Communications of the ACM

machine. Topoi 32, 2 (Oct 2013), 1572–8749.

4. Berners-Lee, T. Information Management: A Proposal. Technical Report, CERN (Mar. 1989, May 1990); http:// cds.cern.ch/record/369245/files/dd-89-001.pdf

5. Berners-Lee, T. Weaving the Web. Harper, New York,

2000.

6. Berners-Lee, T., Hendler, J. and Lassila, O. The Semantic Web. Scientific American 284, 5 (May 2001),

34–43.

7. Berners-Lee, T. et al. Creating a science of the Web.

Science 313.5788 (2006), 769–771.

8. Brand, S. The Media Lab: Inventing the Future at MI T.

V. Viking Penguin, 1987.

9. Cunningham, J. Digital Exile: How I Got Banned for Life from AirBnB. https://medium.com/@ jacksoncunningham/digital-exile-how-i-got-banned- for-life-from-airbnb-615434c6eeba

10. Clark, C. The Sleepwalkers: How Europe Went to War in 1914. Penguin, 2013.

11. Day, M. Teaching the Web: Moving Towards Principles for Web Education. Ph.D. dissertation, University of Southampton, 2019.

12. Gao, J., Galley, M., and Lihong, L. Neural Approaches to Conversational AI. (2018); CoRR abs/1809.08267

13. Gillies, J. and Cailliau, R. How the Web Was Born.

Oxford University Press, Oxford, 2000.

14. Halford, S. and Savage, M. Reconceptualising digital inequality. Information, Communication, and Society

13, 9 (July 2010), 937–955.

15. Halford, S. Digital Futures? Sociological challenges and opportunities in the emergent Semantic Web.

Sociology 47, (Jan. 2012), 173–189.

16. Halford, S. et al. Understanding the production and circulation of social media data: Towards methodological principles and praxis. New Media and Society (2017); https://doi. org/10.1177/1461444817748953.

17. Halford, S. and Savage, M. Speaking sociologically with big data: Symphonic social science and the future for big data research. Sociology 51, 6 (June 2017),

1132–1148.

18. Hill, B.M. Almost Wikipedia: Eight early encyclopedia projects and the mechanisms of collective action. In Essays on Volunteer Mobilization in Peer Production.

Ph.D. dissertation, Massachusetts Institute of Technology, 2013. https://mako.cc/academic/hill-almost_wikipedia-DRAFT.pdf.

19. Krizhevsky, A. et al. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 6

(June 2017), 84–90.

20. Mika, P. and Tummarello, G. Web semantics in the clouds. IEEE Intelligent Systems 23, 5 (May 2008),

82–87.

21. Savage, M. and Burrows, R. The coming crisis of empirical sociology. Sociology 41, 5 (May 2008),

885–899.

22. Schelter, S. and Kunegis, J. On the ubiquity of Web tracking: Insights from a billion-page Web crawl. J.

Web Science 4, 4 (Apr. 2018), 53–66.

23. Shadbolt, N.R. Towards a classification framework for social machines. WWW (Companion Volume) 2013,

905–912.

24. Simonite, T. When it comes to gorillas, Google photos remains blind. Wired (Jan 11, 2018); https://www. wired.com/story/when-it-comes-to-gorillas-google-photos-remains-blind/.

25. Stuart-Ulin, C.R. Microsoft’s politically correct chatbot is even worse than its racist one. Quartz (July 31,

2018); https://qz.com/1340990/microsofts-politically- correct-chat-bot-is-even-worse-than-its-racist-one/.

26. Tinati, R. Big data: Methodological challenges and approaches for sociological analysis. Sociology 48, 4

(2014), 663–668.

27. Vrandecic, D. and Krötzsch, M. Wikidata: A free collaborative knowledgebase. Commun. ACM 57, 10

(Oct. 2014), 78–85.

Steffen Staab holds a chair for Web and computer science at the University of Southampton, U.K. and is a professor at the Universität Koblenz-Landau, Germany, heading its Institute for Web Science and Technologies (WeST).

Susan Halford is a professor of sociology at the University of Bristol, U.K.

Dame Wendy Hall is Regius Professor of Computer Science at the University of Southampton, U.K. and is the Executive Director of the Web Science Institute.

Copyright held by authors/owners.

Publication rights licensed to ACM.

into understanding implications of privacy issues may have been limited, one might have acknowledged that the public’s attitude toward privacy protection did not only stem from lack of knowledge, but also from some nuanced degrees of willingness to share personal information. Such an ambiguous situation calls out for a two-way, participatory dialogue. Not content with only researching ‘on’ users, Web Science is committed to ensuring that the full range of voices is heard as we build our understanding of the Web and shape its future. Web Science seeks creative ways to build public understanding of the public about the threats, but also take on board, appreciate, and remark upon the personal values and attitudes of people. For instance, moral machines are one example where this is done now. 3 We are committed to developing participatory methods that allow us to build insight to diverse perspectives and to build dialogues between these. These methods may include: citizen science—where non-experts are included in a variety of research projects, for example, to study local communitiesj or to contribute subjective, possibly diverging, point of views; 1 online methods for deliberation; organizing face-to-face citizens’ assemblies; and the use of AI techniques (for example, for enhancing knowledge and understanding of the Web and extending dialogue). It is a priority for Web Science that we observe these processes in action to inform continuous improvement in public engagement, for the benefit of policy making and, more widely, the engineering of the Web.

A final example concerns how we
observe the observers. Powerful corpo-
rate or governmental actors may deter-
mine the fate of Web users observing
what we do22 and suggesting what we
might do (or not), for instance, which
accommodation to select, which job to
apply to, or which person to befriend.
Therefore, understanding what these
actors do by tracking their activity and
evaluating their algorithms has be-
come an important activity. Research-
ers and NGOs like Algorithmwatchk
pursue these tasks asking for data do-
j https://bit.ly/2SF8O1w
k https://algorithmwatch.org/en/
In the Web we still lack such regula-
tions, but the more that such actors be-
come gatekeepers to our life, the less
we can just rely on corporate slogans
like “Don’t be evil” (originally used in
Google's corporate code of conduct).

Conclusion

The Web has grown from an idea in 1989 to become the largest sociotechnical assemblage in human history in a little under 30 years. It is implicated in the lives, livelihoods, and life chances of over half the world’s population already and connecting many more every day. While Europe embraces the Web and its opportunities for integration—perhaps more than other parts of the world—it discusses its risks of division. Rather than dystopian, and most likely false, predictions, what it needs is a scientific approach to understanding how the Web works and how it affects society. Web Science has been devised as a field to tackle these questions and we have highlighted a few aspects of where and how Web Science should proceed. In particular, computer science must look beyond its pasture and embrace the methodological experience and diversity by a broad set of fields—more than it has done until now. Funding and academic institutions need to welcome and reward such undertaking or it will not succeed.

Acknowledgment. This article benefited immensely from discussions we had with all the other participants at the Dagstuhl seminarl on “ 10 Years of Web Science: Closing The Loop.” In particular, we want to thank Bettina Berendt, Fabian Gandon, Katharina Kinder-Kurlanda, and Eirini Ntoutsi.