| NSF Org: |
ITE Innovation and Technology Ecosystems |
| Recipient: |
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| Initial Amendment Date: | September 20, 2021 |
| Latest Amendment Date: | July 11, 2023 |
| Award Number: | 2137846 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Michael Reksulak
mreksula@nsf.gov (703)292-8326 ITE Innovation and Technology Ecosystems TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | October 1, 2021 |
| End Date: | September 30, 2024 (Estimated) |
| Total Intended Award Amount: | $750,000.00 |
| Total Awarded Amount to Date: | $750,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1805 N BROAD ST PHILADELPHIA PA US 19122-6104 (215)707-7547 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1925 N. 12th St. Philadelphia PA US 19122-1801 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Convergence Accelerator Resrch |
| Primary Program Source: |
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| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
In today?s world, the news industry is defined not only by the journalism it produces but by the resulting communication it engenders across the digital landscape. The edict that the most likely effect of communication is more communication is best defined as a proposition rather than a scientific principle due to a lack of empirical evidence concerning the full breadth and depth with which one act of communication produces a multitude of subsequent communicative engagements. Journalism is a unique setting to test this proposition in that the utility of any one piece of news is determined by what is done with it communicatively. While news organizations track and analyze immediate audience reactions to their content, such as views, likes, and shares, they have relatively little visibility and understanding of a complete news life cycle, which consists of several stages initiated by many actors with varied intentions. A necessary but not sufficient condition for the news media to build stronger levels of trust with the American people is to track, analyze, and understand the communication life cycle of their journalistic content to make more informed decisions about their work.
This project undertakes a big data approach to the study of the news life cycle that will provide news organizations with an important tool to begin to re-establish sufficient levels of trust with the American people. A big data approach from computer and data science, driven by agenda-setting theory from the social sciences, will help track the communication life cycle of local news across the Web. The journalism life cycle typically involves (i) news organizations generating and disseminating original news content, (ii) digital platforms (e.g., news aggregators) aiding dissemination, (iii) fellow news organizations sharing content, and (iv) audience feedback and dissemination. While most research in this arena has so far focused on national news organizations (e.g., The New York Times), we argue that local news is key to the industry re-asserting its normative democratic value. By leveraging computational techniques like natural language processing and network analysis, the project?s primary goal is to develop a journalist-in-the-loop system able to track the life cycle of local journalistic content to observe its uses and misuses across time and across digital platforms. The proposed system will identify through reaction-intention analyses and topic drift those stages when journalism?s intended effects evolve into positive or negative unintended outcomes. Unintended, negative communication effects of news include the triggering of uncivil, polarizing discourse, audience misinterpretation, the production of misinformation, and the perpetuation of false narratives (e.g., conspiracy theories).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The goals of the project are to build stronger ties between local news organizations and the communities they serve and inform local newsrooms about information gaps and issue publics in their localities. The goal was motivated by the main tenant learned from over 60 interviews that strengthening local news-community relationships will invigorate an industry that is facing challenging times and grant people a stable resource of quality reporting on the issues taking shape in their immediate surroundings. During the duration of the project, we undertook the following main activities:
Human-Centered Design: We conducted two rounds of user interviews. In the first round, we interviewed local journalists. In the second round of interviews, we focused on different types of media professionals such as social media editors, audience growth editors, and directors. We spoke with newsroom personnel from diverse localities and organizations that varied in type (e.g., nonprofit, commercial, independent) and scale (e.g., hyperlocal, local, regional). Other team members, including those from the tech group, attended the interviews. The team discussed the interview results in weekly meetings.
Analogous Users: We also conducted needfinding interviews with potential analogous users working in the healthcare, anti-violence advocacy, and advertising sectors. All interviewees expressed strong interest in our project goals. We talked with 52 local news professionals and 10 analogous users.
CommuniTies Platform: Based on the results from the interviews, we moved to develop a digital platform that would allow local news organizations to better understand the communities they wish to serve by seeing where their content is reaching, what impact it is having on community-level discourse, where their content still needs to get to, and how best to reach the communities that are most in need of quality information.
Prototype: We developed a prototype of the CommuniTies Web application. Our system took sample data from several social media platforms and analyzed it using techniques from graph mining and natural language processing, and displayed community-level insights about local news articles being shared. The prototype was shown as a demo during the Convergence Accelerator Expo 2022.
Artifacts: We reported our work in 7 publications. We 3 released datasets publicly. We also applied for a patent for our algorithm that helps users edit their social media messages before posting them online.
Student involvement: The project engaged 4 PhD students, 1 MS students, and 5 undergraduate students. Among these, 3 PhD students and 3 undergraduate students graduated.
Last Modified: 02/11/2025
Modified by: Eduard Dragut
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