Introduction
Different types of social buttons have diffused across blogs, news websites, social media platforms and other types of websites. These buttons allow users to share, bookmark or recommend the webpage or blogpost across different platforms such as Facebook, Twitter, Digg, Reddit, Delicious, Stumbleupon, etc. The buttons often show a counter of how many times the page/post has been shared or recommended: x likes, x shares, x tweets. These likes, shares and tweets may be approached from a new media studies perspective as new types of hyperlinks and from an economic sociology perspective open up questions about the increasing interrelation between the social, technicity and value online. Within new media studies the hyperlink has previously been studied as a form of currency of the web establishing an economy of links (Walker 2002 & Jarvis 2009) and as an indicator of a discursive relationship (Rogers 2002).

The economy of links describes the link as a currency of the informational web in which search engines use hyperlinks to look at the relations between websites in order to establish a ranking. The term informational web is often used to describe the world wide web as a publication medium for publishing content (Ross 2009) and is characterized by the linking of information (Wesh 2007).2 In this web search engines act as main actors to be able to navigate through all the information by recommending pages based on authority measures.

According to social networking site Facebook “the informational Web is being eclipsed by the social Web” (Claburn 2009). In contrast to the informational web where search engines focus on links between websites, the social web “is a set of relationships that link together people over the Web” and “the applications and innovations that can be built on top of these relationships” (Halpin & Tuffield 2010) and is characterized by the linking of people (Wesh 2007).3 Within the social web search engines and social media platforms look at the connections between people and their relations to other web users or web objects. Facebook popularized the term Social Graph “to describe how Facebook maps out people’s connections” (Zuckerberg 2009). As Facebook considers its services inherently social and its plugins and buttons are called ‘Social plugins’ we summarize the activities they generate as so-called “social activities.”

Where Google can be seen as the main agent of the informational web and the regulator of the link economy, Facebook is currently seen as the emerging agent of the social web. Especially the company’s recent efforts to make the entire web experience more social mark the advent of a different type of economy which is based on social indexing of the web: the Like economy. Key elements of this economy are the social buttons, the activities they generate and the way they connect Facebook with the entire web.

According to Facebook, liking and sharing are valuable for users and the company because they enable to experience the web more socially. A similar connection between the social and economic value has been developed by Adam Arvidsson (2009) with his idea of an ethical economy in which value creation is based on collective negotiation and in which economic value creation is related to the quality of social bonds that are generated. Within this paper we want to question the centrality of social dynamics and social relations as key driver for platform engagement and the Like economy. Through merging a new media with an economic sociology perspective, we will shift attention away from the users and the social to the impact of issues on social activities, as well as their interrelation with technicity and the fabric of the web. Based on an extensive empirical study of button presence and engagement within a sample of 592 URLs, we ask how issues, technicity and the social create a productive assemblage of value creation in an emerging Like economy.

In what follows, this paper aims to address these questions by first looking at the history of different types of web economies over time. How do these ‘new’ social activities central within the social web relate to the hit and link economy of the informational web? What creates engagement and how does this engagement organize the fabric of the web and sociality? And finally, what are the perspectives of a Like economy?

Download full paper as PDF: GerlitzHelmond-HitLinkLikeShare.pdf

We’d be happy to receive any comments and feedback!

Abstract interpretation of the Dutch blogosphere 2001 #1

I am currently working on analyzing the Dutch blogosphere with my colleague Esther Weltevrede with help of colleague Erik Borra from the Digital Methods Initiative. In an early exploratory phase Esther and I started to learn how to use Gephi to visualize our data and networks. In one of my early attempts I created this beautifully abstract interpretation of the Dutch blogosphere. Gephi creates design by research!

Abstract interpretation of the Dutch blogosphere 2001 #2

Actual findings and paper will follow in a few weeks!

Thank you SETUP for a great evening! SETUP also compiled all photos, videos and some tweets of the event in the Storify stream embedded below.

Twitter penetration in the Netherlands

Credits: Michiel Berger (@michielb), Anne Helmond (@silvertje), Marvin de Reuver (@marvindereuver), Esther Weltevrede (@esthr) and Ton Wesseling (@tonwesseling)

This final image has been made with the help from Ton Wesseling who calculated the percentage of Twitter users per city, using data about the number of inhabitants per city from CBS (January 2010).

Looking at the Twitter penetration per city in the top 25 we see that Amsterdam retains its #1 position with an estimate of 3,78% of the population having a Twitter account while some other major cities drop in ranking (Rotterdam from 2 > 6 and Den Haag from 4 > 13).

In this new view we see some cities climbing (eg. Groningen #2 – 2,99% , Hilversum #4 – 1,89%, – Zwolle #5- 1,67%) and others declining (Rotterdam #6- 1,66% , Eindhoven #12- 1,45%, Den Haag #13 – 1,31%). Many of the cities that climb in the ranking are student cities like Groningen, Zwolle, Leiden, Leeuwarden, Delft and Maastricht.

Thank you Ton!

Download a hi-resolution map on our project website for beautifying your office.

Map updated on January 22: Added Apeldoorn (# 21 in absolute ranking and no longer in the top 25 for relative ranking) and fixed an error in the relative ranking row. Map needs more updating concerning bubble size.

Relative Twitter density in the Netherlands

Credits: Michiel Berger (@michielb), Anne Helmond (@silvertje), Marvin de Reuver (@marvindereuver), Esther Weltevrede (@esthr)

This the second map of the Dutch Twitter population according to the Twittergids data. In the first part I visualized the top 25 Twitter cities in the Netherlands. In this follow-up image Esther Weltevrede and I looked into the relative number of Twitter users per 1000 inhabitants for each province. Color density is relative to the highest penetration (province of North Holland).

What is striking is that the province of Groningen has a relatively high number of Twitter users per 1000 inhabitants.

Hi-resolution images available here.