Benjamin Toff

Benjamin Toff

1. What are your current research interests?

Broadly, I study the public's relationship to news and journalism. A lot of that work has focused on subjects like news avoidance and trust in news. More recently, I've also been studying the supply of local news and information in Minnesota and how that ecosystem has been changing as well as working with news organizations around evaluating the effectiveness of different strategies some are deploying to better engage with the public and rebuild trust. I'm also working on projects around the growing use of artificial intelligence in newsrooms and the public's expectations around disclosure, transparency, and labeling. Finally, I'm leading an Interdisciplinary Collaborative Workshop around the 80th anniversary of the Minnesota Poll, which the Star Tribune now fields in partnership with MPR News and KARE-11. We are using the occasion as a jumping off point for thinking about the past, present, and future of state-level public opinion research.
 
2. How do you define Data Science?
 
This is hard. I mostly leave it to others to try and define the field—to the extent it can even be defined as a field. But basically I see it as a set of approaches around harnessing and wrangling different kinds of structured and unstructured data about the world, which allow researchers to produce new knowledge and gain deeper insights. 

3. Can you share an interesting or surprising result you’ve found in your data?
 
In a study I led recently looking at how audiences responded to news generated with the help of AI, we found that when people read stories labeled as having been produced with the help of AI, they evaluate the news organizations responsible for producing that content as less trustworthy even as they don't necessarily perceive the articles themselves as any less accurate or fair or biased. We think it points to some important tensions that news organizations will need to navigate as they adopt these tools for various purposes and seek to be transparent about how they are doing so.

4. Are there any interesting new tools or libraries you or your students have been using?
 
In our project mapping the local news ecosystem in Minnesota, we are building a database of news sources, geocoding them, and building interactive maps using ArcGIS through U-Spatial.


5. What are you most excited about in the field of data science in the next 5 years?


I am particularly excited about advances in using AI to analyze large scale unstructured content like news articles and audio and video files. It's one thing to be able to understand the local news ecosystem in terms of where we know there are outlets and journalists. It's another thing to be able to analyze the content they are producing to be able to measure what communities are and are not being covered and what topics and stories are getting attention in these media. Researchers have been analyzing news content for decades, but typically these studies can only do so at a small scale in very constrained ways. These advances are opening up a lot of possibilities for the study of the information environment and civic life.

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WiADS 2024 Conference: A Day of Inspiration and Connection for Women in Data Science

On November 4th, 2024, the University of Minnesota hosted the highly anticipated Women in AI and Data Science (WiADS) Conference, organized in partnership with MinneAnalytics at the McNamara Alumni Center. This sold-out event brought together over 1,000 registrants, including over 550 in-person attendees, and showcased the work of women, non-binary, and gender-diverse voices in data science.