publications

2025

1. Benchmarking the Benchmarks: Reproducing Climate-Related NLP Tasks

   Tom Calamai, Oana Balalau, and Fabian M. Suchanek

   In Findings of the Association for Computational Linguistics: ACL 2025, Jul 2025

   DOI
2. The Search for Conflicts of Interest: Open Information Extraction in Scientific Publications

   Garima Gaur, Oana Balalau, Ioana Manolescu, and 1 more author

   In Findings of the Association for Computational Linguistics: EMNLP 2025, Nov 2025

   DOI
3. Navigating the Political Compass: Evaluating Multilingual LLMs across Languages and Nationalities

   Chadi Helwe, Oana Balalau, and Davide Ceolin

   In Findings of the Association for Computational Linguistics: ACL 2025, Jul 2025

   DOI
4. Structured Discourse Representation for Factual Consistency Verification

   Kun Zhang, Oana Balalau, and Ioana Manolescu

   In Findings of the Association for Computational Linguistics: ACL 2025, Jul 2025

   DOI

2024

1. Finding Conflicts of Opinion in Citizen Participation Platforms

   William Aboucaya, Oana Balalau, Rafael Angarita, and 1 more author

   In 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2024

   DOI
2. STaR: Space and Time-aware Statistic Query Answering

   Oana Balalau, Simon Ebel, Helena Galhardas, and 2 more authors

   In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, Boise, ID, USA, 2024

   Abs DOI

   High-quality data is essential for informed public debate. High-quality statistical data sources provide valuable reference information for verifying claims. To assist journalists and fact-checkers, user queries about specific claims should be automatically answered using statistical tables. However, the large number and variety of these sources make this task challenging.We propose to demonstrate STaR, a novel method for Space and Time-aware STatistic Retrieval, based on a user natural language query. STaR is deployed within our system StatCheck, which we developed and shared with fact-checking journalists. STaR improves the quality of statistic fact retrieval by treating space and time separately from the other parts of the statistics dataset. Specifically, we use them as dimensions of the data (and the query), and focus the linguistic part of our dataset search on the rich, varied language present in the data. Our demonstration uses statistic datasets from France, Europe, and a few beyond, allowing users to query and explore along space and time dimensions.

3. FactCheckBureau: Build Your Own Fact-Check Analysis Pipeline

   Oana Balalau, Pablo Bertaud-Velten, Younes El Fraihi, and 5 more authors

   In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, Boise, ID, USA, 2024

   Abs DOI

   Fact-checkers are overwhelmed by the volume of claims they need to pay attention to fight misinformation. Even once debunked, a claim may still be spread by people unaware that it is false, or it may be recycled as a source of inspiration by malicious users. Hence, the importance of fact-check (FC) retrieval as a research problem: given a claim and a database of previous checks, find the checks relevant to the claim. Existing solutions addressing this problem rely on the strategy of retrieve and re-rank relevant documents. We have built FactCheckBureau, an end-to-end solution that enables researchers to easily and interactively design and evaluate FC retrieval pipelines. We also present a corpus we have built, which can be used in further research to test fact-check retrieval tools. The source code of our tool is available at this link.

4. Finding Subgraphs with Maximum Total Density and Limited Overlap in Weighted Hypergraphs

   Oana Balalau, Francesco Bonchi, T-H. Hubert Chan, and 3 more authors

   ACM Trans. Knowl. Discov. Data, Feb 2024

   Abs DOI

   Finding dense subgraphs in large (hyper)graphs is a key primitive in a variety of real-world application domains, encompassing social network analytics, event detection, biology, and finance. In most such applications, one typically aims at finding several (possibly overlapping) dense subgraphs, which might correspond to communities in social networks or interesting events. While a large amount of work is devoted to finding a single densest subgraph, perhaps surprisingly, the problem of finding several dense subgraphs in weighted hypergraphs with limited overlap has not been studied in a principled way, to the best of our knowledge. In this work, we define and study a natural generalization of the densest subgraph problem in weighted hypergraphs, where the main goal is to find at most k subgraphs with maximum total aggregate density, while satisfying an upper bound on the pairwise weighted Jaccard coefficient, i.e., the ratio of weights of intersection divided by weights of union on two nodes sets of the subgraphs. After showing that such a problem is NP-Hard, we devise an efficient algorithm that comes with provable guarantees in some cases of interest, as well as, an efficient practical heuristic. Our extensive evaluation on large real-world hypergraphs confirms the efficiency and effectiveness of our algorithms.

2023

1. FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation

   Kun Zhang, Oana Balalau, and Ioana Manolescu

   In Findings of the Association for Computational Linguistics: EMNLP 2023, Dec 2023

   DOI
2. Open Information Extraction with Entity Focused Constraints

   Prajna Upadhyay, Oana Balalau, and Ioana Manolescu

   In Findings of the Association for Computational Linguistics: EACL 2023, May 2023

   DOI

2022

1. Statistical Claim Checking: StatCheck in Action

   Oana Balalau, Simon Ebel, Théo Galizzi, and 8 more authors

   In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, 2022

   Abs DOI

   To strengthen public trust and counter disinformation, computational fact-checking, leveraging digital data sources, attracts interest from the journalists and the computer science community. A particular class of interesting data sources is statistics, that is, numerical data compiled mostly by governments, administrations, and international organizations. Statistics typically are multidimensional datasets, where multiple dimensions characterize one value, and the dimensions may be organized in a hierarchy.We developed StatCheck, a fact-checking system specialized in French. The technical novelty of StatCheck is twofold: (i) we focus on multidimensional, complex-structure statistics, which have received little attention so far, despite their practical importance; and (ii) novel statistical claim extraction modules for French, an area where few resources exist. We will demonstrate our system on large statistic datasets (hundreds of millions of facts), including the complete INSEE (French) and Eurostat (European Union) datasets.More information about StatCheckis available online at: https://team.inria.fr/cedar/projects/statcheck/.

2. GraphCite: Citation Intent Classification in Scientific Publications via Graph Embeddings

   Dan Berrebbi, Nicolas Huynh, and Oana Balalau

   In Companion Proceedings of the Web Conference 2022, Virtual Event, Lyon, France, 2022

   Abs DOI

   Citations are crucial in scientific works as they help position a new publication. Each citation carries a particular intent, for example, to highlight the importance of a problem or to compare against results provided by another method. The authors’ intent when making a new citation has been studied to understand the evolution of a field over time or to make recommendations for further citations. In this work, we address the task of citation intent prediction from a new perspective. In addition to textual clues present in the citation phrase, we also consider the citation graph, leveraging high-level information of citation patterns. In this novel setting, we perform a thorough experimental evaluation of graph-based models for intent prediction. We show that our model, GraphCite, improves significantly upon models that take into consideration only the citation phrase. Our code is available online1.

3. Graph integration of structured, semistructured and unstructured data for data journalism

   Angelos Christos Anadiotis, Oana Balalau, Catarina Conceição, and 5 more authors

   Information Systems, 2022

   Abs DOI

   Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations, or small public administrations) need to be able to make sense of such heterogeneous corpora, even if they lack the ability to define and deploy custom extract-transform-load workflows, especially for dynamically varying sets of data sources. We describe a complete approach for integrating dynamic sets of heterogeneous datasets along the lines described above: the challenges we faced to make such graphs useful, allow their integration to scale, and the solutions we proposed for these problems. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments.

2021

1. Discovering Conflicts of Interest across Heterogeneous Data Sources with ConnectionLens

   Angelos-Christos Anadiotis, Oana Balalau, Théo Bouganim, and 6 more authors

   In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Event, Queensland, Australia, 2021

   Abs DOI

   Investigative Journalism (IJ, in short) requires combining highly heterogeneous digital datasets coming from a wide variety of sources. We have developed ConnectionLens, a system that integrates such sources into a single heterogeneous graph and enables users to query the graph using keywords. The first iteration of the system [7] followed a mediator architecture which severely constrained its query scalability. Thus, we fully re-engineered the system, moving it to a warehouse architecture, and replacing its core components (information extraction, data querying, and interactive interfaces), which allowed us to handle uses cases orders of magnitude larger than the previous platform. In a consortium of computer scientists and investigative journalists, we propose to demonstrate ConnectionLens’ capability to integrate arbitrary heterogeneous datasets and query them flexibly by means of keywords. Among several scenarios, our main focus will be on a real-world journalistic use case about situations which may lead to Conflicts of Interest between biomedical experts and various organizations, such as corporations, lobbies, etc. The demonstration will showcase the end-to-end data analysis pipeline, illustrate each system component, and the different parameters governing graph creation and querying.

2. From the Stage to the Audience: Propaganda on Reddit

   Oana Balalau and Roxana Horincar

   In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Apr 2021

   DOI
3. Breaking Down the Invisible Wall of Informal Fallacies in Online Discussions

   Saumya Sahai, Oana Balalau, and Roxana Horincar

   In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Aug 2021

   DOI

2020

1. Subrank: Subgraph embeddings via a subgraph proximity measure

   Oana Balalau and Sagar Goyal

   In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2020
2. Prince: Provider-side interpretability with counterfactual explanations in recommender systems

   Azin Ghazimatin, Oana Balalau, Rishiraj Saha Roy, and 1 more author

   In Proceedings of the 13th International Conference on Web Search and Data Mining, 2020

2019

1. Discovering the Functions of Language in Online Forums

   Youmna Ismaeil, Oana Balalau, and Paramita Mirza

   In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), Nov 2019

   DOI
2. Analyzing the traits and anomalies of political discussions on reddit

   Anna Guimaraes, Oana Balalau, Erisa Terolli, and 1 more author

   In Proceedings of the International AAAI Conference on Web and Social Media, 2019

2018

1. Listing k-cliques in Sparse Real-World Graphs*

   Maximilien Danisch, Oana Balalau, and Mauro Sozio

   In Proceedings of the 2018 World Wide Web Conference, Lyon, France, 2018

   Abs DOI

   Motivated by recent studies in the data mining community which require to efficiently list all k-cliques, we revisit the iconic algorithm of Chiba and Nishizeki and develop the most efficient parallel algorithm for such a problem. Our theoretical analysis provides the best asymptotic upper bound on the running time of our algorithm for the case when the input graph is sparse. Our experimental evaluation on large real-world graphs shows that our parallel algorithm is faster than state-of-the-art algorithms, while boasting an excellent degree of parallelism. In particular, we are able to list all k-cliques (for any k) in graphs containing up to tens of millions of edges as well as all 10-cliques in graphs containing billions of edges, within a few minutes and a few hours respectively. Finally, we show how our algorithm can be employed as an effective subroutine for finding the k-clique core decomposition and an approximate k-clique densest subgraphs in very large real-world graphs.

2016

1. Finding heaviest k-subgraphs and events in social media

   Matthaios Letsios, Oana Denisa Balalau, Maximilien Danisch, and 2 more authors

   In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016

2015

1. Finding subgraphs with maximum total density and limited overlap

   Oana Denisa Balalau, Francesco Bonchi, TH Hubert Chan, and 2 more authors

   In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 2015