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A Multitask-Learning
We also tested applying multi-task learning approaches (Caruana, 1993) to use either sarcasm prediction  $(\mathrm{P} + \mathrm{S})$ , humor prediction  $(\mathrm{P} + \mathrm{H})$  or both  $(\mathrm{P} + \mathrm{S} + \mathrm{H})$  as auxiliary tasks for parody detection. We utilize BERTweet as the share encoder and independent classification layers for parody and humor or sarcasm. Three sets of weights are applied to losses from each independent classification layer and the three layers are stacked. The best results are chosen and depicted in Table 8, Table 9 and Table 10.
Person
Model	F1
Single-Encoder
BERTweet (P)	90.72 ± 0.31
Multi-Task
P+S+H	87.46 ± 0.18
P+S	89.41 ± 0.31
P+H	87.41 ± 0.38
Table 8: F1-scores for parody detection on the Person Split using Multi-task Learning models (P: Parody, S: Sarcasm, H: Humor). Best results are in bold.
Gender
Model	M→F	F→M
Single-Encoder
BERTweet (P)	88.01 ± 0.29	85.57 ± 0.27
Multi-Task
P+S+H	85.28 ± 0.29	84.10 ± 0.37
P+S	88.13 ± 0.21	86.07 ± 0.44
P+H	84.53 ± 0.31	86.07 ± 0.47
Table 9: F1-scores on the Gender Split using Multi-task Learning models (P: Parody, S: Sarcasm, H: Humor). Best results are in bold.
Location
Model	UK+US → RoW	RoW+US → UK	RoW+UK → US
Single-Encoder
BERTweet (P)	88.21 ± 0.26	87.85 ± 0.24	87.18 ± 0.41
Multi-Task
P+S+H	86.41 ± 0.17	86.23 ± 0.20	85.13 ± 0.29
P+S	87.74 ± 0.36	87.26 ± 0.34	86.67 ± 0.43
P+H	85.54 ± 0.38	84.78 ± 0.47	84.15 ± 0.56
Table 10: F1-scores on the Location Split using Multi-task Learning models (P: Parody, S: Sarcasm, H: Humor). Best results are in bold.