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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 2019



Lecture start: 09.04.2019, 15:45-17:15, Multimedia-Hörsaal (3703 - 023)
Exercise start:
14.05.2019, 17:15-18:45, Multimedia-Hörsaal (3703 - 023)

Teaching Team:

  • Responsible Professor: Prof. Dr. techn. Wolfgang Nejdl
  • Assistant: Philipp Kemkes


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


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 26.04.2019 send a mail to Philipp Kemkes with the following details:

  • 2 papers of the same topic that you wish to present.
  • Papers will be assigned to students according to the first come first served policy.


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


At the end of April you should make an individual appointment with the mentor of your topic to discuss the selected papers. Thus you must read them until the meeting.

You have to meet the mentor of your topic again one week before your presentation during the exercise slot for a rehearsal talk. Hence your slides must be ready and you should have practised your talk already a few times.

List of available Topic Papers

List of papers to be chosen and presented, grouped by topic.

1. Stance Classficiation for Fact Checking

  • [Selected by Luca Brandt] Wang, Xuezhi, Cong Yu, Simon Baumgartner, and Flip Korn. "Relevant document discovery for fact-checking articles." In Companion of the The Web Conference 2018 on The Web Conference 2018, pp. 525-533. International World Wide Web Conferences Steering Committee, 2018. [PDF]
  • [Selected by Luca Brandt] Hanselowski, Andreas, P. V. S. Avinesh, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, and Iryna Gurevych. "A Retrospective Analysis of the Fake News Challenge Stance-Detection Task." In Proceedings of the 27th International Conference on Computational Linguistics, pp. 1859-1874. 2018. [PDF]
  • [Selected by Sen Han] Hasan, Kazi Saidul, and Vincent Ng. "Stance classification of ideological debates: Data, models, features, and constraints." In Proceedings of the Sixth International Joint Conference on Natural Language Processing, pp. 1348-1356. 2013. [PDF]
  • [Selected by Sen Han] Bar-Haim, Roy, Indrajit Bhattacharya, Francesco Dinuzzo, Amrita Saha, and Noam Slonim. "Stance classification of context-dependent claims." In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, vol. 1, pp. 251-261. 2017. [PDF]

2. Fairness and Transparency for Big Data Analysis

  • [Selected by Gautam Shahi] 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]
  • 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]
  • 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 Gautam Shahi] Aylin Caliskan-Islam, Joanna J. Bryson, Arvind Narayanan. Semantics derived automatically from language corpora necessarily contain human biases. 2016. [PDF]

3. A New Age of Search Systems

  • [Selected by Florian Gross] Embedding Search into a Conversational Platform to Support Collaborative Search [PDF]
  • [Selected by Mojtaba Shabani] Knowledge-Context in Search Systems: Toward Information-Literate Actions [PDF]
  • [Selected by Florian Gross] Measuring Learning During Search: Differences in Interactions, Eye-Gaze, and Semantic Similarity to Expert Knowledge [PDF]
  • [Selected by Mojtaba Shabani] Reading Protocol: Understanding what has been Read in Interactive Information Retrieval Tasks [PDF]

4. Crowdsourcing

  • [Selected by Nidhal Khalfallah] Difallah, Djellel Eddine, et al. The dynamics of micro-task crowdsourcing: The case of amazon mturk. WWW '15. [PDF]
  • [Selected by Chenyu He] Raykar, Vikas C., et al. Learning from crowds. JMLR '10. [PDF]
  • [Selected by Nidhal Khalfallah] Kazai, Gabriella. In search of quality in crowdsourcing for search engine evaluation. ECIR '11. [PDF]
  • [Selected by Chenyu He] 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]
  • 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]
  • Liudmila Ostroumova Prokhorenkova et al. Publication Date Prediction through Reverse Engineering of the Web. WSDM 2016 [PDF]

6. Linguistic Essentials and NLP Applications

  • Unsupervised Learning of Distributional Relation Vectors [PDF]
  • Joint Multilingual Supervision for Cross-lingual Entity Linking [PDF]
  • Structured Alignment Networks for Matching Sentences [PDF]
  • Reasoning about Actions and State Changesby Injecting Commonsense Knowledge [PDF]

7. Web graph and social networks

  • Avery Ching, Sergey Edunov, Maja Kabiljo, Dionysios Logothetis, and Sambavi Muthukrishnan. One trillion edges: graph processing at Facebook-scale. VLDB 2015 [PDF]
  • [Selected by Masih Ghaderi] Jure Leskovec, Eric Horvitz: Planetary-scale views on a large instant-messaging network. WWW 2008: 915-924. [PDF]
  • [Selected by Masih Ghaderi] Jure Leskovec, Daniel Huttenlocher, Jon Kleinberg: Signed networks in social media. CHI 2010: 1361-1370. [PDF]
  • Jérôme Kunegis, Andreas Lommatzsch, Christian Bauckhage: The slashdot zoo: mining a social network with negative edges. WWW 2009: 741-750. [PDF]

8. Identifying and measuring user-on-user influence in social networks

  • [Selected by Siming Gao] Eytan Bakshy, Jake M. Hofman, Winter A. Mason, and Duncan J. Watts. 2011. Everyone's an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining (WSDM '11). [PDF]
  • [Selected by Siming Gao] Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management. [PDF]
  • [Selected by Yuchong Zheng] Stern, S. O., Tuckett, D., Smith, R. E., & Nyman, R. 2018. Measuring the Influencers in the News Media's Narratives. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)(pp. 698-701). [PDF]
  • [Selected by Yuchong Zheng] Subhabrata Mukherjee, Stephan Günnemann. GhostLink: Latent Network Inference for Influence-aware Recommendation. 2019. WWW 2019. [PDF]

Detailed Schedule






  • Crowdsourcing (Markus Rokicki)
  • Accessing Web Archives (Dr. Helge Holzmann)




  • Student presentation on: Accessing Web Archives
  • Exercise: A New Age of Search Systems


  • Student presentation on: A New Age of Search Systems

    • Mojtaba Shabani
    • Florian Gross

  • Exercise: Stance Classficiation for Fact Checking


  • Student presentation on: Stance Classficiation for Fact Checking

    • Luca Brandt
    • Sen Han

  • Exercise: Fairness and Transparency for Big Data Analysis


  • Student presentation on: Fairness and Transparency for Big Data Analysis

    • Gautam Shahi

  • Exercise: Linguistic Essentials and NLP Applications


  • Student presentation on: Linguistic Essentials and NLP Applications
  • Exercise: Crowdsourcing


  • Student presentation on: Crowdsourcing

    • Nidhal Khalfallah
    • Chenyu He

  • Exercise: Identifying and measuring user-on-user influence in social networks


  • Student presentation on: Identifying and measuring user-on-user influence in social networks

    • Siming Gao
    • Yuchong Zheng

  • Exercise: Web graph and social networks


  • Student presentation on: Web graph and social networks

    • Masih Ghaderi

  • Exam preperation

Oral Exam

The next oral exams dates will be announced here.

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?