Table 2.

User-level performance (%) using different features.

Featuresa	Accuracy	F1	AUCb
VADERc	54.9	61.7	54.6
Demographics	58.7	56.0	61.4
Engagement	58.7	62.3	61.7
Personality	64.8	67.8	72.4
LIWCd	70.6	70.8	76.0
V + D + E + P + Le	71.5	72.0	78.3
XLNet	78.1	77.9	84.9
All (random forest)	78.4	78.1	84.9
All (logistic regression)	78.3	78.5	86.4 f
All (SVMg)	78.9	79.2	86.1

^aWe used SVM for classifying individual features.

^bAUC: area under the receiver operating characteristic curve.

^cVADER: Valence Aware Dictionary and Sentiment Reasoner.

^dLIWC: Linguistic Inquiry and Word Count.

^eV + D + E + P + L: VADER + demographics + engagement + personality + LIWC.

^fItalics indicate the best performing model in each column.

^gSVM: support vector machine.