Does negatively toned language use on social media lead to attitude polarization?
作者：
Highlights：
• Research on attitude polarization has typically focused on content or congeniality.
• This paper looks at language sentiment as a precursor of polarization.
• 4 million tweets on two controversial topics were examined via sentiment analysis.
• Negative sentiment of a person's own tweets increased polarization.
• Negative sentiment of tweets from a person's friends reduced polarization.
摘要
•Research on attitude polarization has typically focused on content or congeniality.•This paper looks at language sentiment as a precursor of polarization.•4 million tweets on two controversial topics were examined via sentiment analysis.•Negative sentiment of a person's own tweets increased polarization.•Negative sentiment of tweets from a person's friends reduced polarization.
论文关键词：Echo chamber,Attitudes,Attitude strength,Social media,Sentiment analysis,Negativity bias
论文评审过程：Received 3 April 2020, Revised 2 December 2020, Accepted 7 December 2020, Available online 11 December 2020, Version of Record 16 December 2020.
论文官网地址：https://doi.org/10.1016/j.chb.2020.106663
