Web Science 2019
The next oral exams will take place on:
- 24.9.2019 14:50-16:30
- 8.10.2019 14:00-16:30
- 9.10.2019 14:00-16:30
To register send a mail to Philipp. The exam slots will be assigned according to the first come first served policy.
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 (40 minutes) consists of two parts:
- Detailed questions on the papers presented by the student during the course. The presentation of the papers is compulsory!
- More general questions on other papers of the same topic, and some on other topics that have been presented by students (marked in the liste below). 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?
- Responsible Professor: Prof. Dr. techn. Wolfgang Nejdl
- Assistant: Philipp Kemkes
- Lecture + Tutorial: Tuesdays 15:45 - 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 and that haven't been picked by someone else.
- 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. Your presentation should last approximately 40 minutes.
List of available Topic Papers
List of papers to be chosen and presented, grouped by topic.
1. Stance Classficiation for Fact Checking
- [Presented 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]
- [Presented 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]
- [Presented 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]
- [Presented 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
- [Presented 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]
- [Presented by Wenka Zhou] 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]
- [Presented by Wenka Zhou] 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]
- [Presented 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
- [Presented by Florian Gross] Embedding Search into a Conversational Platform to Support Collaborative Search [PDF]
- Knowledge-Context in Search Systems: Toward Information-Literate Actions [PDF]
- [Presented by Florian Gross] Measuring Learning During Search: Differences in Interactions, Eye-Gaze, and Semantic Similarity to Expert Knowledge [PDF]
- Reading Protocol: Understanding what has been Read in Interactive Information Retrieval Tasks [PDF]
- Difallah, Djellel Eddine, et al. The dynamics of micro-task crowdsourcing: The case of amazon mturk. WWW '15. [PDF]
- [Presented by Chenyu He] Raykar, Vikas C., et al. Learning from crowds. JMLR '10. [PDF]
- Kazai, Gabriella. In search of quality in crowdsourcing for search engine evaluation. ECIR '11. [PDF]
- [Presented 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]
- [Presented by Matsuev Egor] 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]
- [Presented by Matsuev Egor] 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. Social networks
- [Presented by Tong Niu] Avery Ching, Sergey Edunov, Maja Kabiljo, Dionysios Logothetis, and Sambavi Muthukrishnan. One trillion edges: graph processing at Facebook-scale. VLDB 2015 [PDF]
- [Presented by Masih Ghaderi] Jure Leskovec, Eric Horvitz: Planetary-scale views on a large instant-messaging network. WWW 2008: 915-924. [PDF]
- [Presented by Masih Ghaderi] Jure Leskovec, Daniel Huttenlocher, Jon Kleinberg: Signed networks in social media. CHI 2010: 1361-1370. [PDF]
- [Presented by Tong Niu] 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
- 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]
- Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management. [PDF]
- [Presented 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]
- [Presented by Yuchong Zheng] Subhabrata Mukherjee, Stephan Günnemann. GhostLink: Latent Network Inference for Influence-aware Recommendation. 2019. WWW 2019. [PDF]
9. Fake news detection
- [Presented by Cong Wan] Popat, Kashyap, et al. "Where the truth lies: Explaining the credibility of emerging claims on the web and social media." WWW, 2017.
- Horne, Benjamin D., and Sibel Adali. "This just in: fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news." arXiv preprint arXiv:1703.09398 (2017).
- [Presented by Cong Wan] Popat, Kashyap, et al. "Credibility assessment of textual claims on the web." CIKM, 2016.
- Kumar, Srijan, Robert West, and Jure Leskovec. "Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes." WWW 2016.
- Stance Classficiation for Fact Checking (Dr. Pavlos Fafalios) - Slides
- Social networks (Philipp Kemkes) - Slides
- Rehearsal presentation: Accessing Web Archives
- Student presentation on: Accessing Web Archives
- Rehearsal presentation: A New Age of Search Systems
- Student presentation on: A New Age of Search Systems
- Rehearsal presentation: Stance Classficiation for Fact Checking
- Student presentation on: Stance Classficiation for Fact Checking
- Rehearsal presentation: Fairness and Transparency for Big Data Analysis
- Student presentation on: Fairness and Transparency for Big Data Analysis
- Rehearsal presentation: Linguistic Essentials and NLP Applications
- Student presentation on: Fake news detection
- Cong Wan - Slides
- Cong Wan - Slides
- Rehearsal presentation: Crowdsourcing
- Student presentation on: Crowdsourcing
- Chenyu He - Slides
- Chenyu He - Slides
- Rehearsal presentation: Identifying and measuring user-on-user influence in social networks
- Student presentation on: Identifying and measuring user-on-user influence in social networks
- Rehearsal presentation: Social networks
- Student presentation on: Social networks
- 17:15 Exam preperation