{"id":100857,"date":"2020-10-29T23:37:50","date_gmt":"2020-10-29T20:37:50","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/researcher-uses-computer-vision-to-determine-which-politicians-instagram-posts-resonate-most\/"},"modified":"2020-10-29T23:37:50","modified_gmt":"2020-10-29T20:37:50","slug":"researcher-uses-computer-vision-to-determine-which-politicians-instagram-posts-resonate-most","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/researcher-uses-computer-vision-to-determine-which-politicians-instagram-posts-resonate-most\/","title":{"rendered":"#Researcher uses computer vision to determine which politicians’ Instagram posts resonate most"},"content":{"rendered":"

“#Researcher uses computer vision to determine which politicians’ Instagram posts resonate most<\/strong>”<\/p>\n

\n
\n
\n
\"politicians\"
\n Credit: CC0 Public Domain
\n <\/figcaption><\/figure>\n<\/div>\n<\/div>\n

As the country enters the final days of a marathon and polarizing election season, politicians’ faces are everywhere. And that trend is not likely going anywhere, especially in the realm of social media<\/a>.<\/p>\n


\n <\/section>\n

A University of Georgia researcher used computer vision to analyze thousands of images from over 100 Instagram accounts of United States politicians and discovered posts that showed politicians’ faces in nonpolitical settings increased audience engagement over traditional posts such as politicians in professional or political settings.<\/p>\n

“Faces are a very important vehicle to extracting emotions,” said Yilang Peng, an assistant professor in the UGA College of Family and Consumer Science<\/a>s. “In general<\/a>, people are paying attention to posts that feature faces, and especially faces of people they recognize.”<\/p>\n

Using computer vision methods, Peng analyzed over 59,000 images published on Instagram of 159 U.S. politicians sampled in fall 2018, including presidential candidates, governors, senators and Cabinet members.<\/p>\n

“Computer vision is an emerging field that looks at how we can train computers to imitate human vision, like detecting objects,” Peng said.<\/p>\n

The technique has been app<\/a>lied to facial recognition software, self-driving cars and even in medical imaging, whereby a computer can extract and identify common features from thousands or millions of images.<\/p>\n

Using this method, visual messages can be described both in terms of content as well as aesthetic appeal, including brightness, blur, color, composition and even facial expressions, Peng said.<\/p>\n

Peng identified four broad categories to classify the setting of the politicians’ images:<\/p>\n