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. 2022 Dec 8;2(2):e37331. doi: 10.2196/37331

Table 2.

Descriptions of all variables.

Variable Description
RT7D # of retweets in the first 168 hours
preprint =1 if the tweet source is a preprint article
peer =1 if the tweet source is a peer-reviewed article
letter =1 if the tweet source is a journal opinion/letter piece
scientist =1 if the user is classified as a doctor or researcher in the life science and biomedical fields
liwc_positive # of positive emotion dictionary words identified by LIWCa 2015
liwc_negative # of negative emotion dictionary words identified by LIWC 2015
emotion: joy =1 if the tweet text is predicted to have a salient emotion of joy
emotion: anger =1 if the tweet text is predicted to have a salient emotion of anger
emotion: fear =1 if the tweet text is predicted to have a salient emotion of fear
emotion: sadness =1 if the tweet text is predicted to have a salient emotion of sadness
emotion: neutral =1 if the tweet text is predicted to have no specific emotion
log_follower (log) number of followers the user had
verified =1 if the user is a verified user
length # of words in the tweet text
hashtags # of hashtags used in the tweet
mention =1 if the tweet contains any mention of other users
title_length # of words in the reference article in preprints or journal
title_liwc_pos # of positive emotion words in the title identified by LIWC 2015
title_liwc_neg # of negative emotion words in the title identified by LIWC 2015
log_cov_tweet (log) rolling 7-day total number of global coronavirus tweets
log_cov_case (log) rolling 7-day total number of global new confirmed COVID cases
log_cov_fatality (log) rolling 7-day total number of global new confirmed COVID fatalities

aLIWC: Linguistic Inquiry and Word Count.