New @Journal_Of_Comm w/@jinwoo_kim01 @andyguess @JasonReifler on how self-selection & exposure to incivility fuel online comment toxicity
academic.oup.com/joc/advance-ar…
-Commenters very polarized
-FB comment toxicity >> comments from nat rep sample
-Toxicity → likes, more toxicity
🧵
Key finding 1 (academic.oup.com/joc/advance-ar…):
Consistent w/@boralexander1 @M_B_Petersen finding that online hostility reflects differences in who participates in political discussion online (cambridge.org/core/journals/…), we show commenters are unusually politically engaged & polarized.
Key finding 2 (academic.oup.com/joc/advance-ar…):
Prior research has no baseline for assessing online comments so we compared FB comments w/comments we elicited from a public sample on those articles. Real-world comments and those from commenters were more toxic (using Perspective API).
Key finding 3 (academic.oup.com/joc/advance-ar…):
Toxicity can spread:
-More toxic comments get more likes (except in most extreme cases), which can increase algorithmic exposure
-Randomly exposing participants to more toxic comments leads them to write more toxic comments themselves
PS For some reason, my PDF program was copying out fuzzy graphics, so here's the first tweet again with sharp graphics if you want something cleaner to RT!
New @Journal_Of_Comm w/@jinwoo_kim01 @andyguess @JasonReifler on how self-selection & exposure to incivility fuel online comment toxicity
academic.oup.com/joc/advance-ar…
-Commenters very polarized
-FB comment toxicity >> comments from nat rep sample
-Toxicity → likes, more toxicity
🧵
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