Analyzing Political Parody in Social Media (2020)

Abstract: Parody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in social media through many popular parody accounts. In this paper, we present the first computational study of parody. We introduce a new publicly available data set of tweets from real politicians and their corresponding parody accounts. We run a battery of supervised machine learning models for automatically detecting parody tweets with an emphasis on robustness by testing on tweets from accounts unseen in training, across different genders and across countries. Our results show that political parody tweets can be predicted with an accuracy up to 90%. Finally, we identify the markers of parody through a linguistic analysis. Beyond research in linguistics and political communication, accurately and automatically detecting parody is important to improving fact checking for journalists and analytics such as sentiment analysis through filtering out parodical utterances.

## introduction parody is a figurative device which is used to imitate and ridicule a particular target @xcite and has been studied in linguistics as a figurative trope distinct to irony and satire @xcite . traditional forms of parody include editorial cartoons, sketches or articles pretending to have been authored by the parodied person. 2 a new form of parody recently emerged in social media, and twitter in particular, through accounts that impersonate public figures. @xcite defines parody accounts acting as: a known, real person, for obviously comedic purposes. there should be no risk of mistaking their tweets for their subject's actual views; these accounts play with stereotypes of these figures or juxtapose their public image with a very different, behind-closed-doors persona. a very popular type of parody is political parody which plays an important role in public speech by offering irreverent interpretations of political personas @xcite . while closely related figurative devices such as irony and sarcasm have been extensively studied in computational linguistics @xcite , parody yet to be explored using computational methods. in this paper, we aim to bridge this gap and conduct, for the first time, a systematic study of political parody as a figurative device in social media. to this end, we make the following contributions: 1. a novel classification task where we seek to automatically classify real and parody tweets. for this task, we create a new large-scale publicly available data set containing a total of 131,666 english tweets from 184 parody accounts and corresponding real accounts of politicians from the us, uk and other countries (section 3); 2. experiments with feature-and neural-based machine learning models for parody detection, which achieve high predictive accuracy of up to 89.7% f1. these are focused on the robust-ness of classification, with test data from: a) users; b) genders; c) locations; unseen in training (section 5); 3. linguistic analysis of the markers of parody tweets and of the model errors (section 6). we argue that understanding the expression and use of parody in natural language and automatically identifying it are important to applications in computational social science and beyond. parody tweets can often be misinterpreted as facts even though twitter only allows parody accounts if they are explicitly marked as parody foot_0 and the poster does not have the intention to mislead. for example, the speaker of the us house of representatives, nancy pelosi, falsely cited a michael flynn parody tweet; foot_1 and many users were fooled by a donald trump parody tweet about 'dow joans'. 5 thus, accurate parody classification methods can be useful in downstream nlp applications such as automatic fact checking @xcite and rumour verification @xcite , sentiment analysis @xcite or nowcasting voting intention @xcite @xcite . beyond nlp, parody detection can be used in: (i) political communication, to study and understand the effects of political parody in the public speech on a large scale @xcite ; (ii) linguistics, to identify characteristics of figurative language @xcite @xcite ; (iii) network science, to identify the adoption and diffusion mechanisms of parody @xcite . ## task & data we define parody detection in social media as a binary classification task performed at the social media post level. given a post t , defined as a sequence of tokens @xmath0, ..., t n }, the aim is to label t either as parody or genuine. note that one could use social network information but this is out of the paper's scope as we only focus on parody as a linguistic device. we create a new publicly available data set to study this task, as no other data set is available. we perform our analysis on a set of users from the same domain (politics) to limit variations caused by topic. we first identify real and parody accounts of politicians on twitter posting in english from the united states of america (us), the united kingdom (uk) and other accounts posting in english from the rest of the world. we opted to use twitter because it is arguably the most popular platform for politicians to interact with the public or with other politicians @xcite . for example, 67% of prospective parliamentary candidates for the 2019 uk general election have an active twitter account. 6 twitter also allows to maintain parody accounts, subject to adding explicit markers in both the user bio and handle such as parody, fake. 7 finally, we label tweets as parody or real, depending on the type of account they were posted from. we highlight that we are not using user description or handle names in prediction, as this would make the task trivial. ## predictive models we experiment with a series of approaches to classification of parody tweets, ranging from linear models, neural network architectures and pretrained contextual embedding models. hyperparameter selection is included in the appendix. ## results this section contains the experimental results obtained on all three different data splits proposed in section 3. we evaluate our methods (section 4) using several metrics, including accuracy, precision, recall, macro f1 score, and area under the roc (auc). we report results over three runs using different random seeds and we report the average and standard deviation. ## analysis we finally perform an analysis based on our novel data set to uncover the peculiarities of political parody and understand the limits of the predictive models. ## conclusion we presented the first study of parody using methods from computational linguistics and machine learning, a related but distinct linguistic phenomenon to irony and sarcasm. focusing on political parody in social media, we introduced a freely available large-scale data set containing a total of 131,666 english tweets from 184 real and corresponding parody accounts. we defined parody prediction as a new binary classification task at a tweet level and evaluated a battery of featurebased and neural models achieving high predictive accuracy of up to 89.7% f1 on tweets from people unseen in training. in the future, we plan to study more in depth the stylistic and figurative devices used for parody, extend the data set beyond the political case study and explore human behavior regarding parody, including how this is detected and diffused through social media.