{"id":218722,"date":"2017-02-21T12:10:41","date_gmt":"2017-02-21T17:10:41","guid":{"rendered":"http:\/\/www.rochester.edu\/newscenter\/?p=218722"},"modified":"2024-10-29T15:14:59","modified_gmt":"2024-10-29T19:14:59","slug":"twitter-provides-a-rich-vein-of-data-218722","status":"publish","type":"post","link":"https:\/\/www.rochester.edu\/newscenter\/twitter-provides-a-rich-vein-of-data-218722\/","title":{"rendered":"Millions of tweets are a gold mine for data mining"},"content":{"rendered":"

Computer scientist Henry Kautz likens Twitter to a kind of distributed sensor network. Hundreds of millions of tweets are posted to the platform each day, with each user observing and reporting on some aspect of the world.<\/p>\n

\"illustration<\/a><\/p>\n

Unlocking big data<\/h2>\n

 <\/p>\n

A Newscenter series on how Rochester is using data science to change how we research, how we learn, and how we understand our world.<\/h3>\n

 <\/p>\n

\u00a0\u201cEach report is very noisy,\u201d says the Robin and Tim Wentworth Director of the Goergen Institute for Data Science at the University of Rochester. \u201cBut the aggregate results can be reliable.\u201d<\/p>\n

Those results can provide information to meet all kinds of challenges\u2013from public concerns regarding health, safety, and the environment, to private ones regarding client and customer satisfaction and changing consumer tastes.<\/p>\n

Tracking sickness and disease<\/h3>\n

Kautz and his team have used Twitter to reliably identify and track people with symptoms of flu<\/a><\/strong>\u00a0and food poisoning<\/a><\/strong>, enabling health officials to respond much more quickly to disease outbreaks, and even to forecast when and if a specific individual will fall ill.<\/p>\n

The Las Vegas Health Department field tested the nEmesis app developed by Kautz and his team to connect food-poisoning-related tweets to restaurants<\/a><\/strong>.\u00a0The researchers found that the tweet-based system led to citations for health violations in 15 percent of inspections, compared to 9 percent using the traditional random system. That resulted in an estimated 9,000 fewer food poisoning incidents and 557 fewer hospitalizations during the course of the study.<\/p>\n

Increasing business transparency<\/h3>\n

Huaxia Rui is a big believer in transparency. That\u2019s why the assistant professor at the Simon Business School uses data science to delve deeply into Twitter\u2013one of the most transparent of our social media\u2013 to study the relationships between companies and their customers.<\/p>\n

Rui studies how companies respond to tweets, with the goal of increasing transparency about customer satisfaction across multiple industries.<\/p>\n

\"airplane<\/p>\n

For example, working with Simon professor Abraham Seidmann and PhD student Priyanga Gunarathne, Rui analyzed more than 450,000 Twitter messages to and from three major airlines. The researchers found that all three airlines were more likely to respond to tweets sent by customers with a higher number of followers<\/a><\/strong>. The study raises interesting questions about fairness, but also concedes that airlines \u201cmay have limited resources to handle all requests for engagement.\u201d<\/p>\n

\u201cWhen you call an airline or any company to complain about it, only you and the company know about it,\u201d Rui says. \u201cIf you\u2019re unhappy, what can you do? File a lawsuit? Most people won\u2019t do that.\u201d<\/p>\n

Twitter postings, on the other hand, are instantly public. \u201cIn general its a good idea to improve the sharing of this data, and increase the transparency, so that people can see in real time what companies are doing and whether their customers are happy,\u201d he says.<\/p>\n

Allocating resources<\/h3>\n

Rui has also found ways in which Twitter data might help both businesses and consumers operate more efficiently.<\/p>\n

In one of his first studies involving the social media platform, Rui and two fellow researchers analyzed the impact of four million tweets on box office sales for 63 movies. So-called \u201cintention tweets\u201d\u2013from people who hadn\u2019t seen the movies, but indicated they wanted to\u2013appeared to have a greater effect on box office sales than \u201cpositive tweets\u201d from people who had actually seen the movies. Why? Rui cites the dual effect of intention tweets: They are a clear indication that their authors intend to see a movie, and also make their followers aware of the movie, possibly influencing them to see it as well.<\/p>\n

How might a savvy business use this kind of information? Imagine you\u2019re the manager of a retail store and it\u2019s two weeks before Black Friday. If you\u2019re scanning Twitter, and detect a surge in \u201cintention\u201d tweets showing an interest in one of your products, \u201cThat could be useful for determining your staffing and inventory,\u201d Rui notes.<\/p>\n

Geotagged tweets could narrow such staffing and inventory decisions to single regions, even individual stores.<\/p>\n

The benefit for consumers? They may be less likely to find long lines or empty shelves on Black Friday if their local stores have done their Twitter \u201chomework\u201d in advance.<\/p>\n

Taking the pulse of the voters<\/h3>\n

Jiebo Luo, associate professor of computer science, PhD students Yu Wang, and their colleagues tracked the Twitter followers of Donald Trump, Hillary Clinton, Bernie Sanders and other candidates <\/a><\/strong>last year to better understand the dynamics of the 2016 presidential campaign.<\/p>\n

\u201cWe wanted to understand how each of the candidate\u2019s campaigns evolved, and be able to explain why someone won or lost,\u201d says Luo, an associate professor of computer science.<\/p>\n

Though the researchers did not set out to predict who would win the recent presidential election, their exhaustive, 14-month study of each candidate\u2019s Twitter followers\u2013enabled by machine learning and other data science tools\u2013offers tantalizing clues as to why the race turned out the way it did.<\/p>\n

In another study with potential political applications, Luo and his students used images extracted from Twitter to train computers to determine what sentiments are likely to be elicited by images<\/a><\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Researchers can track the flu, consumer preferences, and movie box office sales, all from the millions of tweets posted every day.<\/p>\n","protected":false},"author":286,"featured_media":171202,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[116],"tags":[41372,11716,18802,25592,18632,15512,24202,10406],"class_list":["post-218722","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sci-tech","tag-big-data-2017","tag-data-science","tag-department-of-computer-science","tag-goergen-institute-for-data-science-and-artificial-intelligence","tag-hajim-school-of-engineering-and-applied-sciences","tag-henry-kautz","tag-jiebo-luo","tag-simon-business-school"],"acf":[],"yoast_head":"\nMillions of tweets are a gold mine for data mining<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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