Observing the decentralised social web
(one telescope at a time)
This article was submitted to and is under review for the 2017 Workshop on Web Observatories. As an article on my site it is subject to ongoing updates and improvements. To comply with submission deadlines for the workshop, a static snapshot from 22nd January 2017 is at archive.org. A printable copy in the ACM layout is on GitHub.
This is a short report on iteratively developing and using a Personal Web Observatory for one year. Herein are described the architecture of the technical system and, subjectively, the social and personal impact of daily use. The goal is to provoke further conversation about what it means to 'observe' individuals, and the benefits and drawbacks of this on a personal scale. We also discuss how peoples' attitudes towards social media and personal publishing may impact the quality of data collected by Web Observatories.
Studying communities through passive and active digital traces, as Web Observatories are designed to do [9, 26], brings with it a host of privacy, ethical, and methodological concerns. Attempts to address online privacy issues in general are being made with a push towards re-decentralising the Web , in part through open Web standards and work on promoting personal data stores as alternatives to centralised or third-party services. Using this momentum can benefit the Web Science community as well as their observees (though it brings with it its own set of challenges). Decentralisation is applied to Web Observatories in particular through the idea of a Personal Web Observatory  as a user-controlled (as opposed to third-party imposed) means of collecting and tracking data.
I believe that the perspectives of both subject and experimenter are valuable when it comes to studying people in new ways. Taking inspiration from the reflective practices of the Quantified Self community, I progressively built features into my own personal website which allow monitoring and visualisation of day-to-day aspects of my life, and used it continuously for the period of one year. Immersion in the ongoing effects of self-tracking in a decentralised manner led to greater insight in working towards Personal Web Observatories than either developing a personal data store or engaging in self-tracking through third-party services could do alone.
This article begins by outlining related work on Web Observatories, Quantified Self, and the decentralised Social Web. It includes a summary of the architecture of the personal data store being studied, and the types of data collected. I discuss the results in terms of psychological impact of the experiment, evolving motivations, and expected and unexpected consequences. In conclusion I relate these results to new and existing challenges for Web Observatories and Web Scienctists who want to study data generated by Personal Web Observatories or similarly decentralised systems.
Personal Web Observatories
Web Observatories concern the use of peoples' digital footprints as the subject of academic inquiry [9, 26]. Such data encompasses all manner of online and offline activities, and it may be collected passively by systems with which individuals interact, or actively logged, or some combination of the two. In order to address negative privacy implications of collecting and analysing this data,  introduces the idea of a Personal Web Observatory. Personal data stores are presented as an architecture for a decentralised Web Observatory, to allow individuals to maintain control over their data whilst still participating in scientific studies or otherwise releasing their data for use by third parties.
A Personal Web Observatory relies on individuals opting in to self-tracking activities; connecting their personal data store to sensors or user interfaces.
Self-tracking and Quantified Self
There have been a small number of high profile instances of individuals collecting a large volume of data about themselves, then offering it up for auction [5, 20]. Even more common are those who track data about themselves in order to: orchestrate behavioural changes; monitor bodily functions; learn new things about themselves; discern cause-and-effect relations; aid memory; manage information and life events; make better decisions; or just for the fun of it (for surveys of self-tracking motivations and tools, see [12, 2, 7, 23]). This personal self-tracking is disruptive to traditional notions of big data and data science .
The Quantified Self (QS) community is large, global, and growing. In-person conferences and meetups are held regularly [14, 15]. Whilst they use a wide range and combination of DIY and off-the-shelf devices for self-tracking [15, 21], what is particularly interesting are their reflective practices. QS practitioners engage in creative ways to collect, visualise, and understand data about themselves. At meetups, talks are focussed around deep personal insights, optimisation and improvement, and active self-awareness, rather than technology or tools [2, 21, 15].
However, the psychological impact of quantifying day-to-day activities is more complex than detecting trends and setting goals. Self-tracking may work against our best interests as interpretation of data is both subjective, and prone to re-interpretation at each viewing. There is also evidence that attempting to enforce a particular behaviour can have the opposite effect. Peoples' memories and impressions are easily influenced by external and internal factors . A participant in  expressed concern about becoming "compulsive" about data collection, and  also describes how many off-the-shelf self-tracking services do not provide adequate means to aid user reflection.
Another negative effect of QS tracking is poor security of sensor software and third-party storage which can compromise individuals' data . Plus, using third-party software typically comes with terms of service which are problematic from a privacy perspective.
Quantified self tracking is gamification of non-play activities, and  describes gamification as having surveillance at its root.  emphasises that quantification is a tool for governance and control and  concurs that statistics are historically used to manage populations, and this form of control is internalised by individuals for management of self. Prevalence of QS devices and habits can serve to normalise surveillance. Further, QS tracking in the workplace is being introduced in ways that are becoming increasingly difficult to opt-out of, and raises unrealistic expectations of workers "fostered by a quantified, machine-like image of human productivity" .
Nonetheless,  describes QS participants who pushing back against the expectations and categories of the companies whose devices they use with their own interpretations of their data, "calling into question who gets to do the aggregation and how".
Building a Personal Web Observatory
In this section I describe my Personal Web Observatory setup. One notable constraint from the outset was that in order to minimise maintenance requirements the system needed to be no more complicated to run than a personal website. As such it is implemented in PHP and runs on standard shared Web hosting, with a MySQL backend.
The system (which is named sloph) constitutes a central database which is an RDF quadstore (layered on top of a MySQL database by the ARC2 PHP library). Using a graph data model facilitates the addition of new data without the overhead of updating schema or models in the core code.
For incoming data, it uses two endpoints: publication and notification, which implement the server portions of ActivityPub and LDN respectively. To compensate for overlapping standards, the publication endpoint additionally includes bridging code which converts Micropub requests in ActivityPub requests before proceeding, and the notification endpoint converts Webmentions into LDN. Data is processed to examine its validity, and stored as-is in the quad store. Publication data all uses the AS2 syntax and vocabulary (or extensions thereof), and notification data is stored using whatever vocabulary is sent by the notifying party. Data from each endpoint are stored in different graphs in the quad store to manage provenance.
The notifications endpoint can receive unsolicted messages from any LDN Sender, which may be somebody else's personal data store, or a clientside tool. In addition, some third-party services have been configured to send notifications to the notification endpoint. Webmention.io and GitHub have webhook settings, which are set to the notifications endpoint. They forward JSON data, which is easily convertible into JSON-LD, used by the LDN standard. Brid.gy is a service which runs in the background and monitors my social media profiles for replies to my posts, then sends these as Webmention notifications. Upon certain new incoming notifications, the endpoint sends a request to the PushOver API, which sends a push notification to my Android phone.
Data may be retrieved as individual items (AS2 Objects or Activities), or in sets (AS2 Collections); all are identified by URLs. Content negotiation is employed so that requesting clients may access the data in any desired RDF syntax, or HTML. For HTML display, simple templates are created for each 'type' (or shared between a set of similar types) of data item. It is expected that most requests come from Web browsers, so the HTML content is delivered most often. However, other applications or services may consume the data, including readers (which may mix together multiple streams of data for the user), aggregators (which read the data and perform some manipulation or calculation over it to display the results) or publishing clients (which offer editing or combining of existing data). All data is public; I did not implement access control.
The publication endpoint performs additional functions for data enhancement and distribution. It automatically adds missing metadata to posts if necessary (for example, published date and author), as well as storing new relations between posts and relevant collections such as tags, as well as the specific collections required by ActivityPub. The publication endpoint forwards text posts to Twitter, if necessary truncating them and adding a link back to the original, which helps with reach of content (since I don't yet have a subscription mechanism implemented). The publication endpoint also scans the content and certain attributes of incoming data for URLs, and behaves as an LDN and Webmention Sender to deliver notifications to others, if possible.
 suggests that the first feature of a PWO should be to allow individuals to consolidate data collected by third parties into one repository under their control. Rather than attempting to aggregate - or even find - all data about myself spread across the Web, I chose a handful of services which have particular value to me, that I have been using to actively log particular things. I exported data from Lastfm (over ten years of music listening history), Twitter (7 years of short notes), Runkeeper (1 year of runs, walks and hikes with GPS traces), and Github and Bitbucket (5 years of code commit history), and Firefox bookmarks (2 years). I also exported data from 750words.com (almost 7 years of intermittent use) but did not import this into my store due to private content and no reliable access control built in.
On top of these data dumps I created the following templates:
|Type of data||Attributes displayed *|
|* All posts contain tags and a published date, and may contain a last modified date.|
|Articles (blog posts)||name, content|
|Short notes (like Tweets)||content|
|Meals logged||description, restaurant if applicable|
|Travel plans||start and end location (map), date and time, means of transport, cost|
|Checkins to specific places||location (map)|
|Checkins to categories of place, aka 'vague' checkins (eg. 'home', 'office')||location, duration of time there, associated colour|
|Likes||URL of thing liked|
|Bookmarks||URL of bookmark, name of bookmark, optional comment or quote|
|Reposts (aka shares aka retweets aka reblogs)||URL of post, optional comment|
|Acquisitions (purchases and free stuff)||description, source, cost, photo|
|Additions to photo albums||photos and URL of album|
|Events and RSVPs||location, date and time, name, description, event website|
|Subscriptons / follows||URL of profile followed|
|Sleep times||start and end date and time, optional comment|
Templates were created not all at once, but as I decided to start tracking something new and wanted to visualise it. Templates were continually modified and improved over the course of the year.
At the time of writing, I display posts in three different formats on my homepage (figure 2): a feed of the most recent eight article and note posts displayed in full; a list of the most recent of each type of post, displayed as a sentence (eg. "the last thing I ate was toast with peanut butter, 25 minutes ago"); and the last 1600 posts of all kinds, visualised as a string of small coloured boxes with icons. The colours represent where I was at the time of making the post (according to the most recent prior 'vague' checkin) and the icons indicate the type of post. Clicking on any of these boxes takes you to the post itself. In addition, the background colour of the homepage changes according to where I am at the present time. I also show my top 128 tags, and the number of posts for each.
Another type of output is a /summary page, which aggregates data between any two dates, defaulting to the past seven days. This is useful for producing a year- and week-in-review, and includes total amount of money spent, top foods eaten, number of words written, and various averages.
One year of using a Personal Web Observatory
All data is actively recorded; that is, I enter all of the data and no posts are triggered by some other process or sensor. Unfortunately, for the sources of data exported from other services mentioned previously, I did not (yet) implement a connection to their various APIs to post subsequent data directly to my site.
As I decided to log a new kind of data, I either created or sought out a suitable client. As an intermediary measure (eg. while a client was in development), I could insert data into the quadstore directly using my SPARQL endpoint, which turns out to be a pretty useful bootstrapping measure. Clients I developed myself are simple web forms, which post AS2 data as JSON-LD to my publication endpoint.
I typically logged data at the time of its occurrence if possible. If I didn't have an internet connection (a frequent occurrence whilst traveling) I used a local 'timestamp' Android app to record the time and note crucial details, then back-dated posts at the earliest opportunity.
Practical uses for small data
 proposes that "small data analytics, while sparse, could be made statistically viable when gathered longitudinally over time." Whilst I haven't done any complex statistical analysis on my data, certainly aggregated results, counts, and some calculations of means have been personally insightful. To take some trivial examples: that I drank on average 0.8 cups of coffee per day in 2016 was lower than I expected; that I spent an average of $28.71 per day was higher than I expected; and that I spent 1 month, 15 days, 6 hours, 41 minutes, and 15 seconds travelling between places is just kind of interesting (and something I particularly wanted to find out when I started recording 'vague' checkins). I don't expect these statistics to be remotely captivating for anyone else; such is the value of personal "small data."
Such aggregations were able to be put to more focused uses. Logging all of my purchases did not raise my internal awareness of how much I was spending on a weekly or monthly basis, but when I realised I wasn't saving money after about six months it was trival to write a small web app which consumed my data stream, aggregated the total cost by certain categories, and displayed the amount I spent in any given month or week. Adding a setting for a monthly budget goal let the app send me notifications to stop spending when it noticed I was on track to exceed this in the current week. The app is not coupled to my personal site or data, so it can be used by anyone who publishes a stream of their purchases using AS2.
An initial motivation of recording 'vague' checkins was so people could check my site to see whether I was on my way if we were planning to meet, or if I shouldn't be disturbed (eg. if I was in a meeting). Industrious computer science friends created an IRC bot that consumed my /where endpoint and responded when asked
$whereis rhiaro. Other users of the same IRC channel added their own location reporting endpoints for the bot to read, some down to GPS accuracy with a map, shortly thereafter. Other unanticipated uses of data I was recording include launching a travel blog which filtered travel-related posts and photos from my main feed, and a food blog which includes my food logs, food-related photos, and any posts or bookmarks about recipes or restaurant reviews.
If my website is down, I do not exist
Having visible output for each log on my website motivated me to keep logging. I am missing lots of checkins to specific cities because I had trouble with a maps API and didn't complete the template to display them. Similarly I never got around to creating a compelling view of sleep records, so logs for those are few and far between.
I felt an internal pressure to ensure my data stream was complete. If I was offline or out of battery for a length of time, I would keep logs on paper to back-date later. This was due both to wanting to ensure aggregate data was accurate, and fear of 'losing' associated memories. Relatedly, I looked (and in fact still do) through historical logs with surprising frequency, and found memories of events, people and places were triggered by descriptions of meals or photos of purchases that I might otherwise have forgotten. I worry that this is unhealthy, boarding on obsessive behaviour though. At times, particularly before I had a streamlined offline-logging plan in place, or if it was socially inappropriate to be writing or typing logs, I felt anxious that I would neglect to log something.
When my webhost experienced outages, leaving me unable to record data, I both noticed far more quickly and felt a far stronger personal impact than I previously would have when my site was mostly static and for infrequent blogging. I complained to my hosting provider more frequently, and projected a sense of urgency that was probably disproportionate. I was left with a feeling of if my website is down, I do not exist, and I found myself wondering if sysadmins in a data center the other side of the world could possibly know or care about the anxiety they were causing me.
Other, predicted, psychological impact was that publicly logging photos of all of my purchases made me more conscious about what I bought. Even though I didn't actually think anyone looked at my feed, I found being aware that someone could see it helped me to, for example, resist buying junk food at the supermarket.
In this section I expand on some of the topics raised by the results of my creation and use of a PWO, and in particular the implications for Web Observatories, and Web Science as a whole. I think these results highlight many open questions and future research challenges.
PWOs are at the intersection of Web Observatories and Personal Data Stores. Research on the former is concerned with passively collecting and analysing how individuals and communities already use the Web, in order to learn about society. Research on the latter is concerned with improving and often actively influencing the lives of individuals. When we think about integrating individual data stores into an architecture for mass observation, we need to find a way to account for the conflicts that arise.
People are likely to be discouraged from using a PWO if it comes with a high burden of maintenance or cost of running . I was able to minimise the impact of this on myself by integrating it into a system I was already maintaining (my personal website). Doing this had a significant impact on the technologies I was able to use, which was in some ways restricting, but beneficial in the long term. Shared Web hosting, PHP and MySQL are widespread and well-supported; this demonstrates that a PWO need not be architected around specialist or niche technologies, and need not be difficult or burdensome to set up and use. Certainly lowering the barrier to entry to people who already run their own websites could help to springboard adoption.
Centralised services are frequently bought out, shut down, or change focus or terms of service. Whilst the technical burden of maintaining ones own personal data store may be higher than delegating this to a third-party comes with different, but not insubstantial, risks.
Serious review of common practices and formal Web standards can smooth the path to interoperability between different instances of WOs and PWOs. Though the standards discussed in this article were designed from a decentralised Social Web perspective rather than a Web Observatories one, the overlap is clear. Implicit self-tracking that makes up a part of ordinary social media use can be supported in the move towards decentralisation, and privacy-preserving PWOs may start to exist as a side-effect. In my implementation, I did not address the use case from  of aggregation of data from a crowd to produce net benefit. This is also something that shared use of open standards, in particular for data representation, subscription and notification, could facilitate.
Anticipation of future needs is raised as a challenge by . With regard to my changing motivations and day-to-day requirements, I did not set out with a specific list of things I wanted to record and design the system around that. Instead, I used a flexible schemaless architecture which meant that for any new type of data I decided to log I had a minimum of new engineering to do: build a client (or potentially hook up a sensor) to generate the data; and (optionally) create a template to view it.
Working with open standards for creating, updating and deleting data helped here too, as I had the option to look for pre-existing standards compliant clients to post to my data store. Conversely, others whose data stores use the server portion of the publishing protocols can use my clients with their own storage.
Discussion of Personal Data Store architectures tends to revolve around reducing fragmentation and prioritises gathering together all kinds of data in one place . This results in generalised tools and interfaces, which try to make it possible to do a variety of things in one place. I argue that more effective and appealing applications are specialised: particularly good at doing one thing. Whilst the data store itself is generic, standards for decentralisation permit the decoupling of clients - both for creating and displaying data - which is perhaps the best of both worlds.
Data context and integrity
Web Observatories which collect data from centralised social networking sites may be vulnerable to ingesting misinformation, ranging from subconscious selective disclosure to deliberate acts of protest against privacy infringement [>10]. It is difficult to say whether proliferation of personal WOs would mitigate this, but it becomes increasingly important to find ways to capture contextual information when data is recorded.
If Web Observatories begin to collect data from both decentralised and centralised services, it is natural to want to align the data so it can be combined into the same data set. However the source of the data cannot simply be discarded. The same type of data from different types of sources is not necessarily equivalent or directly comparable. Just as different centralised social media sites (and the communities and subcommunities within) have their own cultural norms and expectations, as well as technical constraints or affordances, individual personal data stores come with their own unique contextual information as well. In the decentralised case, the context for data logs may be more difficult to discern, as well as capture.
To take a concrete example: for researchers to find more meaning in the logs in my PWO they need to look at in the context of all of the systems I interact with. My system forwards text notes and longer articles to Twitter, which in term drives traffic back to my site where all my other kinds of logs can be seen. Researchers studying my data would need to consider how my awareness of my Twitter audience (directly through known followers and extended through their audiences in the case of retweets) impacts all of the content I post. This might be different for someone who shares their PWO content with a different social network, or not at all.
The interface used to log the data can also have an impact. Whilst I use a Web form based user interface to check in to a place, someone else might generate exactly the same data automatically by the GPS on their phone, making less of a conscious effort to record their movements. Researchers studying how people use, for example, Swarm, have the advantage of knowing that the interface used by everyone is consistent as well as being able to directly explore it themselves. I anticipate that PWOs will be far more diverse, personalised to fit into their users' day-to-day lives, and possibly inaccessible to researchers.
Using data from QS activities for scientific research comes under fire for a number of reasons: self-reported data may be unreliable or biased; context is often lost when quantifying qualitative data; and data collection is limited to individuals who are inclined to record details about their lives . Though I am not using the contents of my data logs in this report, the same issues apply to my recall of the effects of logging.
Perhaps most importantly, though the length of the study is significant, my sample size is 1. As such, I make no claims about generalisability or conclusivity of the results. I am documenting these experiences as a starting point, to begin to explore Personal Web Observatories in depth, and to highlight areas for focus in the future; this is similar to , in which the authors conducted their research on MyLifeBits with large amount of a single person's data as well.
I invested a considerable amount of time in building new features, fixing bugs, and making small improvements and adjustments to my PWO. As the only user and only developer, I was first to notice if something wasn't working properly, and unhindered by conflicting interests or opinions when it came to making changes. This has advantages for rapid prototyping of a somewhat novel system, as well as commitment to the ideology of dogfooding: if I don't want to use a system I've built, why should anybody else? It similarly meant that features I lost interest in fell by the wayside, whilst features I used regularly were well-maintained, so I didn't invest time in developing unnecessary functionality as I might have done had I been designing this for others. Whilst use cases and UI improvements were constrained strictly to my personal needs, and discussion of features was limited to a handful of like-minded developers, this approach was nonetheless appropriate for the purposes of this analysis.
This article documents a year of developing and using a PWO. By taking the role of subject, not only developer, I have come to a better understanding of the day-to-day impact of PWO technology on an individual. 'Small data' is of interest to the academic community in terms of understanding how individuals engage with their own self-tracking activities and outputs, as well as to highlight the diversity of contexts in which data is logged and how this can impact analysis of an aggregation.
I believe that Personal Web Observatories are more useful when intimately personalised for the individual user. Small scale, pluggable components may help to enable this, and using open standards to integrate components can help with designing specialised logging clients or data interpretation interfaces. This gives users more choice to pick and choose the tools they use with their personal data store, as well as making it easier to add and remove components as desired.
This does not mark the end of my self-tracking experiment, but the beginning of the next phase. Over the next twelve months, I expect to add to my repository: data about mood and health (specifically headaches); the people I spend time with offline, and amount of time spent chatting online; and to add more detail about exercise, and to re-start tracking sleep. I will continue to self-track publicly, but experiment with different views over my data for others, so that vistors to my site from different contexts (eg. professional) are not overwhelmed with data, nor left with an impression of inappropriate oversharing.
Finally, I want to reiterate that providers of PWOs have a great responsibility to the individuals whose data they host. For people who engage in frequent self-tracking, a disruption in service can provoke a minor existential crisis... so such an arrangement should not be entered into lightly.
- Ballano Barcena, M., Wueest, C., Lau, H.: How safe is your quantified self? Symantec Security Response, 2014
- Van den Bulck, J.: Sleep apps and the Quantified Self: a blessing or a curse?, Journal of Sleep Res. 24, 121-123, 2015
- Calvo, R.A. and Peters, D.: The Irony and Re-interpretation of Our Quantified Self, OZCHI'13, 2013
- Capadisli, S. and Guy, A.: Linked Data Notifications. W3C Candidate Recommendation, 2016
- Gallagher, R.: New York Student Selling His Private Data on Kickstarter, Slate, 2013
- Genestoux, J. and Parecki, A.: WebSub. W3C Working Draft, 2016
- Gimpel, H., Nißen, M. and Görlitz, R.: Quantifying the Quantified Self: A study on the motivation of patients to track their own health, Thirty Fourth International Conference on Information Systems, Milan, 2013
- Halpin, H. and Bria, F.: State of the Art of social networking systems, identity ecosystem and social data stores. Decentralised Citizens ENgagement Technologies project, CAPS, 2014
- Hall, W., Tiropanis, T., Tinati, R., et al: The Southampton University Web Observatory. pages 1-4.
- van Kleek, M., Murray-Rust, D., Guy, A., O'Hara, K. and Shadbolt, N. Computationally Mediated Pro-Social Deception. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). ACM, New York, NY, USA, 552-563. 2016.
- van Kleek, M., Smith, D., Tinati, R. et al: 7 Billion Home Telescopes: Observing Social Machines through Personal Data Stores, WWW 2014 Companion, 2014
- Li, I., Dey, A.K. and Forlizzi, J.: Understanding My Data, Myself: Supporting Self-Reflection with Ubicomp Technologies, UbiComp'11, Beijing, China, 2011
- Moore, P.V, and Robinson, A.: The quantified self: what counts in the neoliberal workplace. New Media and Society 18 (11). pp. 2774-2792, 2016
- Moore, P and Piwek, L.: Unintended Consequences and Dark Sides of Quantifying Selves, Sustainable Societies Network commissioned paper, 2015
- Nafus, D. and Sherman, J.: This One Does Not Go Up to 11: The Quantified Self Movement as an Alternative Big Data Practice, International Journal of Communication 8, 1784-1794, 2014
- Parecki, A.: Micropub. W3C Candidate Recommendation, 2016
- Parecki, A.: Webmention. W3C Recommendation, 2017
- Sanderson, R.: Web Annotation Protocol. W3C Proposed Recommendation, 2017
- Snell, J., and Prodromou, E.: ActivityStreams 2.0 Core. W3C Candidate Recommendation, 2016
- Solon, O.: 'Data soul' of Shawn Buckles sells for £288, Wired, 2014
- Swan, M.: Sensor Mania! The Internet of Things, Wearable Computing, Objective Metrics, and the Quantified Self 2.0, Journal of Sensor and Actuator Networks 1, 217-253, 2012
- Swan, M.: Health 2050: The Realization of Personalized Medicine through Crowdsourcing, the Quantified Self, and the Participatory Biocitizen, Journal of Personalized Medicine 2, 93-118, 2012
- Swan, M.: The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery, Big Data, Jun;1(2):85-99, 2012
- Tallon, J. and Webber, C.: ActivityPub. W3C Candidate Recommendation, 2016
- Tan, D. et al: Save everything: Supporting human memory with a personal digital lifetime store, Personal Information Management, 90-107, 2007
- Tiropanis, T., Hall, W., Shadbolt, N., et al: The Web Science Observatory. IEEE Intelligent Systems, 28(2):100-104, 2013
- Till, C.: Exercise as Labour: Quantified Self and the Transformation of Exercise into Labour, Societies 4, p. 446-462, 2014
- Whitson, J.: Gaming the Quantified Self, Surveillance & Society 11(1/2): 163-176 2013