Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog

Effective models of social dialog must understand a broad range of rhetorical and figurative devices. Rhetorical questions (RQs) are a type of figurative language whose aim is to achieve a pragmatic goal, such as structuring an argument, being persuasive, emphasizing a point, or being ironic. While there are computational models for other forms of figurative language, rhetorical questions have received little attention to date. We expand a small dataset from previous work, presenting a corpus of 10,270 RQs from debate forums and Twitter that represent different discourse functions. We show that we can clearly distinguish between RQs and sincere questions (0.76 F1). We then show that RQs can be used both sarcastically and non-sarcastically, observing that non-sarcastic (other) uses of RQs are frequently argumentative in forums, and persuasive in tweets. We present experiments to distinguish between these uses of RQs using SVM and LSTM models that represent linguistic features and post-level context, achieving results as high as 0.76 F1 for “sarcastic” and 0.77 F1 for “other” in forums, and 0.83 F1 for both “sarcastic” and “other” in tweets. We supplement our quantitative experiments with an in-depth characterization of the linguistic variation in RQs.

[1]  M. Heesacker,et al.  Effects of rhetorical questions on persuasion: A cognitive response analysis. , 1981 .

[2]  J. Frank You Call That a Rhetorical Question? Forms and Functions of Rhetorical Questions in Conversation , 1990 .

[3]  R. Kreuz,et al.  Why Do People Use Figurative Language? , 1994 .

[4]  C. Ilie What else can I tell you?: A pragmatic study of English rhetorical questions as discursive and argumentative acts , 1994 .

[5]  R. Gibbs Irony in Talk Among Friends , 2000 .

[6]  Chung-hye Han Interpreting interrogatives as rhetorical questions , 2002 .

[7]  R. Carter,et al.  “There's millions of them”: hyperbole in everyday conversation , 2004 .

[8]  D. Schaffer Can rhetorical questions function as retorts?: Is the Pope Catholic? , 2005 .

[9]  Chung-hye Han Deriving the Interpretation of Rhetorical Questions , 2005 .

[10]  H. Rohde Rhetorical questions as redundant interrogatives , 2006 .

[11]  Yanfen Hao,et al.  Learning to Understand Figurative Language: From Similes to Metaphors to Irony , 2007 .

[12]  Ari Rappoport,et al.  Semi-Supervised Recognition of Sarcasm in Twitter and Amazon , 2010, CoNLL.

[13]  Ari Rappoport,et al.  ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews , 2010, ICWSM.

[14]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[15]  Nina Wacholder,et al.  Identifying Sarcasm in Twitter: A Closer Look , 2011, ACL.

[16]  Davide Buscaldi,et al.  From humor recognition to irony detection: The figurative language of social media , 2012, Data Knowl. Eng..

[17]  Elena Filatova,et al.  Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing , 2012, LREC.

[18]  Antal van den Bosch,et al.  The perfect solution for detecting sarcasm in tweets #not , 2013, WASSA@NAACL-HLT.

[19]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[20]  Ellen Riloff,et al.  Sarcasm as Contrast between a Positive Sentiment and Negative Situation , 2013, EMNLP.

[21]  Marilyn A. Walker,et al.  Getting Reliable Annotations for Sarcasm in Online Dialogues , 2014, LREC.

[22]  David Bamman,et al.  Contextualized Sarcasm Detection on Twitter , 2015, ICWSM.

[23]  Marilyn A. Walker,et al.  Learning to Recognize Affective Polarity in Similes , 2015, EMNLP.

[24]  Marilyn A. Walker,et al.  And That’s A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue , 2015, ArgMining@HLT-NAACL.

[25]  Joonsuk Park,et al.  Automatic Identification of Rhetorical Questions , 2015, ACL.

[26]  Wesley De Neve,et al.  Multimedia Lab @ ACL WNUT NER Shared Task: Named Entity Recognition for Twitter Microposts using Distributed Word Representations , 2015, NUT@IJCNLP.

[27]  Pushpak Bhattacharyya,et al.  Harnessing Context Incongruity for Sarcasm Detection , 2015, ACL.

[28]  Brian Ecker,et al.  Internet Argument Corpus 2.0: An SQL schema for Dialogic Social Media and the Corpora to go with it , 2016, LREC.

[29]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[30]  Byron C. Wallace,et al.  Modelling Context with User Embeddings for Sarcasm Detection in Social Media , 2016, CoNLL.

[31]  Yue Zhang,et al.  Tweet Sarcasm Detection Using Deep Neural Network , 2016, COLING.

[32]  Erik Cambria,et al.  A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks , 2016, COLING.

[33]  Marilyn A. Walker,et al.  Automatically Inferring Implicit Properties in Similes , 2016, HLT-NAACL.

[34]  Marilyn A. Walker,et al.  Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue , 2016, SIGDIAL Conference.

[35]  Tony Veale,et al.  Fracking Sarcasm using Neural Network , 2016, WASSA@NAACL-HLT.