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Web Futures: Inclusive, Intelligent, Sustainable


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Almost from its very beginning, the Web has been ambivalent.
It has facilitated freedom for information, but this also included the freedom to spread misinformation. It has faciliated intelligent personalization, but at the cost of intrusion into our private lifes. It has included more people than any other system before, but at the risk of exploiting them.
The Web is full of such ambivalences and the usage of artificial intelligences threatens to further amplify these ambivalences. To further the good and to contain the negative consequences, we need a research agenda studying and engineering the Web, as well as numerous activities by societies at large. In this talk, I will present and discuss a joint effort by an interdisciplinary team of Web Scientists to prepare and pursue such an agenda.

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Web Futures: Inclusive, Intelligent, Sustainable

  1. 1. Steffen Staab 1Institute for Web Science and Technologies · University of Koblenz-Landau, Germany Web and Internet Science Group · ECS · University of Southampton, UK & Web Futures Inclusive, Intelligent, Sustainable This is really about Web Science Steffen Staab @ststaab
  2. 2. Steffen Staab 2 • Why wasn‘t the Semantic Web working? (Staab et al 2019) • Why did the Web work? – Why did some Web encyclopedias flourish and others not? – Why did some social networks grow and others stall? – Why do open source projects grow and why do companies put money into it? • What could threaten the Web? – Privacy (then!) – Monopoly/Oligopoly ( The origins of Web Science in 2004
  3. 3. Steffen Staab 3 What is Web Science? (Staab, 2013)Co-constitution of Technology and Society
  4. 4. Steffen Staab 4 Less than the union, more than the intersection Web Science Butterfly – How do you see the Web? not loved by anyone and still the best depiction
  5. 5. Steffen Staab 5 Manifesto (Berendt et al) in preparation with most significant contributions by • Bettina Berendt, Fabien Gandon, Susan Halford, Katharina Kinder-Kurlanda, Eirini Ntoutsi Dagstuhl Seminar: Web Science 10 years – closing the Loop Dagstuhl picture here
  6. 6. Steffen Staab 6 What is Web Science? (Staab, 2013)Co-constitution of Technology and Society
  7. 7. Steffen Staab 7 Social determinism Society defines technology and its effects. Example: EU politics defines what technology „is able to do“ – upload filters in copyright law private? commercial? Tower in Paris or Las Vegas? Permanent nightly lightning vs light show? France? Germany? USA? Science and Technology Studies: Co-constitution Von Benh LIEU SONG - own work, CC BY-SA 3.0,
  8. 8. Steffen Staab 8 Technological determinism Technology alone determines usage Example about privacy intrusion by technology: "If you have something that you don't want anyone to know, maybe you shouldn't be doing it in the first place.“ – ERIC SCHMIDT: Science and Technology Studies: Co-constitution By Hecker / MSC - security-conference-2018/image/eric-schmidt/filter/image/, CC BY 3.0 de,
  9. 9. Steffen Staab 10 Co-constitution Technology and society determine each other Example: • Technology: SMS • Usage:   hdgdl lol rofl • Derived technologies: Twitter, Whatsapp • Usage: # • Derived technologies: Instagram, Snapchat • Usage: Politics, Influencing as business model Science and Technology Studies: Co-constitution
  10. 10. Steffen Staab 11 The Web as a boundary object • „data“ • „algorithms“ • „artificial intelligence“ • „gold standard“ • „communities“ • „survey“ • „data protection“ By Illustrator unknown - From Charles Maurice Stebbins & Mary H. Coolidge, Golden Treasury Readers: Primer, American Book Co. (New York), p. 89., Public Domain,
  11. 11. Steffen Staab 12 Ambivalences of the Web Utopia vs Dystopia
  12. 12. Steffen Staab 13 • Germany: „Freie Fahrt für freie Bürger“ – no speed limit • USA: „The right to carry weapons“ – self-defense • UK Brexit: „Take back control“ – no delegation of power Society not good dealing with ambivalences Technical solutions exist to improve social welfare – but they are of no use, if people disagree
  13. 13. Steffen Staab 14 Information Freedom Information Quality Ambivalences of the Web Personalization Privacy Influence by the Masses Manipulation of the Masses Inclusive and Fair Exploitative
  14. 14. Steffen Staab 15 Latest example • „CNN Refuses to Run Trump Campaign's Biden Ad“ because of factual incorrectness • Facebook accepts same video as part of its ad business • Fact checking as a way out? – IFCN • >100 fact checking organizations • Facebook partners • (Brandtzaeg and Følstad 2017) Ambivalence: Information Freedom vs Information Quality Fact checking (Mis-)Trusting the Fact Checkers
  15. 15. Steffen Staab 16 RumourEval 2019 (Gorrell et al 2019) Veracity: False Stance: Comment Stance: Comment Stance: Deny Stance: Deny Subtask A: • classify each comment as support, deny, query, or comment towards the statement in the post Subtask B: • classify the statement expressed in the thread’s source post as true, false, or unverified Labelled collection of comment threads from Twitter and Reddit
  16. 16. Steffen Staab 17 CLEARumor (Baris et al 2019) • Pre-trained ELMo embeddings • CNN-based model with auxiliary input features • Second place in Subtask B (Veracity) Idea: User‘s stance towards a claim could be a clue for debunking the claim in early stage.
  17. 17. Steffen Staab 18 • Data sets: size, balance • Real-time • Astroturfing campaigns • No journalistic knowledge on fact checking: just canned knowledge ⇒ Community oriented approach with FactCheckingNI Failures of current misinfo detection
  18. 18. Steffen Staab 19 • No obvious solution • No stopping condition – Campaigns: Trolls / electronic armies / astroturfing • Involving many stakeholders with diverging interest • Needs technology, behavior, education, policy, media/journalism Misinformation is a Wicked Web Science Problem An AI filter can be a building stone, but not the eventual solution to deal with ambivalence of filtering Mor Namaan 2019
  19. 19. Steffen Staab 20
  20. 20. Steffen Staab 21 Influence by the Masses: Online Participation Democracy! Everyone may participate!! CD
  21. 21. Steffen Staab 22 Future of Online Participation? Deal or No Deal?
  22. 22. Steffen Staab 23 Exclusion: • Not everyone has internet • (functional) analphabets Self censorship: • legasthenic? • Not reveal themselves Gender discrimination • Offline: gender quota for presenters at Green Party • Online: females trolled more than males Online Participation: Always great?
  23. 23. Steffen Staab 24 Exclusion: • Not everyone has internet • (functional) analphabets Self censorship: • legasthenic? • Not reveal themselves Gender discrimination • Offline: gender quote for presenters at Green Party • Online: females trolled more than males Online Participation: Always great? Renate Künast, Member of Bundestag, Green Party Olaf Kosinsky - own work, CC BY-SA 3.0 de, Attacked with speech so abusive that I cannot repeat it here. Considered o.k. by court, because she is politician
  24. 24. Steffen Staab 25 Exclusion: • Not everyone has internet • (functional) analphabets Self censorship: • legasthenic? • Not reveal themselves Gender discrimination • Offline: gender quote for presenters at Green Party • Online: females trolled more than males Communication limited to elites? Online Participation: Always great? Influence by the Masses Manipulation of the Masses
  25. 25. Steffen Staab 26 Typology of Online Participation Main target group in our project Not all participation is positive
  26. 26. Steffen Staab 27 • Mobilization Party members become more active through online • Replacement Members replace traditional activity through online • Reinforcement Active members reinforce their participation relatively • No Use No change of behavior Online participation model of Thuermer Gefion Thuermer PhD thesis „The effect of the introduction of online participation processes in the Green Party Germany“
  27. 27. Steffen Staab 28 Survey Measurements (in the social science sense) Activity Tool Use Activity Increase Mobilisation - + + Reinforcement + + + Replacement x + - Non-Use x - x Activity relates to institutional activity (various factors) Tool Use - use of various tools; Activity Increase compares actual against intended change of behavior - indicates either no difference or negative correlation; + indicates a difference or positive correlation; x indicates either (as it does not make a difference to the model). (Thuermer 2018)
  28. 28. Steffen Staab 29 Several waves of data collections (interviews & surveys) (Thuermer 2019)
  29. 29. Steffen Staab 30 “I think the surveys were very accessible, so that everyone, including old people, could participate. It stated clearly to ‚now click this link in the next line.’” “Online participation is one way to improve inclusion. For example, we have older people who are less mobile who could participate through this route.” „Our assemblies always happen at children’s bedtime. It sounds trivial, but this highly specifically excludes parents. For polls, discussions and so on, online participation would be really great.” “[Online Participation] is really good because it allows easy access independent of people’s life and circumstances. For example, shift workers who work at night and can then go online and participate when they have the time.” (Thuermer et al. WebSci 2018)
  30. 30. Steffen Staab 31 Heterogenity of effects (Thuermer 2019) variable value tools direction of effect
  31. 31. Steffen Staab 32 • Online participation generates data • Data impact reality: – Ex.: Online EU survey on whether to keep time change • Which data do you want to have/? – Democracy requires representativity • Representative for party members (more college degrees) • Representative for voters (more elderly) • Representative for the people für (who represents kids?) Online participation makes data become an actor in the sense of actor-network theory My optimistic point of view: Many questions about „Correctness of data“ are put forward, because they can be put forward now! Research task: De-biasing as task of Computer Science, ... and society
  32. 32. Steffen Staab 33 Another actor...
  33. 33. Steffen Staab 34 Data: - Linux Kernel Mailing List 2014 - LK Github Repository 2014-2015 Questions: • Which shares have hobbyists vs firm-sponsored developers? • Is there a trend over the years? • Are firm-sponsored developers more effective? What motivates companies to Open Source? Inclusive and Fair Exploitative (Homscheid et al. 2016)
  34. 34. Steffen Staab 35
  35. 35. Steffen Staab 36 Sponsorship an important moderator • Structural capital: degree (in+out) • Relational capital: tie strength / weighted degree • Cognitive: involvement on different mailing lists (Homscheid 2019)
  36. 36. Steffen Staab 37 Sustainability A Network Science perspective on
  37. 37. Steffen Staab 38 Preferential Attachment over Time Prediction by Bianconi-Barabasi Impossible to catch up? (Sun et al 18)
  38. 38. Steffen Staab 39 ...and in reality: Time invariance system time = network size ≠ real time Well possible having a popular paper / Web site, even if you come later
  39. 39. Steffen Staab 40 • Preferential attachment with decay of relevance 𝑅(𝑡 − 𝑡𝑖) – Fewer citations as the paper gets older – Π𝑖 ~ 𝜂𝑖 𝑘𝑖 𝑅(𝑡 − 𝑡𝑖) • Exponential growth of the network size – Holds empirically for publications! – 𝑠 = 𝑒 𝛼𝑡 • Power-law degree growth of nodes with regard to their ages. – Holds empirically (see previous slide) Core idea age fitnessdegree
  40. 40. Steffen Staab 41 fitness Network growth: exponential or constant Growth 𝒈~𝒆 𝝈𝒕 𝜎 ∈ ℝ When (exponential) growth can not be uphold, some assumptions will have to give in Academic reward Economics Etc.
  41. 41. Steffen Staab 42 Experiment
  42. 42. Steffen Staab 43 India: Net neutrality extended • No (by Facebook) • Online-Shops must not sell their own stock Inclusive and fair to achieve sustainability • Also for the latecomers Sustainability
  43. 43. Steffen Staab 44 What is the Future of the Web?
  44. 44. Steffen Staab 45 • Documents • Data • Services • Things • People • Assemblies of items Toward a Web of Everything (Berendt et al. 2019)
  45. 45. Steffen Staab 46 Somewhat intelligent, maybe • biased • unreliable • unavailable • unsafe • unsecure • unaccountable Intelligences hosted on the Web (Berendt et al. 2019) Failure of the socio-technical system
  46. 46. Steffen Staab 47 Hybrid societies of intelligence
  47. 47. Steffen Staab 48 • Web → AI – ImageNet • AI → Web – Chatbots – Virtual Assistants • Learning from Web dialogues • Safeguarding Web and Artificial Intelligence
  48. 48. Steffen Staab 49 Conclusion Longer
  49. 49. Steffen Staab 50 • Digital observation of everyday routines • How data is created depends on – technology, communities, ownership, markets, regulations, rights, ... • Just to describe these processes and what they mean, we need – Computer scientists, lawyers, political scientists, sociologists, ... Sociotechnical challenges: Datafication Computer science tends to underestimate the descriptive dimension (Staab, Halford, Hall 2019)
  50. 50. Steffen Staab 51 • 3 billion people without internet • no normative propositions „The Web is `good‘ for everyone“ • Digital divide: Internet benefitting the privileged – rarely than the impoverished? • Digital literacy: Understanding the Web as a system – Everyone must know how a meme grows to avoid misinformation! – Not the case now! Soziotechnical challenges: Digital Divide (Staab, Halford, Hall 2019)
  51. 51. Steffen Staab 52 • Google, Facebook, Amazon,... • 245,000 US$ to train XLNet • Who decides the bias? – Representation of women on Wikipedia • Edit-a-thons • GNU for AI? – ( Who owns AI that steers the Web?
  52. 52. Steffen Staab 53 • Where are we now? What works well? And doesn‘t? For whom, when and why? • What are the possible futures for specific AI applications? • What would have to happen to get us there? • Diversifying the vision of the common good – „good for someone else“ • Empowering participation in the future • Bringing people back in - not as users or consumers, or in terms of impact but as part of the world we are building Susan Halford (Exauguration speech 2019) Democratizing futures: An utopy
  53. 53. Steffen Staab 54 Susan Halford (Exauguration speech 2019)
  54. 54. Steffen Staab 55 Ipek Baris, Lukas Schmelzeisen, Steffen Staab (2019). CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors. Bettina Berendt, Fabien Gandon, Susan Halford, Jim Hendler, Katharina Kinder-Kurlanda, Eirini Ntoutsi, Steffen Staab. 10 Years of Web Science — Dagstuhl Manifesto, Manifesto from Dagstuhl Perspectives Workshop 18262, in preparation 2019. Petter Bae Brandtzaeg and Asbjørn Følstad (2017). Trust and distrust in online fact-checking services. Communications of the ACM, 60(9), 65-71. doi: 10.1145/3122803. Gorrell et al (2019). RumourEval 2019: Determining Rumour Veracity and Support for Rumours. Dirk Homscheid, Mario Schaarschmidt, Steffen Staab: Firm-Sponsored Developers in Open Source Software Projects: a Social Capital Perspective. ECIS 2016: Research-in-Progress Paper 12 Dirk Homscheid. Firm-Sponsored Developers in Open Source Software Projects: A Social Capital Perspective. PhD thesis submitted at Universität Koblenz-Landau, 2019. Steffen Staab, Susan Halford, Wendy Hall: Web science in Europe: beyond boundaries. Commun. ACM 62(4): 74 (2019) Jun Sun, Steffen Staab, Fariba Karimi, Decay of Relevance in Exponentially Growing Networks. In: Proc. of ACM WebSci ‘18. ACM. Jun Sun, Matus Medoy, Steffen Staab. Time-invariant degree growth in preferential attachment network models. Submitted 2019. Gefion Thuermer „The effect of the introduction of online participation processes in the Green Party Germany“, PhD thesis, University of Southampton 2019. Thuermer, G., Roth, S., O'Hara, K., & Staab, S. Everybody thinks online participation is great – for somebody else: A qualitative and quantitative analysis of perceptions and expectations of online participation in the Green Party Germany. In Proc. of ACM WebSci 2018, pp. 287-296. References
  55. 55. Steffen Staab 56Institute for Web Science and Technologies · University of Koblenz-Landau, Germany Web and Internet Science Group · ECS · University of Southampton, UK & Thanks to my many, many collaborators! Ipek Baris, Lukas Schmelzeisen, Jun Sun, Dirk Homscheid, Mario Schaarschmidt (U Koblenz) Gefion Thuermer (U of Southampton) and all the others!