AI Case Study
The AP informs its news stories with analysis of Twitter users' engagement with the US president's tweets using machine learning
The AP used machine learning to classify Twitter users engaging with US President Donald Trump's tweets by political leaning, age, gender and location in order to determine how different segments reacted to the President in order to generate story content.
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The AP "collaborated with Cortico, a media analytics nonprofit recently launched from the Laboratory for Social Machines at the MIT Media Lab, to parse a data set of Trump’s posts from his first 100 days in office. We analyzed the level of attention the president gave to certain issues, as well as how Twitter users have responded.
Researchers at the Laboratory of Social Machines built a series of classifiers to categorize Twitter users by age, gender, political ideology and location. They did so by using a type of artificial intelligence called supervised learning — a process of extracting novel information from labeled input data. In this case, researchers labeled tweets as belonging to a certain demographic (for example, left- or right-leaning citizens) and allowed the AI to differentiate among the labels and develop its own rules to later classify new exemplars. Informed by those insights, AP visual journalist Maureen Linke was able to build upon the initial findings and contextualize the data through traditional journalistic research."
The Associated Press (AP) wanted to incorporate more data analytics into its journalism and determined that looking at the US President Donald Trump's tweets could yield insights into his changing policy stances.
"Our results showcased the benefits of using machine learning in reporting: The resulting story was picked up by more than 270 media outlets including The New York Times, Politico and The Washington Post, and featured as a must-read by industry think tanks such as Nieman Lab and NYC Media Lab."
Labelled data (tweets)