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Twitter posts reveal polarization in Congress on COVID-19

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The fast politicization of the COVID-19 pandemic could be seen in messages members of the U.S. Congress despatched in regards to the difficulty on the social media web site Twitter, a brand new evaluation discovered.

Utilizing synthetic intelligence and assets from the Ohio Supercomputer Heart, researchers performed an evaluation that coated all 30,887 tweets that members despatched about COVID-19 from the primary one on Jan. 17 by March 31.

The algorithm they created might appropriately classify the political get together of the member who despatched every tweet 76 % of the time, primarily based solely on the textual content of the tweet and the date it was despatched.

“We discovered that after the events began to determine the political implications of the difficulty, polarization was evident within the tweets fairly rapidly,” mentioned Jon Inexperienced, co-author of the examine and doctoral scholar in political science at The Ohio State College.

The examine was printed in the present day (June 24, 2020) within the journal Science Advances.

“It’s outstanding that we might establish partisanship even when members have solely 280 characters to ship their messages in Twitter,” mentioned examine co-author Skyler Cranmer, the Carter Phillips and Sue Henry Professor of Political Science at Ohio State.

Democrats despatched out considerably extra tweets (19,803) about COVID-19 than did Republicans (11,084), the examine confirmed.

The hole within the variety of tweets despatched by Democratic versus Republican politicians widened after the primary case of neighborhood unfold was recognized in California and grew additional following the declaration of a nationwide emergency.

“This implies Democratic members have been sending earlier and stronger alerts to their constituents that they need to be involved in regards to the disaster,” Cranmer mentioned.

What Democrats and Republicans tweeted about regarding the pandemic was totally different, too, outcomes confirmed.

For instance, the phrase “well being” was utilized in 26 % of Democratic tweets, however solely 15 % of Republican tweets.

General, Democrats have been extra more likely to focus on public well being and security, in addition to American staff, whereas Republicans emphasised a basic want for nationwide unity, mentioned China and enterprise, and framed the pandemic as a warfare.

As one part of the evaluation, the researchers recognized members who fell within the vary of what they known as “partisan overlap.” Congressional members on this overlap space have been these whose tweets have been extra more likely to be confused by the algorithm with these of somebody from the opposite get together.

Solely 31 % of members fell on this space.

“Which means for 69 % of members, their tweets are extra partisan than essentially the most comparable member of the opposite get together,” Inexperienced mentioned.

Polarization was not fixed over time.

Within the first full week after the primary point out of COVID-19, the algorithm developed by the researchers had comparatively low accuracy when attempting to find out whether or not a Democrat or a Republican wrote a specific tweet. That signifies there was little polarization.

Nevertheless, polarization rapidly rose, peaking throughout the week starting Feb. 9. It then declined barely in early-to-mid March earlier than rising once more in late March because the events debated financial reduction packages.

The findings counsel that Congress missed a chance early within the pandemic to develop a consensus that might have helped america reply to the disaster, Cranmer mentioned.

“One thing on the size of COVID-19 requires a large-scale authorities response. The federal government can reply significantly better when it’s united in its mission,” he mentioned.

Inexperienced and Cranmer, co-authored the examine with their Ohio State political science colleagues Jared Edgerton and Daniel Naftel, together with Kelsey Shoub of the College of South Carolina.

Story Supply:

Materials supplied by Ohio State University. Unique written by Jeff Grabmeier. Notice: Content material could also be edited for type and size.


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