Julian Risch

I am a Ph.D. student at the Information Systems Group and a member of the HPI Research School. My research focuses on topic modeling and comment analysis.

Contact Information

Prof.-Dr.-Helmert-Straße 2-3
D-14482 Potsdam
Room: F-2.08

Phone: +49 331 5509 272

Email: Julian Risch

Open Master's Theses

I provide supervision for an open Master's thesis on Natural Language Processing for Information Retrieval, which is described here. Further, feel free to discuss Master's thesis topics in the area of News Comment Analysis, such as Toxic Comment Classification, with me.

Teaching

Text Mining in Practice
Information Retrieval and Web Search
Recommender Systems
How Do Search Engines Work?
Text Mining
Hate Speech Detection (Master's Project Advisor)
Quality Management for Online News Comments (Master's Thesis Advisor)

Publications

Risch, J., Krestel, R.: Aggression Identification Using Deep Learning and Data Augmentation. Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING 2018). bl. 150--158 (2018).

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@inproceedings{risch2018aggression, abstract = {Social media platforms allow users to share and discuss their opinions online. However, a minority of user posts is aggressive, thereby hinders respectful discussion, and — at an extreme level — is liable to prosecution. The automatic identification of such harmful posts is important, because it can support the costly manual moderation of online discussions. Further, the automation allows unprecedented analyses of discussion datasets that contain millions of posts. This system description paper presents our submission to the First Shared Task on Aggression Identification. We propose to augment the provided dataset to increase the number of labeled comments from 15,000 to 60,000. Thereby, we introduce linguistic variety into the dataset. As a consequence of the larger amount of training data, we are able to train a special deep neural net, which generalizes especially well to unseen data. To further boost the performance, we combine this neural net with three logistic regression classifiers trained on character and word n-grams, and hand-picked syntactic features. This ensemble is more robust than the individual single models. Our team named “Julian” achieves an F1-score of 60% on both English datasets, 63% on the Hindi Facebook dataset, and 38% on the Hindi Twitter dataset.}, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING 2018)}, month = {August}, pages = {150--158}, title = {Aggression Identification Using Deep Learning and Data Augmentation}, year = 2018 }
Risch, J., Krestel, R.: Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom. Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING 2018). bl. 166--176 (2018).

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@inproceedings{risch2018delete, abstract = {Comment sections of online news providers have enabled millions to share and discuss their opinions on news topics. Today, moderators ensure respectful and informative discussions by deleting not only insults, defamation, and hate speech, but also unverifiable facts. This process has to be transparent and comprehensive in order to keep the community engaged. Further, news providers have to make sure to not give the impression of censorship or dissemination of fake news. Yet manual moderation is very expensive and becomes more and more unfeasible with the increasing amount of comments. Hence, we propose a semi-automatic, holistic approach, which includes comment features but also their context, such as information about users and articles. For evaluation, we present experiments on a novel corpus of 3 million news comments annotated by a team of professional moderators.}, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (co-located with COLING 2018)}, month = {August}, pages = {166--176}, title = {Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom}, year = 2018 }
Ambroselli, C., Risch, J., Krestel, R., Loos, A.: Prediction for the Newsroom: Which Articles Will Get the Most Comments? Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2018). bl. 193--199. ACL, New Orleans, Louisiana, USA (2018).

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@inproceedings{krestel-naacl18, abstract = {The overwhelming success of the Web and mobile technologies has enabled millions to share their opinions publicly at any time. But the same success also endangers this freedom of speech due to closing down of participatory sites misused by individuals or interest groups. We propose to support manual moderation by proactively drawing the attention of our moderators to article discussions that most likely need their intervention. To this end, we predict which articles will receive a high number of comments. In contrast to existing work, we enrich the article with metadata, extract semantic and linguistic features, and exploit annotated data from a foreign language corpus. Our logistic regression model improves F1-scores by over 80% in comparison to state-of-the-art approaches.}, author = {Ambroselli, Carl and Risch, Julian and Krestel, Ralf and Loos, Andreas}, booktitle = {Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2018)}, month = {June 1--6}, pages = {193--199}, publisher = {ACL}, title = {Prediction for the Newsroom: Which Articles Will Get the Most Comments?}, year = 2018 }
Risch, J., Krestel, R.: My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections. Proceedings of the 18th ACM/IEEE Joint Conference on Digital Libraries (JCDL) (2018).

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@inproceedings{risch2018approach, abstract = {Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such as digital libraries. However, topic modeling on documents from different collections is challenging because of domain-specific vocabulary. We present a cross-collection topic model combined with automatic domain term extraction and phrase segmentation. This model distinguishes collection-specific and collection-independent words based on information entropy and reveals commonalities and differences of multiple text collections. We evaluate our model on patents, scientific papers, newspaper articles, forum posts, and Wikipedia articles. In comparison to state-of-the-art cross-collection topic modeling, our model achieves up to 13% higher topic coherence, up to 4% lower perplexity, and up to 31% higher document classification accuracy. More importantly, our approach is the first topic model that ensures disjunct general and specific word distributions, resulting in clear-cut topic representations.}, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the 18th ACM/IEEE Joint Conference on Digital Libraries (JCDL)}, title = {My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections}, year = 2018 }
Maschler, F., Niephaus, F., Risch, J.: Real or Fake? Large-Scale Validation of Identity Leaks. 47. Jahrestagung der Gesellschaft für Informatik (INFORMATIK). bll. 2437-2448 (2017).

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@inproceedings{maschler2017largescale, abstract = {On the Internet, criminal hackers frequently leak identity data on a massive scale. Subsequent criminal activities, such as identity theft and misuse, put Internet users at risk. Leak checker services enable users to check whether their personal data has been made public. However, automatic crawling and identification of leak data is error-prone for different reasons. Based on a dataset of more than 180 million leaked identity records, we propose a software system that identifies and validates identity leaks to improve leak checker services. Furthermore, we present a proficient assessment of leak data quality and typical characteristics that distinguish valid and invalid leaks.}, author = {Maschler, Fabian and Niephaus, Fabio and Risch, Julian}, booktitle = {47. Jahrestagung der Gesellschaft für Informatik (INFORMATIK)}, pages = {2437-2448}, title = {Real or Fake? Large-Scale Validation of Identity Leaks}, year = 2017 }
Krestel, R., Risch, J.: How Do Search Engines Work? A Massive Open Online Course with 4000 Participants. Proceedings of the Conference Lernen, Wissen, Daten, Analysen. bll. 259-271 (2017).

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@inproceedings{krestel2017search, abstract = {Massive Open Online Courses (MOOCs) have introduced a new form of education. With thousands of participants per course, lectur- ers are confronted with new challenges in the teaching process. In this pa- per, we describe how we conducted an introductory information retrieval course for participants from all ages and educational backgrounds. We analyze different course phases and compare our experiences with regular on-site information retrieval courses at university.}, author = {Krestel, Ralf and Risch, Julian}, booktitle = {Proceedings of the Conference Lernen, Wissen, Daten, Analysen}, month = {September}, pages = {259-271}, title = {How Do Search Engines Work? A Massive Open Online Course with 4000 Participants}, year = 2017 }
Risch, J., Krestel, R.: What Should I Cite? Cross-Collection Reference Recommendation of Patents and Papers. Proceedings of the International Conference on Theory and Practice of Digital Libraries (TPDL). bll. 40-46 (2017).

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@inproceedings{risch2017what, abstract = {Research results manifest in large corpora of patents and scientific papers. However, both corpora lack a consistent taxonomy and references across different document types are sparse. Therefore, and because of contrastive, domain-specific language, recommending similar papers for a given patent (or vice versa) is challenging. We propose a hybrid recommender system that leverages topic distributions and key terms to recommend related work despite these challenges. As a case study, we evaluate our approach on patents and papers of two fields: medical and computer science. We find that topic-based recommenders complement term-based recommenders for documents with collection-specific language and increase mean average precision by up to 23%. As a result of our work, publications from both corpora form a joint digital library, which connects academia and industry.}, author = {Risch, Julian and Krestel, Ralf}, booktitle = {Proceedings of the International Conference on Theory and Practice of Digital Libraries (TPDL)}, pages = {40-46}, title = {What Should I Cite? Cross-Collection Reference Recommendation of Patents and Papers}, year = 2017 }
Bleifuß, T., Bülow, S., Frohnhofen, J., Risch, J., Wiese, G., Kruse, S., Papenbrock, T., Naumann, F.: Approximate Discovery of Functional Dependencies for Large Datasets. Proceedings of the International Conference on Information and Knowledge Management (CIKM). bll. 1803-1812. ACM, New York, NY, USA (2016).

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@inproceedings{bleifuss2016approximate, abstract = {Functional dependencies (FDs) are an important prerequisite for various data management tasks, such as schema normalization, query optimization, and data cleansing. However, automatic FD discovery entails an exponentially growing search and solution space, so that even today’s fastest FD discovery algorithms are limited to small datasets only, due to long runtimes and high memory consumptions. To overcome this situation, we propose an approximate discovery strategy that sacrifices possibly little result correctness in return for large performance improvements. In particular, we introduce AID-FD, an algorithm that approximately discovers FDs within runtimes up to orders of magnitude faster than state-of-the-art FD discovery algorithms. We evaluate and compare our performance results with a focus on scalability in runtime and memory, and with measures for completeness, correctness, and minimality.}, address = {New York, NY, USA}, author = {Bleifuß, Tobias and Bülow, Susanne and Frohnhofen, Johannes and Risch, Julian and Wiese, Georg and Kruse, Sebastian and Papenbrock, Thorsten and Naumann, Felix}, booktitle = {Proceedings of the International Conference on Information and Knowledge Management (CIKM)}, pages = {1803-1812}, publisher = {ACM}, series = {CIKM '16}, title = {Approximate Discovery of Functional Dependencies for Large Datasets}, year = 2016 }
Hennig, P., Berger, P., Dullweber, C., Finke, M., Maschler, F., Risch, J., Meinel, C.: Social Media Story Telling. Proceedings of the 8th IEEE International Conference on Social Computing and Networking (SocialCom2015). , Chengdu, China (2015).

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@inproceedings{2015_Hennig_SocialCom, abstract = {The number of documents on the web increases rapidly and often there is an enormous information overlap between different sources covering the same topic. Since it is impractical to read through all posts regarding a subject, there is a need for summaries combining the most relevant facts. In this context combining information from different sources in form of stories is an important method to provide perspective, while presenting and enriching the existing content in an interesting, natural and narrative way. Today, stories are often not available or they have been elaborately written and selected by journalists. Thus, we present an automated approach to create stories from multiple input documents. Furthermore the developed framework implements strategies to visualize stories and link content to related sources of information, such as images, tweets and encyclopedia records ready to be explored by the reader. Our approach combines deriving a story line from a graph of interlinked sources with a story-centric multi-document summarization.}, address = {Chengdu, China}, author = {Hennig, Patrick and Berger, Philipp and Dullweber, Christian and Finke, Moritz and Maschler, Fabian and Risch, Julian and Meinel, Christoph}, booktitle = {Proceedings of the 8th IEEE International Conference on Social Computing and Networking (SocialCom2015)}, month = 12, title = {Social Media Story Telling}, year = 2015 }
Schmidt, D., Frohnhofen, J., Knebel, S., Meinel, F., Perchyk, M., Risch, J., Striebel, J., Wachtel, J., Baudisch, P.: Ergonomic Interaction for Touch Floors. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. bl. 3879--3888. ACM, Seoul, Republic of Korea (2015).

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@inproceedings{Schmidt:2015:EIT:2702123.2702254, abstract = {The main appeal of touch floors is that they are the only direct touch form factor that scales to arbitrary size, therefore allowing direct touch to scale to very large numbers of display objects. In this paper, however, we argue that the price for this benefit is bad physical ergonomics: prolonged standing, especially in combination with looking down, quickly causes fatigue and repetitive strain. We propose addressing this issue by allowing users to operate touch floors in any pose they like, including sitting and lying. To allow users to transition between poses seamlessly, we present a simple pose-aware view manager that supports users by adjusting the entire view to the new pose. We support the main assumption behind the work with a simple study that shows that several poses are indeed more ergonomic for touch floor interaction than standing. We ground the design of our view manager by analyzing, which screen regions users can see and touch in each of the respective poses.}, address = {New York, NY, USA}, author = {Schmidt, Dominik and Frohnhofen, Johannes and Knebel, Sven and Meinel, Florian and Perchyk, Mariya and Risch, Julian and Striebel, Jonathan and Wachtel, Julia and Baudisch, Patrick}, booktitle = {Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems}, pages = {3879--3888}, publisher = {ACM}, series = {CHI '15}, title = {Ergonomic Interaction for Touch Floors}, year = 2015 }