| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 13, 2018 |
| Latest Amendment Date: | August 13, 2018 |
| Award Number: | 1813662 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Hector Munoz-Avila
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2018 |
| End Date: | August 31, 2022 (Estimated) |
| Total Intended Award Amount: | $499,682.00 |
| Total Awarded Amount to Date: | $499,682.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
101 COMMONWEALTH AVE AMHERST MA US 01003-9252 (413)545-0698 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
OGCA, 100 Venture Way, Ste. 201 Hadley MA US 01035-9450 |
| 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): | Info Integration & Informatics |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This project aims to develop algorithms and tools that allow a person to recognize that a web page or other document discusses one or more topics that are controversial -- that is, about which there is strong disagreement within some sizeable group of people. The project will develop algorithms and tools that explain the controversy surrounding the topic, identifying the populations that disagree, the stances that they take, and how those stances conflict with each other. The advances in these algorithms will broaden the research community's understanding of how discussions and disagreements on topics can be modeled computationally and how that resulting information can be conveyed to a general user. The project will assist people in critical evaluation of on-line material and help them understand why a page is educative or why it is not.
The aim of this project is to provide users with tools that illuminate the broader context of the topic or topics of a single page or document that someone finds. Previous work has shown that it is possible to recognize with reasonable accuracy that a document is part of a controversial topic, but that work is fragmented across different genres, demands more robust modeling and more thorough evaluation, and lacks explanatory power that can help a reader understand why and how a text is contentious. In this project, the researchers explore fundamental questions about how controversy can be modeled computationally so that it can be recognized "in the wild". The project also explores model variations that allow an algorithm to extract an explanation of the nature of the controversy. The project applies and extends text analysis and comparison techniques. It leverages powerful statistical language modeling methods as well as recent neural network (deep learning) approaches to represent text, its controversial nature, its stances, and their relationships, all extracted from Web pages and other documents. The modeling will be initially used offline to identify collections of topics known to be controversial and then adapt that collection by monitoring slowly-changing news sources and blog postings as well as ephemeral microblog sources of data to capture rapid changes in controversy. The researchers will make the resulting techniques available by providing an open-source example server.
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.
A challenge faced by nearly every user of the web is how to know whether the text they are reading is accurate or is part of a large and controversial discussion. Attempts to address this problem focus on whether the author or source is authoritative, whether the language inappropriate, or similar high-level clues.
As an addition to those approaches, we have focused on explanation approaches that highlight differences between claims in one document and those in other documents -- ideally more authoritative statements of evidence. We developed several types of models: some that captured the relationship between documents and the query that retrieved them, some that extracted the key topics and subtopics of the documents to show the connections between different documents with different perspectives, and some that explicitly captured the alignment between words in different spans of text to explicitly highlight the disagreement across sentences. We created training and test collections of data that has been freely shared with the research community. We used that data to measure the effectiveness of our various approaches, finding that they generally outperformed existing approaches adapted to these problems. This grant supported the training of five PhD students (two of whom are female). It resulted in 9 publications, contributing to the theses of the students.
Last Modified: 01/09/2023
Modified by: James M Allan
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