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Logo: Institut für Verteilte Systeme - Fachgebiet Wissensbasierte Systeme (KBS)
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Logo: Institut für Verteilte Systeme - Fachgebiet Wissensbasierte Systeme (KBS)
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Web Science 2018



Course start: 10.04.2018, 15:30-17:00, Multimedia-Hörsaal (3703 - 023)

Teaching Team:

  • Responsible Professor: Prof. Dr. techn. Wolfgang Nejdl
  • Assistant: Philipp Kemkes
  • NOTE: Please put "[WebScienceCourse]" into the subject line when writing an email


  • Lecture + Tutorial: Tuesdays 15:30 - 17:45
  • Room: Multimedia-Hörsaal (3703 - 023), Appelstraße 4, 30167 Hannover

Oral Exam

The next oral exams will take place on 19. and 20. March 2019, 16:00 - 18:00.

Te register send a mail to Philipp.

Location: KBS, Appelstr. 4, 2nd floor, Prof. Nejdl's office (please ring the bell to enter the floor and wait in the waiting room in the middle of the hall). 

The oral exam consists of two parts:

  1. Detailed questions on the papers presented by the student during the course. The presentation of the papers is compulsory!
  2. More general questions on other papers of the same topicand some on other topics. As a guideline you should be able to answer the following questions:

    • What is the problem addressed in the paper?
    • How does the solution look like?
    • How is it evaluated?

Topics for Student Paper Presentation

Below are the topics of Web Science that will be addressed in the course. Each student will have to pick two papers of the same topic that she/he will present to the other students in the second part of the course. 


Until 24.04.2018 send a mail to Philipp with the following details:

  • At least 2 papers of the same topic that you wish to present.
  • Any time period (if exists) during the semester lecture period in which you absolutely cannot present.


We will try to take the following criteria into account when assigning papers to students:

  • Papers will be assigned to students according to the first come first served policy.
  • The exact presentation date will be fixed as soon as enough topics have been assigned.
  • Presentations about the same topic should take place on the same day.
  • A similar number of papers per topic should be presented (as far as possible).
  • Each topic should have at least one paper presented.



  • Here we collected hints helping you to prepare a good presentation.
  • You are highly encouraged to use the provided slide template for your presentation: powerpoint latex.

List of available Topic Papers

Below are the papers to be chosen and presented, grouped by topic.

1. Fake news detection

2. Fairness and Transparency for Big Data Analysis

  • [Selected by Kabir Firoz] Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, Joseph A. Konstan. Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity. WWW '14. [PDF]
  • [Selected by Md Musa] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi. Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment. WWW '17. [PDF]
  • [Selected by Md Musa] Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, Adam Kalai. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. NIPS '16. [PDF]
  • [Selected by Kabir Firoz] Aylin Caliskan-Islam, Joanna J. Bryson, Arvind Narayanan. Semantics derived automatically from language corpora necessarily contain human biases. 2016. [PDF]

3. Introduction to DeepLearning

    4. Crowdsourcing

    • [Selected by Alexandra Risch] Difallah, Djellel Eddine, et al. The dynamics of micro-task crowdsourcing: The case of amazon mturk. WWW '15. [PDF]
    • Raykar, Vikas C., et al. Learning from crowds. JMLR '10. [PDF]
    • [Selected by Alexandra Risch] Kazai, Gabriella. In search of quality in crowdsourcing for search engine evaluation. ECIR '11. [PDF]
    • Bernstein, Michael S., et al. Soylent: a word processor with a crowd inside. UIST '10. [PDF]

    5. Accessing Web Archives

    • Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. Graphs over time: densification laws, shrinking diameters and possible explanations. KDD 2005 [PDF]
    • [Selected by Max Kaulmann] Marijn Koolen and Jaap Kamps. The Importance of Anchor Text for Ad Hoc Search Revisited. SIGIR 2010 [PDF]
    • Avishek Anand, Srikanta Bedathur, Klaus Berberich, and Ralf Schenkel. Index Maintenance for Time-Travel Text Search. SIGIR 2012 [PDF]
    • [Selected by Max Kaulmann] Liudmila Ostroumova Prokhorenkova et al. Publication Date Prediction through Reverse Engineering of the Web. WSDM 2016 [PDF]

    6. Semantic Text Mining

    • [Selected by Nils Nommensen] Vlad Niculae, Joonsuk Park, Claire Cardie. Argument Mining with Structured SVMs and RNNs. ACL '17. [PDF]
    • [Selected by Nils Nommensen] David Tsurel , Dan Pelleg, Ido Guy, Dafna Shahaf. Fun Facts: Automatic Trivia Fact Extraction from Wikipedia. WSDM '17. [PDF]
    • [Selected by Max Idahl] Knowledge Base Unification via Sense Embeddings and Disambiguation  [PDF]
    • [Selected by Max Idahl] Knowledge Graph and Text Jointly Embedding [PDF]

    7. Quality Control Mechanisms in Crowdsourcing Systems

    8. Compression

    Detailed Schedule

    10.04.2018 - Lecture

    • Fairness and Transparency for Big Data Analysis (Prof. Dr. Wolfgang Nejdl) - Slides

    17.04.2018 - Lecture

    • Quality Control Mechanisms in Crowdsourcing Systems (Ujwal Gadiraju) - Slides
    • Semantic Text Mining (Besnik Fetahu) - Slides

    24.04.2018 - Lecture

    • Introduction to DeepLearning (Asmelash Teka) - Slides

    - Lecture

    • Accessing Web Archives (Helge Holzmann) - Slides
    • Fake news detection (Vinicius Woloszyn) - Slides


    15.05.2018 - Lecture

    • Compression (Philipp Kemkes) - Slides


    29.05.2018 - Lecture

    • Crowdsourcing (Markus Rokicki) - Slides


    Student presentations


    05.06.2018 - Fairness and Transparency for Big Data Analysis and Accessing Web Archives

    12.06.2018 - Fake news detection

    - Introduction to DeepLearning

    - DeepLearning and Fairness and Transparency for Big Data Analysis

    • Kabir Firoz (Fairness and Transparency) - Slides
    • Xue Yuan (DeepLearning) - Slides

    03.07.2018 - Compression and Crowdsourcing

    • Alexander Treptau - Slides
    • Alexandra Risch (Crowdsourcing) - Slides

    10.07.2018 - Quality Control Mechanisms in Crowdsourcing Systems

    - Semantic Text Mining