Rada Mihalcea selected for NSF INSPIRE Award

Prof. Rada Mihalcea has been awarded an INSPIRE (Integrated NSF Support Promoting Interdisciplinary Research and Education) Award from the National Science Foundation for an interdisciplinary project on tracking cultural diversity.

The project, headed by Prof. Mihalcea and entitled “Language-Based Computational Methods for Analyzing Worldviews,” will be conducted in collaboration with Prof. James Pennebaker in the Department of Psychology at The University of Texas at Austin. The goal of the three-year study is to gather new insights into the ways people organize and understand their worlds within and across different cultures by mean of innovative methodologies and tools from the fields of psychology and computational linguistics.

The project will track the underlying values, beliefs, and concerns of very large groups of people by analyzing the ways they use words. The findings from this project will provide a better understanding of people on the individual psychological level as well as the cultures themselves, while developing and demonstrating new research techniques that can be used in future by many disciplines to exploit the vast troves of scientifically valuable textual data currently available online.

Specifically, the project targets the following three main research objectives: 1) Construct a very large multicultural database of writings from English-speaking cultures, covering several styles and genres, including: social media (e.g., blogs, tweets); news articles; literary works; student writings. 2) Build computational linguistic models that can automatically identify differences in concept usage for different cultures, and apply these models on a large scale. 3) Validate the findings of these computational models through psychological qualitative and quantitative methods in laboratory studies.

Unlike previous studies, this project will utilize large datasets of differences in perception for thousands of concepts, by several cultures representing hundreds of thousands of people. It will inform applications in communication, tracking of cultural values, and others. The project will also provide educational opportunities, in the form of training for students in both computer science and psychology, who will be directly exposed to interdisciplinary research, cultural diversity, and international experiences. Finally, the large multicultural dataset that will be created as part of this project, along with the tools to process it, will be made publicly available, thus enabling future research, as well as educational projects concerned with the analysis and understanding of cultural diversity and world view.

Prof. Rada Mihalcea received her PhD in Computer Science and Engineering from Southern Methodist University. She joined the faculty at Michigan in 2013 after serving on the faculty at the Department of Computer Science at the University of North Texas.

Prof. Mihalcea’s research interests are in computational linguistics, with a focus on lexical semantics, graph-based algorithms for natural language processing, and multilingual natural language processing. She is currently involved in a number of research projects, including word sense disambiguation, monolingual and cross-lingual semantic similarity, subjectivity, sentiment, and emotion analysis, multimodal affect analysis, and computational humor.

She is the recipient of a NSF CAREER award and a Presidential Early Career Award for Scientists and Engineers. In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.

About the NSF INSPIRE Award

The NSF INSPIRE awards program was established to address some of the most complicated and pressing scientific problems that lie at the intersection of traditional disciplines.  It is intended to encourage investigators to submit bold, exceptional proposals. INSPIRE is open to interdisciplinary proposals on any NSF-supported topic, submitted by invitation only after a preliminary inquiry process initiated by submission of a required Letter of Intent.