{"id":79231,"date":"2018-11-27T08:33:01","date_gmt":"2018-11-27T12:33:01","guid":{"rendered":"http:\/\/www.bu.edu\/eng\/?p=79231"},"modified":"2022-10-31T12:09:46","modified_gmt":"2022-10-31T16:09:46","slug":"what-if-you-could-manage-information-overload","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/eng\/2018\/11\/27\/what-if-you-could-manage-information-overload\/","title":{"rendered":"What If You Could Manage Information Overload?"},"content":{"rendered":"

Groundbreaking collaboration among BU schools and researchers leads to $1 million NSF grant to address media overload<\/h2>\n
\n

By Kate Becker for BU Research<\/a>\u00a0<\/span><\/span><\/span><\/span><\/em><\/p>\n<\/header>\n

\"Lei
Lei Guo (from left) Prakash Ishwar, Derry Wijaya, and Margrit Betke. Photo by Cydney Scott<\/figcaption><\/figure>\n

Between news articles, tweets, Facebook memes, online videos, Reddit threads, and all the other media sources that we routinely tap into, it seems impossible these days to be informed without being overloaded, or to stay connected without getting hopelessly tangled up.<\/p>\n

But what if there were a machine or mechanism that could take in all that information\u2014words, pictures, posts, and videos, even in dozens of different languages\u2014and somehow make sense of it all? Better yet, what if that machine could measure public sentiment about any given event, figure out how different media outlets are covering it, and unscramble the relationship between the two?<\/p>\n

That\u2019s the goal of a boundary-breaking collaboration between Boston University researchers Margrit Betke, College of Arts & Sciences professor of computer science, Prakash Ishwar, College of Engineering professor of electrical and computer engineering and systems engineering, Lei Guo, College of Communication assistant professor of emerging media studies, and Derry Wijaya, College of Arts & Sciences assistant professor of computer science. In September 2018, the BU team received a $1 million, four-year research grant from the National Science Foundation to advance their work.<\/p>\n

The research itself is as much a part of this unusual story as the researchers who pulled it together while coming from a diverse collection of BU schools.<\/p>\n

A Lecture. A Spark.<\/h3>\n

The backstory of how these unlikely collaborators found each other goes back to a lecture hall at the Hariri Institute for Computing. That\u2019s where, in November 2015, Guo, then brand-new to the BU faculty, delivered a packed-house talk as a Hariri Junior Faculty Fellow about using \u201cbig data\u201d methods to analyze online communication. Guo, who studies how media influences public opinion (and vice versa), was explaining how she had used computers to help analyze 77 million tweets about the 2012 presidential election, in which President Barack Obama defeated Republican Mitt Romney.<\/p>\n

Until that point, media research had traditionally been done manually, with students and scholars laboriously poring over and classifying text. Guo wanted to show that computers could help make sense of data troves\u2014like those election tweets\u2014too big to be parsed by hand.<\/p>\n

Betke was in the audience, and she was dazzled. \u201cI was sitting there like, \u2018Wow, this is so exciting!\u2019\u201d But Betke studies computer vision, not text. She didn\u2019t see how her computer science expertise could apply to the political problems Guo was working on.<\/p>\n

That might have been the end of the story, but Betke\u2019s graduate student Mehrnoosh Sameki (GRS\u201917) was just as exhilarated by Guo\u2019s talk, and even keener to pursue a partnership. Betke recalls Sameki insisting, \u201cCan\u2019t we just think of something that connects the fields?\u201d<\/p>\n

That thought was still simmering a few months later, when the researchers ran into each other at BU Data Science Day, another Hariri Institute event, and began sketching out a collaboration.
\nThe team would also include Ishwar, who studies machine learning\u2014that is, creating computer algorithms that can \u201clearn\u201d to make decisions based on a set of examples. \u201cMachine learning helps us to scale up the processing and analysis of big data,\u201d says Ishwar. \u201cIt can never be a complete replacement of human expertise\u2014at least not in the near future\u2014but it is a catalyst which aids, accelerates, and amplifies human expertise\u2013based analysis of data.\u201d<\/p>\n

They quickly discovered that their fields had more in common than they thought.<\/p>\n

\u201cThere are machine learning tools that actually apply to both fields: looking for patterns in images, looking for patterns in text. That enabled us to work together,\u201d said Betke.<\/p>\n

But the team wasn\u2019t complete.<\/p>\n

The Missing Piece<\/h3>\n

\u201cWe needed a fourth person who is really good at automated analysis of text,\u201d recalls Betke. That person was Derry Wijaya, who joined the BU faculty in September 2018. Wijaya is an expert in natural language processing\u2014that is, making computer programs that understand ordinary speech and writing, not computer code\u2014and is especially interested in multilingual systems that can learn as many as 100 languages, even with minimal input. \u201cWe had this perfect team of different backgrounds come together and the expertise is just such a good mix,\u201d says Betke.<\/p>\n

The story illustrates the precise kind of planned serendipity that the Hariri Institute wants to cultivate, says its director, Azer Bestavros, CAS professor of computer science. \u201cComputer science is not just about engineering computing devices and platforms anymore\u2014it\u2019s really about the innovation that emerges from integrating our ways of thinking and our ways of doing into every discipline,\u201d says Bestavros. \u201cThe vision of the Institute is to make that happen by connecting computer scientists with opportunities that span the landscape of academic disciplines,\u201d he says.<\/p>\n

In addition to programs that expose researchers to the possibilities of data science, the Hariri Institute also serves as an \u201cincubator\u201d that disperses small seed grants.<\/p>\n

And so, in 2016, with another presidential contest looming, Guo, Ishwar, Betke, and their collaborators applied for and won a Hariri Research Award to develop their techniques and apply them to tweets and YouTube videos about the election<\/a>. The year after that, they parlayed that success into a Google research award<\/a>. Their goal: to make a better Google news feed, one that could automatically incorporate diverse viewpoints and serve as an antidote to the \u201cecho chamber effect\u201d that exposes readers only to stories that reflect and reaffirm their existing opinions.<\/p>\n

Research Rewarded<\/h3>\n

The $1 million NSF grant is the biggest yet for the project, and will unfold in three phases: First comes the data gathering\u2014collecting thousands of news stories, lead photos, video clips, comments, tweets, and more; then, tapping into crowdsourcing workforces like Amazon\u2019s Mechanical Turk, as well as analysis efforts from communication researchers, they will begin manually analyzing the relatively small selection of the media in their library. They will start with basic questions\u2014What is this article about? Who is in it? Where did it take place?\u2014and move on to trickier judgments, like whether the item is largely positive or negative; finally, using machine learning, they will begin \u201ctraining\u201d the computer to make human-like judgments about the media in their library.<\/p>\n

Other big-data studies have used artificial intelligence to pick out the who-what-where from news stories. What makes this one unique is it will be the first to try to determine something subtler: the story\u2019s particular point of view, or frame.<\/p>\n

\u201cTo analyze a mass shooting, for example, there are a lot of perspectives to talk about it,\u201d says Guo. The same event could become a human-interest story, a policy op-ed, or an economic impact report. \u201cFraming analysis in our field is always done using student coders or researchers to manually go through the article,\u201d says Guo. \u201cWe want to see the limits of what machine learning can do.\u201d<\/p>\n

The team hopes that including images and video in their analysis will help the computer judge framing more accurately. For example, the lead image on a human-interest story is likely to be a face or faces, while a story on new legislation might be topped with a picture of the White House.<\/p>\n

The researchers will also look at how stories are told differently around the world. \u201cWe\u2019re expecting that the frames that people use outside the US are different from the ones in the US,\u201d says Betke.<\/p>\n

Even before it\u2019s analyzed, the dataset, incorporating media in 100 different languages, will be a first-of-its-kind tool for researchers who study language, says Wijaya: \u201cHaving a dataset actually is very important to move further in research, to push the boundaries of what\u2019s possible.\u201d<\/p>\n

And the Hariri Institute is there to connect researchers who want to push boundaries, says Bestavros. \u201cHow do you empower them, or magnify what they do?\u201d he asks. \u201cYou connect them to one another and help them incubate their ideas.\u201d<\/p>\n

 <\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

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