Nava Tintarev


“It’s Not Just Hate”: A Multi-Dimensional Perspective on Detecting Harmful Speech Online
Federico Bianchi | Stefanie HIlls | Patricia Rossini | Dirk Hovy | Rebekah Tromble | Nava Tintarev
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for annotating offensive online speech. Detecting offensive content is rapidly becoming one of the most important real-world NLP tasks. However, most datasets use a single binary label, e.g., for hate or incivility, even though each concept is multi-faceted. This modeling choice severely limits nuanced insights, but also performance.We show that a more fine-grained multi-label approach to predicting incivility and hateful or intolerant content addresses both conceptual and performance issues.We release a novel dataset of over 40,000 tweets about immigration from the US and UK, annotated with six labels for different aspects of incivility and intolerance.Our dataset not only allows for a more nuanced understanding of harmful speech online, models trained on it also outperform or match performance on benchmark datasets


Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content
Myrthe Reuver | Nicolas Mattis | Marijn Sax | Suzan Verberne | Nava Tintarev | Natali Helberger | Judith Moeller | Sanne Vrijenhoek | Antske Fokkens | Wouter van Atteveldt
Proceedings of the 1st Workshop on NLP for Positive Impact

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.


Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems
Alisa Rieger | Mariët Theune | Nava Tintarev
2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence

Cognitive biases in the context of consuming online information filtered by recommender systems may lead to sub-optimal choices. One approach to mitigate such biases is through interface and interaction design. This survey reviews studies focused on cognitive bias mitigation of recommender system users during two processes: 1) item selection and 2) preference elicitation. It highlights a number of promising directions for Natural Language Generation research for mitigating cognitive bias including: the need for personalization, as well as for transparency and control.


MinkApp: Generating Spatio-temporal Summaries for Nature Conservation Volunteers
Nava Tintarev | Yolanda Melero | Somayajulu Sripada | Elizabeth Tait | Rene Van Der Wal | Chris Mellish
INLG 2012 Proceedings of the Seventh International Natural Language Generation Conference


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Using NLG and Sensors to Support Personal Narrative for Children with Complex Communication Needs
Rolf Black | Joseph Reddington | Ehud Reiter | Nava Tintarev | Annalu Waller
Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies