Nicolas Mattis

Nicolas Mattis

PhD candidate | News selection & nudging in recommender systems, computational methods

Posts by Collection

portfolio

publications

Implementing Evaluation Metrics Based on Theories of Democracy in News Comment Recommendation (Hackathon Report)

Published in Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation, 2022

Diversity in news recommendation is important for democratic debate. Current recommendation strategies, as well as evaluation metrics for recommender systems, do not explicitly focus on this aspect of news recommendation. In the 2021 Embeddia Hackathon, we implemented one novel, normative theory-based evaluation metric, “activation”, and use it to compare two recommendation strategies of New York Times comments, one based on user likes and another on editor picks. We found that both comment recommendation strategies lead to recommendations consistently less activating than the available comments in the pool of data, but the editor’s picks more so. This might indicate that New York Times editors’ support a deliberative democratic model, in which less activation is deemed ideal for democratic debate.

Recommended citation: Reuver, M., & Mattis, N. (2021, April). Implementing evaluation metrics based on theories of democracy in news comment recommendation (Hackathon report). In Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation (pp. 134-139). https://aclanthology.org/2021.hackashop-1.19/

Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content

Published in Proceedings of the 1st Workshop on NLP for Positive Impact, 2022

In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation. Recommending news from diverse viewpoints is important to prevent potential filter bubble effects in news consumption, and stimulate a healthy democratic debate. To account for the complexity that is inherent to humans as citizens in a democracy, we anticipate (among others) individual-level differences in acceptance of diversity. We connect this idea to techniques in Natural Language Processing, where distributional language models would allow us to place different users and news articles in a multidimensional space based on semantic content, where diversity is operationalized as distance and variance. In this way, we can model individual “latitudes of diversity” for different users, and thus personalize viewpoint diversity in support of a healthy public debate. In addition, we identify technical, ethical and conceptual issues related to our presented ideas. Our investigation describes how NLP can play a central role in diversifying news recommendations.

Recommended citation: Reuver, M., Mattis, N., Sax, M., Verberne, S., Tintarev, N., Helberger, N., ... & van Atteveldt, W. (2021, August). Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content. In Proceedings of the 1st Workshop on NLP for Positive Impact (pp. 47-59). https://aclanthology.org/2021.nlp4posimpact-1.6/

Nudging towards news diversity: A theoretical framework for facilitating diverse news consumption through recommender design

Published in New Media & Society, 2022

Growing concern about the democratic impact of automatically curated news platforms urges us to reconsider how such platforms should be designed. We propose a theoretical framework for personalised diversity nudges that can stimulate diverse news consumption on the individual level. To examine potential benefits and limitations of existing diversity nudges, we conduct an interdisciplinary literature review that synthesises theoretical work on news selection mechanisms with hands-on tools and implementations from the fields of computer science and recommender systems. Based thereupon, we propose five diversity nudges that researchers and practitioners can build on. We provide a theoretical motivation of why, when and for whom such nudges could be effective, critically reflect on their potential backfire effects and the need for algorithmic transparency, and sketch out a research agenda for diversity-aware news recommender design. Thereby, we develop concrete, theoretically grounded avenues towards facilitating diverse news consumption on algorithmically curated platforms.

Recommended citation: Mattis, N., Masur, P., Möller, J., & van Atteveldt, W. (2022). Nudging towards news diversity: A theoretical framework for facilitating diverse news consumption through recommender design. new media & society, 14614448221104413. https://doi.org/10.1177/14614448221104413

Responsible Opinion Formation on Debated Topics in Web Search (to be published at ECIR 2024)

Published in European Conference on Information Retrieval - IR4 Good, 2024

Web search has evolved into a platform people rely on for opinion formation on debated topics. Yet, pursuing this search intent can carry serious consequences for individuals and society and involves a high risk of biases. We argue that web search can and should empower users to form opinions responsibly and that the information retrieval community is uniquely positioned to lead interdisciplinary efforts to this end. Building on digital humanism—a perspective focused on shaping technology to align with human values and needs—and through an extensive interdisciplinary literature review, we identify challenges and research opportunities that focus on the searcher, search engine, and their complex interplay. We outline a research agenda that provides a foundation for research efforts toward addressing these challenges.

Recommended citation: Rieger, A., Draws, T., Mattis, N., Maxwell, D., Esweiler, D., Gadiraju,U., McKay, D., Bozzon, A., & Pera, M. (2024, March). Responsible Opinion Formation on Debated Topics in Web Search. To be published in European Conference on Information Retrieval (pp. tbd). https://www.ecir2024.org/accepted-paper/

talks

teaching