Combining Humor and Sarcasm for Improving Political Parody Detection (2022)

Abstract: Parody is a figurative device used for mimicking entities for comedic or critical purposes. Parody is intentionally humorous and often involves sarcasm. This paper explores jointly modelling these figurative tropes with the goal of improving performance of political parody detection in tweets. To this end, we present a multi-encoder model that combines three parallel encoders to enrich parody-specific representations with humor and sarcasm information. Experiments on a publicly available data set of political parody tweets demonstrate that our approach outperforms previous state-of-the-art methods.

## introduction parody is a figurative device which imitates entities such as politicians and celebrities by copying their particular style or a situation where the entity was involved @xcite . it is an intrinsic part of social media as a relatively new comedic form @xcite . a very popular type of parody is political parody, which is used to express political opposition and civic engagement @xcite . one of the hallmarks of parody expression is the deployment of other figurative devices, such as humor and sarcasm, as emphasized on studies of parody in linguistics @xcite . for example, in their method for automatic recognition of posts shared by political parody accounts on twitter is solely based on vanilla transformer models. in this paper, we hypothesize that humor and sarcasm information could guide parody specific text encoders towards detecting nuances of figurative language. for this purpose, we propose a multi-encoder model ( §2) consisting of three parallel encoders that are subsequently fused for parody classification. the first encoder learns parody specific information subsequently enhanced using the representations learned by a humor and sarcasm encoder respectively. our contributions are: (1) new state-of-the-art results on political parody detection in twitter, consistently improving predictive performance over previous work by @xcite ; and ## multi-encoder model for political parody prediction maronikolakis et al. (2020) define political parody prediction as a binary classification task where a social media post t , consisting of a sequence of tokens @xmath0, ..., t n }, is classified as real or parody. real posts have been authored by actual politicians (e.g., realdonaldtrump) while parody posts come from their corresponding parody accounts (e.g., realdonaldtrfan). parody tends to express complex tangled semantics of both humor and sarcasm simultaneously @xcite . to better exploit this characteristic of parody, we propose a multi-encoder model that consists of three parallel encoders, a feature-fusion layer and a parody classification layer depicted in fig. 1 . 4 . 4 early experiments with multi-task learning did not result in improved performance. the results of these experiments can be found in appendix a. ## error analysis finally, we perform an error analysis to examine the behavior and limitations of our best-performing model (multi-encoder with self-attention). the next two examples correspond to real tweets that were misclassified as parody: we speculate that the model misclassified these tweets as parody because they contain terms that the following two examples correspond to parody tweets that were misclassified as real: to recognise. as a result, in addition to humor and sarcasm semantics, the model might be improved by integrating knowledge from the political domain such as from political speeches. ## conclusion in this paper, we studied the impact of jointly modelling figurative devices to improve predictive performance of political parody detection in tweets. our motivation was based on studies in linguistics which emphasize the humorous and sarcastic components of parody @xcite . we presented a method that combines parallel encoders to capture parody, humor, and sarcasm specific representations from input sequences, which outperforms previous state-of-the-art proposed by @xcite . in the future, we plan to combine information from other modalities (e.g., images) for improving parody detection @xcite .