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In this paper, we propose a set of concepts underlying the process and requirements of observation: that is, the process of employing web observatories for research. We refer to observation as a new concept, distinct from search, which we believe is worthy of study in its own right and note that the process of observation moves the focus of information retrieval away from universal coverage and towards improved quality of results and thus has many potential facets not necessarily present in traditional search.

In this doctoral proposal, we describe an approach to identify recurring, collective behavioral mechanisms in the collaborative interactions of Wikipedia editors that have the potential to undermine the ideals of quality, neutrality and completeness of article content. We outline how we plan to parametrize these patterns in order to understand their emergence and evolution and measure their effective impact on content production in Wikipedia. On top of these results we intend to build end-user tools to increase the transparency of the evolution of articles and equip editors with more elaborated quality monitors. We also sketch out our evaluation plans and report on already accomplished tasks.

Planners and social psychologists have suggested that the recognizability of the urban environment is linked to people's socio-economic well-being. We build a web game that puts the recognizability of London's streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. The game picks up random locations from Google Street View and tests users to see if they can judge the location in terms of closest subway station, borough, or region. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgram's). We collect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of London based on their responses. We find that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway passengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life off- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare. This line of work is at the crossroad of two emerging themes in computing research - a crossroad where "web science" meets the "smart city" agenda.

Insights from Web Science and Big Data Analysis have led many researchers to the conclusion that the Web not only represents an almost unlimited data store but also a remarkable multi-disciplinary laboratory environment. A new challenge is how to best leverage the potential of this experimental space. What are the procedures for defining, implementing and evaluating "Web-scale" experiments? What are acceptable measures of robustness and repeatability? What are the opportunities for experimental collaboration? What disciplines are likely to benefit from this new research model? The Web Laboratory model provides an exciting new and fertile model for future research.

As a scoping exercise in the design of our Social Machines Observatory we consider the observation of Social Machines "in the wild", as illustrated through two scenarios. More than identifying and classifying individual machines, we argue that we need to study interactions between machines and observe them throughout their lifecycle. We suggest that purpose may be a key notion to help identify individual Social Machines in composed systems, and that mixed observation methods will be required. This exercise provides a basis for later work on how we instrument and observe the ecosystem.

This paper presents an analysis of tweets collected over six days before, during and after the landing of the Mars Science Laboratory, known as Curiosity, in the Gale Crater on the 6th of August 2012. A sociological application of web science is demonstrated by use of parallel coordinate visualization as part of a mixed methods study. The results show strong, predominantly positive, international interest in the event. Scientific details dominated the stream, but, following the successful landing, other themes emerged such as fun, and national pride.

We present the Koblenz Network Collection (KONECT), a project to collect network datasets in the areas of web science, network science and related areas, as well as provide tools for their analysis. In the cited areas, a surprisingly large number of very heterogeneous data can be modeled as networks and consequently, a unified representation of networks can be used to gain insight into many kinds of problems. Due to the emergence of the World Wide Web in the last decades many such datasets are now openly available. The KONECT project thus has the goal of collecting many diverse network datasets from the Web, and providing a way for their systematic study. The main parts of KONECT are (1) a collection of over 160 network datasets, consisting of directed, undirected, unipartite, bipartite, weighted, unweighted, signed and temporal networks collected from the Web, (2) a Matlab toolbox for network analysis and (3) a website giving a compact overview the various computed statistics and plots. In this paper, we describe KONECT's taxonomy of networks datasets, give an overview of the datasets included, review the supported statistics and plots, and briefly discuss KONECT's role in the area of web science and network science.