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Register a new userAnalyzing Political Parody in Social Media
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Added by
Antonios Maronikolakis
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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.
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Social media provides many opportunities to monitor and evaluate political phenomena such as referendums and elections. In this study, we propose a set of approaches to analyze long-running political events on social media with a real-world experiment: the debate about Brexit, i.e., the process through which the United Kingdom activated the option of leaving the European Union. We address the following research questions: Could Twitter-based stance classification be used to demonstrate public stance with respect to political events? What is the most efficient and comprehensive approach to measuring the impact of politicians on social media? Which of the polarized sides of the debate is more responsive to politician messages and the main issues of the Brexit process? What is the share of bot accounts in the Brexit discussion and which side are they for? By combining the user stance classification, topic discovery, sentiment analysis, and bot detection, we show that it is possible to obtain useful insights about political phenomena from social media data. We are able to detect relevant topics in the discussions, such as the demand for a new referendum, and to understand the position of social media users with respect to the different topics in the debate. Our comparative and temporal analysis of political accounts can detect the critical periods of the Brexit process and the impact they have on the debate.
Social media platforms attempting to curb abuse and misinformation have been accused of political bias. We deploy neutral social bots who start following different news sources on Twitter, and track them to probe distinct biases emerging from platform mechanisms versus user interactions. We find no strong or consistent evidence of political bias in the news feed. Despite this, the news and information to which U.S. Twitter users are exposed depend strongly on the political leaning of their early connections. The interactions of conservative accounts are skewed toward the right, whereas liberal accounts are exposed to moderate content shifting their experience toward the political center. Partisan accounts, especially conservative ones, tend to receive more followers and follow more automated accounts. Conservative accounts also find themselves in denser communities and are exposed to more low-credibility content.
We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify these aspects with existing lexical methods, and propose clustering of tweet embeddings as a means to identify salient topics for analysis across events; human evaluations show that our approach generates more cohesive topics than traditional LDA-based models. We apply our methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the discussion of these events is highly polarized politically and that this polarization is primarily driven by partisan differences in framing rather than topic choice. We identify framing devices, such as grounding and the contrasting use of the terms terrorist and crazy, that contribute to polarization. Results pertaining to topic choice, affect and illocutionary force suggest that Republicans focus more on the shooter and event-specific facts (news) while Democrats focus more on the victims and call for policy changes. Our work contributes to a deeper understanding of the way group divisions manifest in language and to computational methods for studying them.
Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraud---such as pump and dump scams---where the goal is to artificially inflate the perceived worth of a currency, luring victims into investing before the fraudsters can sell their holdings. Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump. In this work we propose and evaluate a computational approach that can automatically identify pump and dump scams as they unfold by combining information across social media platforms. We also develop a multi-modal approach for predicting whether a particular pump attempt will succeed or not. Finally, we analyze the prevalence of bots in cryptocurrency related tweets, and observe a significant increase in bot activity during the pump attempts.
Identifying political perspective in news media has become an important task due to the rapid growth of political commentary and the increasingly polarized ideologies. Previous approaches only focus on leveraging the semantic information and leaves out the rich social and political context that helps individuals understand political stances. In this paper, we propose a perspective detection method that incorporates external knowledge of real-world politics. Specifically, we construct a contemporary political knowledge graph with 1,071 entities and 10,703 triples. We then build a heterogeneous information network for each news document that jointly models article semantics and external knowledge in knowledge graphs. Finally, we apply gated relational graph convolutional networks and conduct political perspective detection as graph-level classification. Extensive experiments show that our method achieves the best performance and outperforms state-of-the-art methods by 5.49%. Numerous ablation studies further bear out the necessity of external knowledge and the effectiveness of our graph-based approach.
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