From structure and agency to 1.5 reality: Sharing Economy on Wikipedia and Reddit
Camilo Perdomo & Miguel Gómez
Taking a look at what Sharing Economy means in Wikipedia, it defines itself as an umbrella term related to economic activities usually regarded as online transactions. This rather broad definition led us to consider that an economic activity is not related to economic transactions, but also other forms of transaction like exchange or payment with different means than money.
These online transactions could be explained as social practices between members of a community that seek other kinds of retribution instead of just getting profit. Such social practices are forming the framework of private companies around the globe. Therefore, we wonder, as sociologists, if is there some kind of tension between social practices and companies within Sharing Economy?
This assumption will guide this first and second part of this blog posts, but it will evolve, as we did, on finding the meaning of tensions in mapping controversies within the digital world. In this stage, our point of view divides Sharing Economy in individuals and organizations that interact through several forms of commercial transaction.
However, when we applied the technical instruments, which will be explained later on, and “the monatological” interpretation on how the overlapping of individuals merge in a network in which every component (individual/node/page) has a full view of the network it inhabits (the 1,5 level standpoint ) (Latour et. at, 2012), our sociological mindset started to work differently . Therefore, in these entries, the reader (you) will explore and discover with us, our process in figuring out the networks and disassembling our sociological assumptions.
Before we depart, is necessary to define controversy and why it is worth to map them. According to Venturi […] “when actors discover that they cannot ignore each other, and controversies end when actors manage to work out a solid compromise to live together. Anything between these two extremes can be called a controversy.”(2010:261). We understood controversy as a democratic effort to see society as a living entity that is changing due to the actions of the actors, no matter if they are human or non-human. As for mapping, we define it as an enormous effort to give a place to every element in the network, instead of pick a few and build an analysis (from micro to macro social). The possibility that mapping offers is avoiding bias in hierarchies, determinism structures influence the individual or vice versa (Venturi, 2010).
With this in mind, we present in this entry the technical description and analysis on the Wikipedia page “Sharing Economy” as follows. We narrate how the networks were processed, then, we made a small analysis on a semantic network and two timelines about revisions in the Wikipedia page.
Once we analyzed the clusters and sub-clusters of Sharing Economy networks in Wikipedia, we looked at another social platform to compare how some concepts found in the Sharing Economy networks assemble new associations. We chose the social network Reddit to perform this task and focused our search on relevant keywords exclusively. This is developed in the second blog entry.
Once the article Sharing Economy is chosen as the starting point, some tools were applied to visualize the inner structure, not only from Sharing Economy but from all the links this entry has in Wikipedia (A.K.A Scrapper). The first tool applied was the software Seealsology that allows to trace the links within the section See Also on Wikipedia. This generates a map of the links within the See Also category in the entry.
Inside ‘Sharing Economy’, we found 8 subcategories that direct to 154 different entries. We created the first network that visualize how pages in Wikipedia cite each other. The links come from the text, “See Also” section and templates. The second network lies when the text is the only source of the links. Then, we added pages from outside the “sharing economy” category and its sub-categories.
When discovering the way pages cite each other, we have a network based on the degree they mention equivalent external sources. Then, we get the full text for each of the pages in the category and specific terms. We finally have a script that retrieves the revision history made by users (definition) for each page in the category of Wikipedia, from which has been produced a timeline that helped us determine when Sharing Economy was reviewed.
How is the network of “Sharing Economy” in Wikipedia?
Thanks to the input from Seealsology, we gained the knowledge on how we should start to look at networks. We should focus our attention on the groups of nodes that were closer to each other (distance). Then, it was time to run a more complex software called Gephi that not only show networks, but also can manipulate through algorithms the distribution of the nodes, prioritize the visualization of certain edges and nodes, change their size according to parameters and improve the layout of the network.
We use Gephi to perform some of these tasks in the following network and it showed us the Wikipedia Links network with new nodes and edges. This new vision made us consider that Sharing Economy encompasses companies with rather different types of economic activities and services.
The network shown below has two main concentrations of nodes (A.K.A. clusters) united by two central nodes. A priori, the graph involves a tension between social practices and private companies, which is explained as follows. The modularity (measurement to visualize either by size or color which nodes are gathered in clusters) has been applied to this graph. Thus, the software colored the nodes based on the cluster where they belong to. The nodes have been sized according to the indegree, in other words, the extent articles in Wikipedia have been cited by others.
The pink cluster contains nodes that relate largely to automobile and private sector: on one hand, we have Zipcar, Flexcar, etc. (see Figure 3 to have a clearer view). In the Turo (car rental) Wikipedia page, it is highly cited the rest of the automobile industry, thus having a high outdegree, and functions as a connector with the other cluster.
On the other hand, the green cluster encompasses concepts that can be defined as social practices: Sharing Economy, Hospitality Service, Book Swapping, etc. These nodes are located in the periphery of the green cluster, while analysing Figure 3 we can find the green nodes are related to companies that are based on social practices. For instance, Airbnb and Couchsurfing are connected with Hospitality Service and Collaborative Economy. This becomes an interesting point, because a question arises: to what extent private companies take advantage of social practices generally well perceived by the public?
In addition, we can find several subclusters, for example, Bicycle-Sharing practice comprises companies regarded to such sector. In this point, we would like to clarify why we added the picture of a meme, because we do not reach an understanding on why Uber appears connected to the green cluster, whereas it is absent in the car companies cluster. We tend to think that this is due to the fact that uber is presumably categorized as a collaborative economy.
The Network of co-occurring words extracted through semantic analysis of Sharing Economy article in Wikipedia.
Words and phrases are relevant in Wikipedia, they are embedded in a context that might give meaning and interpretation to the searched terms. Thus, a network of the word “Sharing Economy” was explored and we had the following network with nodes representing links where Sharing Economy was most frequently mentioned.
The network is classified into 5 main clusters, after applying the modularity tool. The first node that caught our attention was the node called credit card, which interacts with 4 out of the 5 clusters. It has been interpreted as the result of being the main payment method used by companies in their commercial transactions.
When looking at the 5 clusters we found the first cluster, where most of their nodes are related to bicycles. From this cluster, names of social practices like Bicycle Sharing, Bike Docks and Docking Stations are shown. It is worth to mention the node New York and other cities as nodes in this group. This could indicate that at least in New York, there are several services related to the use of bikes, although there are none company names in the cluster.
The second cluster defined as Hospitality Service practices illustrates that the social relationship between the users and the companies is mediated through several means, e.g. money for the node called membership fee. Conversely, cultural bonds appear as a condition to access a service, which is the case of the node pasporta servo, which is a service that offers to host for three days for free to Esperanto’s speakers across the world.
The third and fourth cluster, it has been defined as Platform Services. We identified social practices such as Food Delivery, Transportation Network Company, Car Sharing Service that indicate the services offered by the companies.
The fifth and last cluster identified by car organizations assemble several nodes related to services, concepts and cities that mention explicitly the car industry. This cluster could be interpreted as interactions between particular services and particular places, which made us consider the presence and use of certain services depends on their associations with local (geographically) links on Wikipedia.
While we learned about Sharing Economy and discuss among us, if it was the right choice to begin with (switching the idiot role during the research), we found other Wikipedia articles related to Sharing Economy. Applying our sociological scepticism, we found the article Platform Economy seeks to create a distinction between companies and social practices. This article regards companies like Uber or Airbnb which should be considered as Platform Economies, due to the exclusive use of money as the payment method.
For this stage, we analysed two timelines to compare Sharing Economy and Platform Economy according to the revisions. These revisions showed to what extent a topic is popular. As shown in the next graph the revisions into the article of Platform Economy began in 2017 and experienced a peak in 2018.
As for the case of Sharing Economy, we found a longer presence of users making revisions from 2013 with a peak in 2015 and a decrease onwards. We could argue that the decrease on this article happened in the same time period of the increase on the Platform Economy article, which could be interpreted as the initiation of a controversy in which certain companies try to redefine firstly under the definition of Sharing Economy and then shifted to Platform Economy.
As mentioned in our first entry, the following part is focused on Reddit. Three key-words have been searched on this network in order to find an answer for our prior question: is there some kind of tension between social practices and companies within Sharing Economy?
Protocol for Reddit: Bug hunters, learning about scripts and bots on Reddit
We started running a script (piece of software that automatise several tasks executed from an environment, in our case was on Jupyter) that aims to find specific keywords within a scope of 600 submissions (user’s posts) on Reddit . In order to get that, an API (interface to interact with a media platform, in Reddit’s case, is called PRAW Python Reddit API Wrapper) was created beforehand. In the interface you are asked to insert a date that will show data onwards, e.g. we started searching Sharing Economy on 2012 as our starting point when become popular (as it is shown in our previous timeline)
We applied the same criteria for the rest of the terms. The script also gave us the choice to avoid bots in the upcoming network (Reddit users explain them here). The keywords that we wanted to find out how to interact in Reddit are “Gig Economy”, “Sharing Economy” and “Airbnb”.
The result of running the script was two documents with information about how users interact (either are authors or comment) with Reddit submissions: body of the comment, date, subreddit (sections by topics of interest) where it belongs to, submissions url, up and down-votes, etc. Then, we exported the document with only information regarding users interaction with the submissions to Table 2 Net that outputs with a Gephi network that shows in a map nodes (submissions) and edges (users) that connect nodes.
One of the keywords chosen in the network was Sharing Economy. Such social practice is prominent, since it has been our gate-keeper considering its role, as Neyland states: […] “members of the group who are particularly useful in providing access to the group being studied, who introduce the ethnographer and aid the ethnographer’s move from location to location ”(2007:16). From this key-word, we found several social practices and companies defined by clusters and nodes in Wikipedia. Afterwards, we attempted to search the same in Reddit, and see if we find the same patterns or not, with the following result.
The network we have below illustrates how Sharing Economy interacts with 600 submissions on Reddit. It results in 1991 users (edges) interacting with these submissions (nodes). The nodes are sized by degree, i.e. the bigger the node the larger amount of users interacted with them. If two nodes are connected by one edge, it means that two submissions are interacted (either being an author or commented) by the same user.
Exploring the submissions with highest degree, we identified almost all of them treat the same topic: Rento App. This type of submission was found not only in the nodes with highest degree, but in different clusters. It is necessary to clarify that Rento App is a platform where people can rent out their unused things, thus reducing consumption and getting profit. The subreddits that contain these submissions are mainly Rento, crowdselling subreddits as icocryptoo and communities for discussing alternative currencies as such.
Apparently, Rento covers a very large amount of submissions in Reddit, here you can see an example and appears to have the posts with the highest degree. We therefore interpret some meanings: firstly, Rento highly associates itself to the social practice “Sharing Economy” and, secondly, a private company has a large influence on Reddit regarding a social practice as “sharing economy”.
The next illustration represents how the word “Airbnb” interacts on Reddit. We searched this word since it has been important in our analysis from Wikipedia. Sharing Economy, as a social practice, has a strong connection with Airbnb, within Wikipedia categories and links. Wikipedia shows that Airbnb can be seen as controversial, since some people understand it as a practice within Sharing Economy, whereas others find it as a challenge to urban dynamics and social welfare.
From this apparent controversy, we decided to dig further on Reddit and search the user’s connections with submissions containing the word “Airbnb”. Then, we got a network of 600 nodes (submissions/posts) and 538 edges (users interacting). Applying the statistical tool modularity, two clusters with colored nodes appeared. Furthermore, we asked Gephi to size the nodes by the degree of interactions, i.e. the most popular submissions within the network.
We found that submissions with the highest degree (most popular among the users by posting or commenting) and included in the pink cluster belong to Airbnb subreddit. These submissions are associated with practical questions about what clients should do with the owners, charges, cleaning manners, complaints, etc. (one of them can be checked here). In contrast, the green cluster relates to submissions that revolve around more general themes. In this cluster Airbnb is also mentioned several times and deal with travel topics, but what makes the difference with the pink cluster is due to the fact that Airbnb is not the main topic nor the submissions belong to Airbnb subreddit.
None of the submissions that we inspected refer to a political criticism to Airbnb practices, unlike in Wikipedia where Airbnb, Uber and other companies alike were heavily criticized by their performances against the existing legislation, unfair competence, etc. Such distinction between Wikipedia and Reddit might be considered as a marketing strategy from Airbnb to avoid critics within Reddit community and at the same time promote their services. Also, this could mean Reddit users for some reason are not involved at all in any discussions regarding Airbnb services or client experiences.
Another key-word that we wanted to search on Reddit is “Gig Economy”. Gig economy does not have an entry in Wikipedia however, it has been highly cited in different articles regarding Uber, Airbnb and companies as such which are known as Gig Economies. This concept works in the framework of a labour market characterized by the prevalence of short-term contracts or freelance work as opposed to permanent jobs.
When searching Gig Economy on Reddit with a limited amount of 600 submissions, these submissions were connected by 3357 users interacting. As in the previous networks, we applied modularity in order to identify the clusters with coloured nodes and sized the nodes according to the degree of interaction with users. We thus got three different clusters.
Looking in detail in the purple cluster, we can find submissions that imply critical stances to Gig Economies, such as Deliveroo. However, digging further in the green cluster, the distinction between the pink seems to be the subreddit where the submissions belong to. For instance, within the green cluster, people in InstacartShoppers discuss everything related to independent contracting for delivery services, although keeping as well a critical stance to Gig Economies. The pink cluster also maintains a disapproving approach to Gig Economies and the prominent subreddit is couriersofreddit which calls for delivery workers to interact there.
After the analysis, some conclusions are addressed. Our first blog entry started with a question: is there some kind of tension between social practices and companies within Sharing Economy?
In Wikipedia, we found an apparent tension between these social practices (Car Sharing, Sharing Economy, Bike Sharing, Book Swapping, etc.) and companies (Airbnb, Uber, etc.), insofar companies related themselves to these social practices. These tensions arise through public criticism based on the use of the law and other unfair competences. We then shifted our focus to Reddit, where we could not see those critics as heavily as in Wikipedia. On Reddit, some interactions were seen from private companies (Rento and Airbnb), e.g. covering the majority of posts, thus getting a good public image on Reddit. Nevertheless, such criticism was found on the term Gig Economy, within the Reddit submissions.
As it has been mentioned, we come from a sociological mindset that throughout the article has been slightly transformed. The previous understanding consisted of a structure wherein agents constitute such structure, therefore these two layers are permanently analyzed. However, the 1.5 vision of Latour et al. (2012) and the present research lead us to understand these layers with a single one, where both function together. In other words, all the companies, geographical locations and social practices should be understood as one layer where the entirety is relevant. Therefore, a tension between social practices and companies should not have been taken as a starting point, by doing this we avoid potential bias throughout the research. The lesson of such a statement could be that people are behind the networks and we should expect to find unexpected associations that maybe could show new participants, hidden participants or those who do not have a voice to begin with.
- Latour, B., Jensen, P., Venturini, T., Grauwin, S., & Boullier, D. (2012). ‘The whole is always smaller than its parts’ — a digital test of Gabriel Tardes monads. The British Journal of Sociology, 63(4), 590–615. doi:10.1111/j.1468–4446.2012.01428.
- Neyland, D. (2007) Organizational Ethnography. London: Sage.
- Venturini, T. (2010). Diving in magma: how to explore controversies with actor-network theory. Public Understanding of Science, 19(3), 258–273. https://doi.org/10.1177/0963662509102694