about networks, you need to define what a link is.

1.1. Seeing through the eyes of others: understanding collective intelligence

Imagine you are walking the streets of a foreign city. You could not pass for a local: your clothes, your looks, the way you look around give you away as a stranger. Groups of people look your way, talk to each other. Some call out to you, but you do not know the local language well enough to understand what they want. Also, you find it hard to decode their attitude. Are they just being friendly? Do they want something? Is there maybe some hostility in their behaviour? Maybe you have walked into a dodgy neighbourhood – hard to say, this does not look much different from where you started your walk.

You are unable to figure out your situation, but this is not because you have no information. The information is right in front of you, in the way people are addressing you and their stance. What you are missing is context: you do not know how these people who walk the same street as you perceive the situation, and your role in it. Maybe they see you as a curiosity, and they would like to make friends. Maybe they see you as a trespasser, or even a hostile or mocking presence.

Many important real-life decisions are made in similar situations:

  • Risk management. Consider a municipal-sponsored initiative to incentivize electronics recycling for car owners, specifically geared at having in-dash GPS systems recycled and reused. It is supposed to be a win for the environment and the consumers. On the one hand, in-dash navigation systems use non-renewable resources like copper, gold, and silver for wiring, aluminium for casing, and sometimes even rare earths magnets like neodymium. A municipal task force is formed to steward the campaign, retailers and repair shops are enlisted to offer discounts on service and purchases to customers who consent to having their in-dash navigation systems recycled when they upgrade or replace the units. But the campaign fails; very few units are recycled, despite the financial incentives to do so. What could be going on? Is there insufficient awareness about the campaign because of low outreach and social media promotion efforts? Are the logistics not sufficiently user-friendly (e.g. there are only a few dealerships and garages where trade-ins are possible, and they are not conveniently located)? Are people anxious about data privacy and thus reluctant to recycle navigation systems? If the latter is the problem, would a greater incentive outweigh the data privacy concerns, or is another approach necessary to mitigate those anxieties? All the scenarios are compatible with the data (the campaign is not succeeding), but they have different implications for what you should do to fix the problem.
  • Stakeholder engagement. Consider a government agency evaluating whether an issue needs regulating, and how to regulate it. For example, the issue could be greenhouse gas emission standards for hybrid-electric vehicles. Initial proposals meet with opposition from some of the stakeholders, including both the component manufacturers and some environmental groups advocating for environmental standards. Why do they oppose the proposal? Do the upstream stakeholders find it too restrictive, while the environmental advocates think it is insufficient? What changes would it take for the different groups to accept it?
  • Foresight. Consider someone trying to anticipate future events. They see long-term dynamics playing out, but it is impossible to make a priori prediction about their implications. For example, you might see that a country, following the victory of a Green Party led coalition in its most recent election, is undertaking a transition to sustainable energy and decarbonizing its economy. Imported diesel-fueled cars are now subject to heavy tariffs, domestic production of diesel automobiles is sunsetting over the next five years, and wind and solar energy production is incentivized through government subsidies. Automotive industry workers, who are a sizable voting contingent, are offered free retraining in green skills and guaranteed job placement in the sustainable energy sector. Will public consensus around the common good and an environmentally friendly future help usher the country in question into a new era of sustainability? Or will disgruntled fossil fuel industry workers reject the prospect of re-training and new careers, foment social unrest, and put labour unions at odds with sustainable energy policies? Will a right-wing government that is more friendly to the fossil fuel lobby win in the next election?

In all these cases, and others, all-important information is encoded in the way other people see things. Moreover, in most cases you cannot just acquire this information by asking a few people. Because:

  • Asking direct questions is vulnerable to cognitive biases.
  • Most of the time we do not know what question to ask.

Most people find it difficult to correctly process their own attitudes. They will often misunderstand, misrepresent or just lie. For example, voting polls tend to underestimate the electorates of racist candidates, because some voters do not like to say they intend to vote for them (even in an anonymous poll).

Consider ethnography as a possible tool that helps us see through other people’s eyes. Ethnography is a qualitative research technique aimed at discovering how a certain group of humans perceives a set of issues. Its unique value lies in that its findings encode the culture and worldview of the group being studied. The word “ethnography” is derived from the Greek words ethnos (meaning folk, people, nation) and grapho (meaning “I write”). This makes it especially suited to situations where you are interested in the social and cultural meanings that arise organically from human interactions. Ethnographers like to ask “what does this issue look like from out here? How does this specific group of people see it? How does it see itself with respect to it?”

The interest of ethnographers is not on people as individuals. It is on people as members of a community. At its origins (in the 19th century), the discipline focused on the culture of tribes and other political units that had become part of European colonial empires. Colonial administrations, like the person walking the streets of a foreign city in the example at the beginning of these lessons, had access to information, but not to the cultural context that allowed interpreting that information. Over time, ethnographers have expanded their definition of “community”, from the original ethnos to communities of interest, or practice. You can design ethnographies of almost any human group you can think of, truck drivers in Bavaria, inner-city crack users in the United States, or environmental activists in Scandinavia. Although the definition of community has broadened since the early days, ethnographers have maintained their focus on the community, not the individual, as their unit of analysis.

You might ask: what is so interesting about communities? Surely communities are only the sum of the people that constitute them, right? It turns out no, they are not. This is because people in communities interact all the time. Interaction is what gives rise to the evolution of conventions (in some cultures it is appropriate for two male friends to walk hand in hand, but in others it is not) and shared meanings (riding a bicycle to work is interpreted as a socially positive sign of environmental awareness in Denmark, as a socially negative sign of low disposable income in Georgia; in the United States, hybrid vehicle ownership can positively signify a progressive socially conscious perspective in a liberal state like California, or negatively mark someone as a “liberal snowflake” in a conservative place like rural Ohio ). Through interaction, people can share information, and find, together, what that information means. Over time, people who interact more come to form clusters of individuals that see the world in similar ways. These clusters are what we call “communities”.

Communities constantly try to make sense of the world around them, and they do so via their members interacting with each other. Is artificial intelligence good or bad? For whom? Is membership of the European Union good or bad for our country? For ourselves? Why? Is our culture threatened by modernity? And by immigration? Almost no one tries to answer these questions by sitting alone in a room and thinking about them, or going to a library. Most people look for answers by talking to friends, family members, religious or political leaders. Even those few who do their own research immediately share their results and interpretations with the people around them.

In other words, people behave like nodes in a distributed network to process information into meaning. Scholars call this process collective intelligence, a term minted by Pierre Lévy in 1997. Collective intelligence is a “flocking” process, whereby people coordinate locally and without central command, giving rise to large-scale phenomena. It can perform amazing feats: just consider Wikipedia, the largest encyclopaedia ever created. With no office, no money and no command structure, without even knowing each other, hundreds of thousands of contributors coordinate every day in building a highly coherent object, Wikipedia itself. In Edgeryders, we believe collective intelligence is a powerful engine of sense-making, and a critical tool for finding solutions to the most pressing societal challenges. In the next lesson, you will learn more about how we think about it.

1.2. Emergence in social dynamics

Many of us suspect that we, as a species, have lost control of key issues affecting our future. Take your pick: climate change. Rising inequalities. Loss in biodiversity. Rogue finance. Permanent budgetary crisis of nation states. Persistent unemployment. Permanent crises of political non-correspondence. Everyone agrees that these are bad, and something should be done. And indeed ideas are proposed; petitions signed; laws passed; elections gained and lost over these issues. But then, something happens that defeats most of what we do. These phenomena, somehow, seem to exist out of the reach of our control.

What is going on here?

The explanation lies in a process called emergence. We see it in the natural world, at all scales: a great many interacting agents give rise to a phenomenon that exists at a level higher than that of the agents themselves. A good example is a cloud. What we call a cloud is a pattern of suspended water molecules. All water molecules are identical: you can look at one until the end of time, and you will never be able to determine whether it is part of a cloud, or what, in it, causes the cloud to exist. In fact, individual molecules float in and out of the cloud all the time, without the cloud itself losing its coherence. A cloud is not “a thing”: it is an emergent property of water molecules fluctuating in the planet’s atmosphere. There are many other examples like this. Our minds are emergent properties of our neurons passing electrical and chemical signals to each other; ant colonies are an emergent property of the mutual interaction of many individual ants. And so on.

This line of reasoning carries onto social and economic phenomena. In 2011, GPS manufacturer TomTom marketed its products under this headline:

“You are not stuck in traffic. You are traffic.”

This makes sense. If you are sitting in your car, stuck in a traffic jam, you don’t think that the traffic jam is your fault, because you have driven on the same road before, behaving in exactly the same way, and there was no traffic jam then! It must be those other people in their cars. But everyone in the traffic jam is thinking the same thing. The traffic jam is an emergent property of many people (including you) commuting on the same roads, in a more or less synchronised way. No one is in charge of it: not an individual, not an organisation. There is no conspiracy. No one person or organisation can stop it.

Now try this: “You are not the victim of fake news. You are fake news.” This applies to all of us who have reshared content on social media without fact checking. I guess it is most of us: for sure it happened to me. You can even argue that it applies even to those of us who have social media accounts, even if we do carefully check everything we say; troll farms would not exist if social media were not so incredibly popular among, well, all of us.

Let’s try some more:

“You are not suffering from climate change. You are climate change.”

“You are not on the receiving end of an economic crisis. You are the economic crisis.”

“You are not the victim of a loss in biodiversity. You are the loss in biodiversity.”

You get the point. You may disagree with some of these, but most people agree that some behaviours that look completely innocent to us combine with a myriad of other factors (physical, cultural, institutional, financial…) to produce emergent phenomena.

Emergent phenomena are not necessarily negative. Try this: “Circular economy is not an impossible utopia. You are the circular economy!”

And notice a pattern: most really big, really important issues are emergent. They do not exist at the human scale: it does not make sense to say that you are “congested” when driving your car. Individual cars cannot be congested: only traffic can. This statement is equivalent to saying that a single H2O molecule cannot be liquid, only trillions of molecules of water interacting can.

Earlier we claimed that many of these large societal issues seem to exist out of our reach. This turns out to be correct, in the specific sense that they do not exist at the individual scale at which we, individual humans, operate. It makes them especially hard – some even say conceptually impossible – to approach and solve at the individual human level.

The collective intelligence is also an emergent phenomenon, because system-level coherence arises from individual-level behaviour, with no central planner calling the shots. In other words, collective intelligence exists at the same scale of large, scary issues like climate change or rising inequalities. At Edgeryders, we explore how to channel it into a tool that we, as humans, can at least point at those issues. It’s a long shot, but the stakes are so high that we believe it’s worth trying.

Scholarly study of emergence goes under the name of complex systems science. In science, a system is called complex when it has properties that cannot be deduced by studying its component parts. Scientists have concluded that these properties arise from the interaction of its component parts, just like traffic jams arise from the interaction between cars. For this reason, much of complex systems science uses networks to model the emergence of such properties. Networks are mathematical objects that formalise the idea of interaction and connection: they consist of entities, called nodes, and their connections, called edges or arcs. Network models can produce sophisticated, life-like behaviour starting from nothing but identical interacting parts and randomness. For example, Lászlo Bárabási proved in 1999 that a specific type of network called scale-free arises from taking a very simple network, and adding nodes with the rule that new nodes are more likely to connect to the existing nodes that have more connections. To see this rule in action, you can play with a simulation of Bárabási’s model here.

We treat collective intelligence as an emergent phenomenon of certain types of online conversations; so it makes sense to conceive of the conversations themselves as networks.

2. Scaling collective intelligence with Social Semantic Networks

2.1. Can conversations scale?

Open, online conversations can be large, with many thousands of participants; this makes them an attractive engine of collective intelligence. We examine a special type of network called semantic social networks, and learn how to use them for representing and analysing large online conversations.

Open conversations are great settings to generate collectively intelligent outcomes. Participants start by choosing a topic; they then contribute pieces of knowledge that they might already have, and then work together to piece them into an organic exploration of the topic at hand. By “organic”, I mean that the knowledge in the group at the start of the conversation becomes integrated.

This happens in two ways. One is validating knowledge. A participant might contribute a piece of knowledge, for example “demand for electric parts in vehicles has been rising in the past ten years”. The other participants, then, have a chance to validate it (“yes, I have seen those statistics too”), totally or partially (“that’s certainly true in Spain, where I come from”); to dispute it (“wait, are we sure about that? I remember reading the opposite!”); or to problematize it (“those statistics focus on new vehicle markets, but that does not say anything about consumers who prefer classic cars. There might even be an increased demand for refurbished older cars because so many people are concerned about data leaks and cyber security issues with all those electronics in cars.”). The result of this process is a common knowledge base. It will generally not be fully consistent or complete, but participants will have become aware of the inconsistencies and gaps.

The second way to knowledge integration is its shared interpretation, in the sense of spotting patterns of correlation and causation. In the example above, people might propose candidate causes for the phenomenon of increased demand for car electronics, discuss them, connect them with other social phenomena (for example increased interest in “smart electronics” across the board, or a rise in environmental consciousness).

So, an open conversation aggregates and rationalises the knowledge available to participants, previously dispersed across individuals, into a shared knowledge base. It also aggregates contextual information available to individuals into a shared set of interpretations. The main problem with all this is the scale at which this all happens. On the one hand, adding more participants means bringing to the table both more information and more processing power. On the other hand, human conversations are limited in scale: some researchers believe that they break down above only four participants!

Online conversations promise the possibility to overcome these limits to scale. They happen in writing, and therefore in asynchronous or semi-synchronous form. This removes the frustration of participants waiting for their turn to speak, and writes each contribution into the group’s long-term memory. Depending on the capabilities of the software used, online conversations also allow people to spontaneously self-organise in groups debating different aspects of the topic at hand. Potentially, this solves the problem of scale: the popular forum Reddit is divided into topic-based “subreddits”, and the largest “reddits” have tens of millions of subscribers.

However, large conversations create another problem: they are difficult to navigate and harvest for results. They can consist of hundreds, or thousands, or tens of thousands of individual contributions, authored over several months or even years. All of them are local in the sense that the participants authoring them were reacting to, and addressing, a specific subset of people in the conversation in the specific context of the phase of the debate they were having at the time. It can be difficult to somehow wrap up this avalanche of content into a compact, elegant form.

2.2. Introducing Semantic Social Networks

At Edgeryders, we believe this can be done by representing large-scale conversations by a specific kind of network, called Semantic Social Network (SSN). A SSN is a network where:

  • Nodes represent people (hence it is a social network).
  • Edges represent interactions in the conversation. Alice links to Bob as she has replied to Bob’s contribution.
  • Edges encode semantics: we know what concepts Alice expressed in reacting to Bob’s contribution.

The process evolves through several steps.

Step one is hosting an online conversation, and making sure it is lively, respectful and truth-oriented. This conversation will be your content’s generator. Hosting a conversation appropriate for being a collective intelligence engine requires some software, but most importantly it requires community management skills. Any piece of text written by participants in the context of an online conversation is called a contribution. Posts and comments in a blogging platform, and posts in a forum platform, are examples of contributions. Community management is treated in a separate course, and not covered here.

Step two is associating one or more keywords to each contribution to the conversation. This happens by creating something we call an annotation, a document that refers to one single snippet of text in one contribution, and contains the keyword(s) associated by the analyst to it. Keywords are generated applying ethnographic techniques; the process works best when it is professional ethnographers doing it. In that case, the keywords are known as ethnographic codes, or simply codes. Ethnographers are trained in two things. First, they represent the point of view of the people in the conversation, and not that of the ethnographers themselves, or anybody else for what matters. Second, they maintain a coherent ontology of the concept covering the conversation at hand. What you end up with is not a folksonomy, with partially overlapping or redundant codes, but a proper ontology. Ethnographic coding is also treated in a separate place, and not covered here. Here we begin after we have completed steps one and two.

At this point, we are ready to build the SSN. To do so, start by inducing the social network of your conversation.

  • Create a node for each participant.
  • Go through the contributions by each participant. For each one of them, find out the one person that the participant is talking to. Be aware that this is a simplification: sometimes people address more than one person in the same contribution. The simplification is necessary in order to gain access to the armoury of simple network mathematics, because a simple network’s edges always connect two, and only two, nodes. There is indeed a class of objects, called hypergraphs, whose edges connect more than two nodes, but they are rarely used in practice. You can represent the connection between n nodes in a simple graph by connecting with ordinary edges all n nodes to all other n-1 nodes.
  • At this point, you have a social network, representing the conversational interaction between participants. You also have semantics, encoded in the annotations and the relative keywords (figure 1).

Figure 1. One interaction in an annotated online conversation, expressed as a social network.

Discard the text of the contribution altogether, replacing it with the codes imported from the annotations regarding that contribution. What you obtain is two participants, connected by one interaction, which in turn encodes some semantics: “Ayman is talking to Ben about code 1 and code 2”. In other words Ayman and Ben are connected by their interaction around code 1 and code 2. This is the atom of a semantic social network (figure 2)

Figure 2. Discard the contribution’s full text and replace it with the codes from the annotations relative to that contribution. What obtains is an “atom” of a SSN.

  • Do this for each contribution and each participant, to get the complete SSN of the conversation (figure 3).

Figure 3. A very simple SSN example.

This is only a “toy” network, of course. To give you an idea, our first full-blown SSNA project in 2017 had 330 participants, nearly 4,000 contributions, 6,000 annotations and 1,200 codes. But the good news is this: no matter how large, a network is still a network, and we have mathematical and software tools to treat them. One thing that makes sense, for example, is filtering it, and looking at the filtered graph. In figure 4 we can see our toy network filtered by the code #design. It shows the conversation around that code.

Figure 4. The subnetwork of #design

The conversation around design is, in this case, disconnected. Five people are talking about it, but they are not talking to each other. Instead, they have formed two groups. Jay, Hegazy and Ben form one, while the other consists of Fran and Gazbia. This means that the validation of knowledge around design has been limited: the two groups might have reached different, even opposite conclusions, and not even be aware of each other. We, as analysts, need to be careful in interpreting the conclusions of this debate.

Another thing that you can do with a SSN is flip it around, to build a network where the nodes represent codes, and the edges represent contributions that contain both codes. Instead of codes connecting people, now we have people (the authors of those contributions) connecting codes (figure 5)

Figure 5. The SSN of figure 3, shown from a semantic perspective.

We call this the co-occurrences graph: because it is induced by multiple codes co-occurring in the same contribution. It is a semantic view, the SSN shows which concepts connect to each other, and which people are making these connections. Each edge can be interpreted as someone making an association between the two codes at the extremities of the edge. By extension, we can interpret this view, the network of co-occurrences, as the association pattern that the community makes across all codes. You may be familiar with association patterns from films where a psychologist speaks a “trigger” word, and her patient has to say the first word that comes to her mind upon hearing the trigger word. The co-occurrence on a SSN encodes the same idea, except that the entity making the associations is the entire conversation, not any individual participant. The co-occurrences graph is a true emergent property of the online conversation, in the sense of Lesson 2: there is no central command deciding that any two codes should be connected, and how strongly.

Notice that the semantic graph is disconnected: there is no association path that goes from #dementia to #arduino. People seem to be having two different conversations. Again, this is an interesting emergent property of the conversation. It is truly collective, because it results from free interaction across participants.

Notice that edges in a SSN can be weighted. When edges are weighted, they are associated with a scalar (a number): the higher the scalar, the stronger the connection. For example, from figure 3 we know the connection between #design and #accessibility is made by Gazbia, while talking to Fayez. This shows up as an edge between these two codes in figure 5. But what if Fayez makes the same connection? In this case, rather than adding a second edge between #design and #accessibility, we can increase by one unit the weight of the already existing edge. The weight is interpreted as “number of posts that make this connection”. In the social interaction network, weight normally denotes “number of interactions between these nodes”.

To summarise, we have argued that open, online human conversations are a powerful and potentially scalable engine of collective intelligence. We introduced a specific type of network, called semantic social network, and proposed it is an attractive method to represent a large scale online conversation, and study it at the scale of the whole conversation. What is attractive in it is that semantic social networks are networks, and networks are well-understood mathematical objects. SSNs, like any other networks, can be analysed in terms of their mathematical properties, many of which have to do with their global “shape”, or topology.

3. Practical Prospects

In this guide, we have provided a primer on how the combination of ethnographic methods and SSNA analysis works to glean topically targeted insight. This kind of ethnographic research can also be scaled and expanded to cover various topics related to the car industry, sustainability, and the circular economy in order to gain nuanced insights into complex socio-cultural dynamics that may be of interest and use to a variety of stakeholders. By delving into the perceptions and narratives of meaning-making and behavioral practices within communities under study, researchers can uncover valuable perspectives on issues ranging from electronic waste management to sustainable mobility practices. Here are some examples of how our approach could be leveraged to explore such topics:

  1. Electronics Recycling Awareness Among Car Consumers Campaigns: A study could investigate community attitudes among car owners and car drivers towards electronics recycling initiatives in urban areas across Europe. Questions may revolve around awareness levels, perceived barriers to participation, and the effectiveness of incentive structures in encouraging recycling behavior among car owners and drivers.
  2. Comparative Perceptions of Sustainability Related to Electronics in Cars vs. Electric Cars: Researchers could discover whether sustainability-inclined consumers see a spectrum of sustainable solutions linked to a gradual incorporation of longer-lasting and recyclable electronics into cars, or whether they are more likely to perceive a binary dichotomy between diesel vehicles with selected electronic features and hybrid / electric vehicles? How might their perception of the threshold for achieving sustainability inform their consumer choices?
  3. Transition to Electric Vehicles: Ethnographic research could explore how different societal groups perceive and adopt electric vehicles. Stakeholders may be interested in understanding factors influencing EV acceptance, such as concerns about charging infrastructure accessibility, perceptions of driving range, social status associated with vehicle ownership, and propensity to focus on the future (potentially related to age and parenting status).
  4. Circular Economy Practices in Automotive Manufacturing: Researchers could examine the organizational dynamics and concomitant cultural norms within European automotive plants to understand the adoption of circular economy principles. Questions might focus on the integration of re-manufacturing processes (logistics, sorting, assembly, packaging, etc.), production line employee and managerial attitudes towards waste reduction initiatives, and cultural, economic, and logistical barriers to implementing closed-loop systems.
  5. Urban Mobility Cultures: A study could investigate urban communities’ perspectives on shared mobility services, such as car-sharing and ride-hailing platforms. Stakeholders may seek insights into factors shaping usage patterns, attitudes towards private and cooperative vehicle ownership, and the role of social trust and risk perceptions in collaborative consumption models.
  6. Public Perception of Sustainable Materials in Vehicle Design: Ethnographic research could explore consumer attitudes towards “green” materials like biocomposites and natural fibres used in car interiors and exteriors. Topics explored could include significance of sustainability certifications, perceptions of material quality, willingness to pay for renewable materials, and the influence of peer preferences on purchasing decisions.

3.1. Potential stakeholders interested in undertaking such projects include (but are not limited to)

  1. Municipal Departments of Transportation: Seeking insights to inform sustainable urban mobility strategies and infrastructure planning.
  2. Automotive Manufacturers: Seeking to align product development with evolving consumer preferences and sustainability goals, as well as evolving environmental regulations nationally and internationally.
  3. Recycling Businesses: Seeking to optimize electronics recycling processes and increase awareness and participation rates among consumers.
  4. Environmental NGOs: Seeking to understand societal attitudes towards sustainable transportation and advocate for policy interventions related to “greening” the automotive sector through material and behavioral changes.
  5. Consumer Research Firms: Seeking to refine market research efforts to identify emerging trends, opportunities, and threats in the automotive sector related to sustainability and circular economy initiatives.

In sum, all of us – car owners, car manufacturers, sustainability researchers, policy makers, are both the products and makers of culture, and this is a snapshot of how leveraging ethnographic research methodologies can provide a rich understanding of the cultural dimensions surrounding topics like electronics in cars, sustainable behavior, and circular economy practices within the European context (and beyond!). By offering nuanced empirical answers to stakeholders’ questions and concerns, our research design and methods can help inform decision-making and contribute to bringing about positive environmental and economic changes in the automotive production and consumption sectors.

4. Annexes

4.1. Community management

At Edgeryders, we recognize the importance of community management and have developed a comprehensive approach to nurturing our global network of over 6000 members. Our philosophy is rooted in empathy, understanding, and active participation, creating a space where individuals can connect, collaborate, and thrive.

We have a team of community managers who are responsible for fostering a welcoming and inclusive environment where members feel empowered to share their ideas and contribute to our work.

Also, we prioritize ethical research by ensuring informed consent from the community. As a “collective intelligence engine,” user contributions fuel research projects. To address this, we utilize a “consent funnel.” This funnel prompts new users with a questionnaire upon their first post which explains Edgeryders’ research goals and clarifies how content is used, empowering users to make informed decisions about participation. This system ensures transparency and ethical data collection within the Edgeryders community.

To successfully do that we engage in diverse types of activities:

  1. Welcoming Newcomers: Setting the Stage for Connection
    As new members join our community, we prioritize making them feel welcome and included. Our community managers take a personalized approach, reaching out to each newcomer. This personalized touch introduces them to the community, highlights recent happenings, and connects them to relevant topics based on their interests.
  2. Nurturing Conversations: Fostering Meaningful Engagement
    Our community managers actively engage in discussions, ensuring that every voice is heard and valued. We encourage a culture of respectful and constructive dialogue, fostering a sense of inclusivity and belonging. By paying attention to what members are saying, we can identify areas of interest and connect individuals with shared passions, further strengthening the community’s bonds.
  3. Synthesize, Curate, Engage: Sharing Our Stories
    We strive to share our work, values, and successes in a clear and engaging manner. Our community managers synthesize key information from various projects and initiatives, creating informative blog posts and platform posts. This content is then disseminated through our mailing list and social media channels, reaching a wider audience and attracting new members.
  4. Embracing Offline Engagement
    While online interactions form the backbone of our community, we also recognize the value of offline engagement. We organize both online and offline events, providing opportunities for participants to connect in person. These events foster a sense of companionship and deepen the relationships between members.
  5. Creativity and Storytelling: Inspiring Innovation
    We believe that creativity and storytelling are powerful tools for community engagement. We encourage members to share their experiences, ideas, and challenges. We also host open calls for engaging stories, encouraging members to create fictional narratives that can spark new ideas and collaborations.
  6. Community Journalism
    We believe that community journalism is an effective way to raise awareness about our work and connect with a wider audience. We partner with professionals in the field of journalism to create articles, blog posts, and videos that showcase the diverse perspectives and expertise within our community.

By embracing these strategies, we strive to create a vibrant and thriving community where members feel valued, connected, and empowered to make a positive impact. Our community management approach is not just about managing interactions; it’s about fostering a sense of belonging, driving innovation, and shaping a more collaborative and engaged world.

4.2. Graphryder User Guide

Continues here: Graphryder: Users guide

5. Thank you and Contacts

Thank you for reading this introduction to the Semantic Social Network Analysis of the ethnographic data collected during the Treasure project.

A Semantic Social Network (SSN) is an ethnographic corpus arranged in a special network form. This arrangement allows us to process a very large corpora of ethnographic data, overcoming the limitations of human short term memory. It is a useful tool for ethnographers.

The text is only a brief introduction to its use and you are strongly encouraged to try things out for yourself using a Graphryder instance. The Treasure data can be accessed through this link.

Edgeryders team is available to answer questions. Ask them in the Research Network’s forum space.