10 Jahre Web Science
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by Steffen Staab, Wolfgang Nejdl, Nikolaus Forgo Introduction to the 10th Web Science birthday
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10 Jahre Web Science
- 1. 10 Jahre Web Science Ein Blick auf die nächsten 10 Jahre http://www.webscience.org/webscience10/tv-channel-webscience10/ Steffen Staab Chair of WSTNet WAIS Univ. of Southampton WeST Univ. of Koblenz- Landau Wolfgang Nejdl L3S Leibniz Universität Hannover Nikolaus Forgó L3S Leibniz Universität Hannover
- 3. World Wide Web Work Dating 17% marriages in US due to online dating Traveling Learning Leisure Science Open access papers cited more often 11:7 https://flic.kr/p/F37KoU
- 6. Web Science Network of Laboratories Wendy Hall - CeBIT 2013
- 7. 10 Jahre Web Science Research Initiative Keynotes • Ricardo Baeza-Yatest, Yahoo! • Andrew Tomkins, Google • Daniel Olmedilla, Facebook • Jure Leskovec, Stanford & Pinterest • Daniel Miller, UCL, ERC Grant „Social Network Sites and Social Science” • Helen Margetts, Oxford Internet Institute Panels 10 Years of Web Science Computational Social Science Privacy and Internet Governance 8th ACM Web Science Conference 2016 22.-25. Mai 2016 in Hannover http://websci17.org/ Troy, NY, USA, 26-18 June 2017
- 8. WWSSS – WSTNet Web Science Summer School Koblenz 2016 St. Petersburg 2017 30/11/16 8Thomas Risse Next one: St. Petersburg, July 2017
- 9. Web Science the grand challenge Web Science Observatory Researchers around the world gathering and sharing data and evidence Sharing tools, methods and techniques Web Science Collaboratories Longitudinal studies Wendy Hall - CeBIT 2013
- 10. Spam Attack on Copts Gun running from Sudan Verlieren wir die Vergangenheit des Web?
- 11. ALEXANDRIA (ERC Advanced Grant, 2.5 Mio. Euro) World Wide Web – Digitales Erbe der Gesellschaft Was bleibt vom Web in 100 / 1000 Jahren, wenn es niemand bewahrt? Datensammlung durch Deutsche Nationalbibliothek, British Library, Internet Archive, u.a. Suche und Analyse durch ALEXANDRIA Entwicklung neuer Modelle und Algorithmen, die es ermöglichen, nicht nur auf die Gegenwart, sondern auch auf die Vergangenheit des Web zuzugreifen Semantische und zeitliche Suche nach Rudolph Giuliani 1997 2000 2006 2014 Mayoral Campaign Mayoral Campaign Mayoral Campaign 9/11 Post politics endeavours Senate, Cancer, Allegations NumberofDocuments Mayor
- 12. SoBigData - Social Mining & Big Data Ecosystem Big Data Analytics & Social Mining as a tool to measure, understand and possibly predict human behavior Research infrastructure (RI) for ethic-sensitive scientific discoveries and advanced applications of social data mining to the various dimensions of social life, as recorded by “big data”.
- 13. Integrating key national infrastructures and centers of excellence CNR & Uni Pisa (SoBigData.it) Social Data Big Data Analytics and Social Mining Services Uni Hannover/L3S (Alexandria) German Web Archive (80 TB) Services and expertise on Web Archives Uni Sheffield (GATE Cloud) Natural language processing and text mining FhG IGD & FhG IAIS Information Visualization and Visual Analytics Aalto University Data, services and competences on social network analysis Uni Tartu (E-Gov.data) Estonian e-government and ehealth data ETH Zürich: Search engine for Open Data
- 14. 1st Call SoBigData-funded Transnational Access Forschungsaufenthalte (bis zu 2 Monate) bei SoBigData Partnern zu den Themen: * City of Citizens * Well-being and Economy * Societal Debates * Migration Studies
- 15. Tracking User Behavior About 75% of websites track user behavior across sites. [Zhonghao Yu et al. WWW16]
- 16. Bias in the Data Bias in the Algorithm Bias in the Social Machine WebObservatory
- 18. Observing Bias in Social Networks (Lerman et al 15) Part of US election/Brexit misprediction?
- 19. Check out: http://www.kdnuggets.com/2016/07/big-data-bible-codes-bonferroni.html „Torture the data, and it will confess to anything.“ Ronald Coase, economist, Nobel Prize Laureate. Bonferroni Effect
- 22. Reality Sensing, Mining and Augmentation for Mobile Citizen-eGovernment Dialogue
- 23. Web for Everyone
- 24. Uber, the world‘s largest taxi company, owns no vehicles. Facebook ...most popular media owner, creates no content. Alibaba, the most valuable retailer, has no inventory. Airbnb... largest accommodation provider, owns no real estate. Data Oligopolists
- 25. Uber Whom do you take a ride with? - the right picture – also for online dating ... Facebook Which source do you trust? - rumor checks change the trust .... Alibaba Whom do you trust to buy from? - others‘ ratings Airbnb Whom do you want for a sleepover? Vertrauen
- 26. Das Recht 27
- 27. Vor langer Zeit …
- 29. 1984 won‘t be like 1984
- 31. 1981 (1987) – Volkszählung
- 32. Seither …
- 35. Computer überall
- 38. If the product is for free, you are the product
- 40. (1)
- 41. Digitale Agenda (2010 ff.)
- 42. Diagnose 30% of Europeans have still never used the internet; Europe has only 1% penetration of fibre-based high-speed networks whereas (Japan 12%, South Korea is at 15%) EU spending on ICT research and development stands at only 40% of US levels. Four times as many legal music downloads in the US as in the EU
- 43. (2)
- 44. Europeans have a long tradition of declaring abstract privacy rights in theory that they fail to enforce in practice.
- 45. Neuordnung des europäischen (Datenschutz)rechts
- 46. 47 01/2012
- 47. Zentrale Versprechen One Continent, one Law Internetfit
- 48. Aber
- 49. Diskussion im Parlament Albrecht: 350 Änderungsvorschläge 3.133 Änderungsanträge
- 50. Aber
- 53. Und heute … 54
- 54. https://isc.sans.edu/diary/Port+7547+SOAP+Remote+Code+Execution+Attack+Against+DSL+Modems/21759 55 In particular, Austria is experiencing a strong increase in TR-069 traffic within the last 24 hours.
- 55. Ergebnis
- 56. 04/2016
- 58. Web Science – The next 10 Years Social challenges Discrimination Trust Moral AI Legal challenges regulation of infrastructure for economic competition tracking everywhere Political challenges Misinformation Participation Internet governance Technical challenges Artificial Intelligence Security ...
Editor's Notes
- Gestern fielen hundertausende Internet Anschluesse aus – heute waren mehrere Erfahrungsberichte wie Leute nicht mehr wussten, was sie tun sollten For example, in the US it is now reported that between 15-20% of newly married couples met their spouses on line (cf. http://www.statisticbrain.com/online-dating-statistics/). https://www.timeshighereducation.com/home/open-access-papers-gain-more-traffic-and-citations/2014850.article
- Out of 350K dierent sites visited by 200K users over a 7 day period, 273K sites contained trackers that were sending information that we deemed unsafe. Data elements that are only and always sent by a single user, or a reduced set of users, are considered unsafe with regard to privacy. 50% of news site carry at least 11 different trackers
- - The majority of your friends in facebook have more friends than you do here: a. the majority of your friends are colored b. the majority of your friends are non-colored (same network) Practical example might be media biases
- Big Data presents opportunities for data mining and machine learning previously unimaginable, given the vast size of datasets from which we are able to learn, cluster upon, find associations within, and generally search for insights not before attainable. Mining Big Data is not a plug-and-play, one-size-fit-all, (insert another cliche here) process, however; though there seems to be alarmingly little discussion anymore of their importance in relation to Big Data, statistical thinking, methods, and processes matter. It is possible that the lack of discussion is because most people understand this fundamental truth already, which I find doubtful. Perhaps I simply have not come across relevant such topics of late, and they do, in fact, exist. I also find this doubtful. I fear that oversight or an essential lack of understanding are more likely to blame. Big Data This article is not a blanket criticism of learning from Big Data; instead, it is much more accurately a reminder that time-tested statistical methods are more valid now than ever, in this Era of Big Data. In that regard, this discussion will focus on 2 particular statistical issues to be on the look out for in your own work and in the work of others mining and learning from Big Data. And for the practitioners out there, this is not about abstract statistical theory. This is about practicality. And the highly improbable probabilities that can be improperly gleaned from Big Data. The Bonferroni Principle There is a concept in statistics that goes like this: even in completely random datasets, you can expect particular events of interest to occur, and to occur in increasing numbers as the amount of data grows. These occurrences are nothing more than collections of random features that appear to be instances of interest, but are not. This bears repeating: even amounts of random data lead to what seem to be events of interest, and the number of these seemingly interesting events grows as does the size of the dataset. The Bonferroni Principle1 is a statistical method for accounting for these random events. To employ it, determine the number of expected random events of interest in the dataset, and if the observed number is significantly greater than this number, the chances of any observations providing useful insight are almost nonexistent. The Bonferroni Correction is a technique for helping to avoid such observations. Torture the data, and it will confess to anything. — Ronald Coase, economist, Nobel Prize Laureate. One of the most prominent and easy to understand examples of the Bonferroni Principle is that of the George W. Bush administration's Total Information Awareness data-collection and data mining plan of 20021. The criticism of the plan's effectiveness, and its relationship to the Bonferroni Principal, are as follows. Suppose we are looking for terrorists, from a potential pool made up of a very large number of individuals. Let's say that, in actuality, however, there is an incredibly small number of individuals who are terrorists. Now suppose these potential terrorists are thought to be deliberately visiting particular locations in pairs for meetings, but let's further suppose that these potential terrorists are actually non-terrorists moving about randomly. By using hard numbers for such a scenario and working out the probabilities, Rajaraman & Ullman gives the example of one billion potential "evil-doers," and though the actual number may be something very small (they give the example of 10 pairs), statistical probabilities could put the number of suspected pairs meeting at given locations due to pure randomness at 250,000 (again, in this particular example). Now, this is clearly a problem. In purely practical terms, imagine having to recruit, train, and pay enough police personnel to investigate each of these flagged individuals! If a Big Data mining practitioner had first computed some number which could be proven a reasonable number of expected random events (the Bonferroni Principle in action), the entire investigation would have been immediately recognized as flawed, given the near-absolute certainty that this Bonferroni number would have been less than a quarter of a million, the suspected number of significant events shown above. Knowing when our out-of-the-gate quantitative assumptions are off base is critically useful in the Era of Big Data. The Bonferroni Principle is one example of how Big Data can result in highly unlikely outcomes masquerading as statistically sound.