Mixed negative binomial regression models predicting the monthly number of words belonging to the LIWC dictionary “Home”.

Note. CI = confidence interval; ICC = intraclass correlation coefficient; LIWC = Linguistic Inquiry and Word Count; uid = user id; wc = word count. (DOCX)</p

Mean monthly LIWC scores for tweets from New York during 2020.

Note. NegEmo = Negative emotion; PosEmo = positive emotion; Linguistic Inquiry and Word Count (LIWC) scores represent percentages of total in-category words within a given text. (DOCX)</p

Mixed negative binomial regression models predicting the monthly number of words belonging to the LIWC dictionary “Anger”.

Note. CI = confidence interval; ICC = intraclass correlation coefficient; LIWC = Linguistic Inquiry and Word Count; uid = user id; wc = word count. (DOCX)</p

Fig 2 -

a Estimated monthly means of in-category word count for each LIWC category for an average user. Note. Orange dotted lines show the beginnings of the first (London and New York) and second (London only) lockdown. Estimates are based on mixed negative binomial regression models. The natural log of total word count was fixed to each city’s 2020 mean. Error bars denote 95% confidence intervals. b Estimated monthly means of in-category word count for each LIWC category for an average user. Note. Orange dotted lines show the beginnings of the first (London and New York) and second (London only) lockdown. Estimates are based on mixed negative binomial regression models. The natural log of total word count was fixed to each city’s 2020 mean. Error bars denote 95% confidence intervals.</p

Mixed negative binomial regression models predicting the monthly number of words belonging to the LIWC dictionary “Anxiety”.

Note. CI = confidence interval; ICC = intraclass correlation coefficient; LIWC = Linguistic Inquiry and Word Count; uid = user id; wc = word count. (DOCX)</p

Testing for fluctuations over the different months within a year, based on chi-square tests between the models with and without the factor month for New York.

Note. AIC = Akaike information criterion; BIC = Bayesian information criterion. (DOCX)</p

Equally weighted 29-day moving averages of VADER scores.

Note. Orange dotted lines show the beginnings of the first (London and New York) and second (London only) lockdown; neg = negative; pos = positive; neu = neutral; VADER = Valence Aware Dictionary for Sentiment Reasoning. (DOCX)</p

Monthly LIWC scores with bootstrapped 95% confidence intervals.

Note. Orange dotted lines show the beginnings of the first (London and New York) and second (London only) lockdown; purple dotted lines show the Linguistic Inquiry and Word Count (LIWC) reference values for tweets; LIWC scores represent percentages of total in-category words; monthly means are from the original sample; error bars denote bootstrapped 95% confidence intervals aggregated from 10,000 iterations. (DOCX)</p

Testing for fluctuations over the different months within a year, based on chi-square tests between the models with and without the factor month for London.

Note. AIC = Akaike information criterion; BIC = Bayesian information criterion. (DOCX)</p

Mean monthly LIWC scores for tweets from New York during 2019.

Note. NegEmo = Negative emotion; PosEmo = positive emotion; Linguistic Inquiry and Word Count (LIWC) scores represent percentages of total in-category words within a given text. (DOCX)</p