Rubin, V.R., Conroy, N. J., Chen, Y. & Cornwell, S. (2016) Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News. In the Pro- ceedings of the Workshop on Computational Approaches to Deception Detection at the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-CADD2016), San Diego, California, June 17, 2016. Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News.

Victoria L. Rubin, Niall J. Conroy, Yimin Chen, and Sarah Cornwell Language and Information Technology Research Lab (LIT.RL) Faculty of Information and Media Studies University of Western Ontario, London, Ontario, CANADA [email protected], [email protected], [email protected], [email protected]

Twitchell, 2004). The falsehoods are intentionally Abstract poorly concealed, and beg to be unveiled. Yet some readers simply miss the joke, and the fake news is Satire is an attractive subject in deception detec- further propagated, with often costly consequences tion research: it is a type of deception that inten- (Rubin, Chen, & Conroy, 2015). tionally incorporates cues revealing its own de- ceptiveness. Whereas other types of fabrications 1.1. The News Context aim to instill a false sense of truth in the reader, a In recent years, there has been a trend towards de- successful satirical hoax must eventually be ex- creasing confidence in the mainstream media. Ac- posed as a jest. This paper provides a conceptual overview of satire and humor, elaborating and il- cording to Gallup polls, only 40% of Americans lustrating the unique features of satirical news, trust their mass media sources to report the news which mimics the format and style of journalistic “fully, accurately and fairly” (Riffkin, 2015) and a reporting. Satirical news stories were carefully similar survey in the UK has shown that the most- matched and examined in contrast with their legit- read newspapers were also the least-trusted (Reilly imate news counterparts in 12 contemporary news & Nye, 2012). One effect of this trend has been to topics in 4 domains (civics, science, business, and drive news readers to rely more heavily on alterna- “soft” news). Building on previous work in satire tive information sources, including blogs and social detection, we proposed an SVM-based algorithm, media, as a means to escape the perceived bias and enriched with 5 predictive features (Absurdity, unreliability of mainstream news (Tsfati, 2010). Humor, Grammar, Negative Affect, and Punctua- Ironically, this may leave the readers even more sus- tion) and tested their combinations on 360 news ceptible to incomplete, false, or misleading infor- articles. Our best predicting feature combination (Absurdity, Grammar and Punctuation) detects mation (Mocanu, Rossi, Zhang, Karsai, & satirical news with a 90% precision and 84% re- Quattrociocchi, 2015). call (F-score=87%). Our work in algorithmically In general, humans are fairly ineffective at recog- identifying satirical news pieces can aid in mini- nizing deception (DePaulo, Charlton, Cooper, mizing the potential deceptive impact of satire. Lindsay, & Muhlenbruck, 1997; Rubin & Conroy, 2012; Vrij, Mann, & Leal, 2012). A number of fac- 1. Introduction tors may explain why. First, most people show an inherent truth-bias (Van Swol, 2014): they tend to In the course of news production, dissemination, assume that the information they receive is true and and consumption, there are ample opportunities to reliable. Second, some people seem to exhibit a deceive and be deceived. Direct falsifications such “general gullibility” (Pennycook, Cheyne, Barr, as journalistic fraud or social media hoaxes pose ob- Koehler, & Fugelsang, 2015) and are inordinately vious predicaments. While fake or satirical news receptive to ideas that they do not fully understand. may be less malicious, they may still mislead inat- Third, confirmation bias can cause people to simply tentive readers. Taken at face value, satirical news see only what they want to see – conservative view- can intentionally create a false belief in the readers’ ers of the news satire program the Colbert Report, minds, per classical definitions of deception (Buller for example, tend to believe that the comedian’s & Burgoon, 1996; Zhou, Burgoon, Nuna-maker, &

statements are sincere, while liberal viewers tend to rhetorical strategy (in any medium) that seeks wit- recognize the satirical elements (LaMarre, tily to provoke an emotional and intellectual reac- Landreville, & Beam, 2009). tion in an audience on a matter of public … significance” (Phiddian, 2013). 1.2. Problem Statement On the attack, satirical works use a variety of rhe- High rates of media consumption and low trust in torical devices, such as hyperbole, absurdity, and news institutions create an optimal environment for obscenity, in order to shock or unease readers. Tra- the “rapid viral spread of information that is either ditionally, satire has been divided into two main intentionally or unintentionally misleading or pro- styles: Juvenalian, the more overtly hostile of the vocative” (Howell, 2013). Journalists and other two, and Horatian, the more playful (Condren, content producers are incentivized towards speed 2014). Juvenalian satire is characterized by con- and spectacle over accuracy (Chen, Conroy, & tempt and sarcasm and an often aggressively pessi- Rubin, 2015) and content consumers often lack the mistic worldview (e.g., Swift’s A Modest Proposal). literacy skills required to interpret news critically Horatian satire, by contrast, tends more towards (Hango, 2014). What is needed for both content pro- teasing, mockery, and black humor (e.g., Kubrick’s ducers and consumers is an automated assistive tool Dr. Strangelove). In each, there is a mix of both that can save time and cognitive effort by flag- laughter and scorn – though Juvenalian tends to- ging/filtering inaccurate or false information. wards scorn, some hint of laughter must be present In developing such a tool, we have chosen news and vice versa for Horatian. satire as a starting point for the investigation of de- Satire must also serve a purpose beyond simple liberate deception in news. Unlike subtler forms of spectacle: it must aspire “to cure folly and to punish deception, satire may feature more obvious cues that evil” (Highet, 1972: 156). It is not enough to simply reveal its disassociation from truth because the ob- mock a target; some form of critique or call to action jective of satire is for at least some subset of readers is required. This “element of censoriousness” or to recognize the joke (Pfaff & Gibbs, 1997). And “ethically critical edge” (Condren, 2012: 378) sup- yet, articles from The Onion and other satirical news plies the social commentary that separates satire sources are often shared and even reprinted in news- from mere invective. However, the receptiveness of an audience to satire’s message depends upon a papers as if the stories were true1. In other words, level of “common agreement” (Frye, 1944: 76) be- satirical news may mislead readers who are unaware tween the writer and the reader that the target is wor- of the satirical nature of news, or lacking in the con- thy of both disapproval and ridicule. This is one way textual or cultural background to interpret the fake that satire may miss its mark with some readers: as such. In this paper, we examine the textual fea- they might recognize the satirist’s critique, but tures of satire and test for the most reliable cues in simply disagree with his or her position. differentiating satirical news from legitimate news. As a further confounding factor, satire does not speak its message plainly, and hides its critique in 2. Literature Review irony and double-meanings. Though satire aims to 2.1. Satire attack the folly and evil of others, it also serves to highlight the intelligence and wit of its author. Sat- As a concept, satire has been remarkably hard to pin ire makes use of opposing scripts, text that is com- down in the scholarly literature (Condren, 2012). patible with two different readings (Simpson, 2003: One framework for humor, proposed by Ziv (1988), 30), to achieve this effect. The incongruity between suggests five discrete categories of humor: aggres- the two scripts is part of what makes satire funny sive, sexual, social, defensive, and intellectual. Sat- (e.g., when Stephen Colbert, states “I give people ire, according to Simpson (2003), is complicated be- the truth, unfiltered by rational argument.”2), but cause it occupies more than one place in the frame- readers who fail to grasp the humor become, them- work: it clearly has an aggressive and social func- selves, part of the joke. In this way, we consider tion, and often expresses an intellectual aspect as satire a type of deception, but one that is intended to well. From this, satire can be conceptualized as “a be found out by at least some subset of the audience.

1 See: https://www.washingtonpost.com/news/worldviews/wp/2015/06/02/7-times-the-onion- 2 2006 White House Correspondents Dinner: https://youtu.be/2X93u3anTco was-lost-in-translation

Although “the author mostly assumes that readers Media Insight Project, 2014). Furthermore, on so- will recover the absurdity of the created text, which cial media platforms like Facebook and Twitter, sto- hopefully will prompt the readers to consider issues ries which are “liked” or “shared” all appear in a beyond the text” (Pfaff & Gibbs, 1997), what one common visual format. Unless a user looks specifi- reader considers absurd might be perfectly reasona- cally for the source attribution, an article from The ble to another. Onion looks just like an article from a credible In his Anatomy of Satire, Highet distinguishes source, like The New York Times. In an effort to satire from other forms of “lies and exaggerations counteract this trend, we propose the creation of an intended to deceive” (1972: 92). Whereas other automatic satire detection system. types of fabrications and swindles aim to instill a So, how can satirical news stories be identified? false sense of truth in the reader, which benefits the Ermida (2012: 194-195) proposes the model of pa- deceiver for as long as it remains undiscovered, a rodic news satire in Figure 1. successful satirical hoax must eventually be ex- posed. After all, an author of satire cannot be appre- ciated for his or her wit if no one recognizes the joke. This interpretation of satire is in line with Hop- per & Bell’s (1984) category of “benign fabrica- tions,” which include make believe, jokes, and teas- ing – types of deception that are generally both so- cially acceptable and fairly easy to uncover.

Figure 1: Ermida's (2012) model of satirical news. 2.2. Satire in News News satire is a genre of satire that mimics the for- For the purposes of this research, we focus on com- mat and style of journalistic reporting. The fake ponent III to inform our investigation into cues to news stories are typically inspired by real ones, and differentiate news satire from legitimate news re- cover the same range of subject matter: from politics porting. The next section overviews satirical news to weather to crime. The satirical aspect arises when detection efforts to date and positions them as ben- the factual basis of the story is “comically extended eficial in the deception detection research. to a fictitious construction where it becomes incon- gruous or even absurd, in a way that intersects en- 3. Detection Methodology Review tertainment with criticism” (Ermida, 2012: 187). The methods described in this review demonstrate News satire is most often presented in the Horatian promising results for satire and humor detection. style, where humor softens the impact of harshness The goal of screening legitimate news content is of the critique – the spoonful of sugar that helps the achieved based on the assumption that successful medicine go down. More than mere lampoon, fake identification of satire is independent from both the news stories aim to “arouse the readers’ attention, amuse them, and at the same time awaken their ca- originating source of the piece, and its provenance pacity to judge contemporary society” (Ermida, as a news document. Instead, Natural Language 2012: 188). Processing (NLP) methods in combination with ma- With the rise of the internet, news satire sites chine learning deal with content directly by detect- such as The Onion have become a prolific part of ing language patterns, topicality, sentiment, rhetor- the media ecosystem. Stories from satirical sources ical devices and word occurrences which are com- are frequently shared over social media, where they mon to satire and irony. There is a need for a unified deceive at least some of their readers. Indeed, peo- approach that combines best practices for a compre- ple are fooled often enough that internet sites such hensive NLP satire detection system. as Literally Unbelievable (Hongo, 2016) have 3.1. Word Level Features sprung up to document these instances. Several factors contribute to the believability of Burfoot & Baldwin (2009) attempted to determine fake news online. Recent polls have found that only whether or not newswire articles can be automati- 60% of Americans read beyond the headline (The cally classified as satirical. The approach relied on

lexical and semantic features such as headlines, pro- Rosso, 2014). In this study, we use this measure of fanity, or slang, as well as support vector machines absurdity as an indicator of the pragmatic compo- (SVMs) on simple bag-of-words features which nent (II.b) of satirical news (Ermida, 2012). were supplemented with feature weighting. Similar attempts have used corpus-based relatedness which 3.3. Humor and Opposing Scripts uses cosine similarity and tf*idf weighting. At the Sjöbergh and Araki (2007) presented a machine granularity of individual words and n-grams, text learning approach for classifying sentences as one- cues can point to the presence of satire, for example liner jokes or normal sentences. Instead of deep counterfactual words (“nevertheless”, “yet”), and analysis, they rely on weighting derived from a temporal compression words (“now”, “abruptly”) combination of simple features like word overlap (Mihalcea, Strapparava, & Pulman, 2010). As a with a joke corpus, and ambiguity (number of dic- base measure, tf*idf on bi-grams provided F-score tionary.com word senses), achieving an 85% accu- of roughly 65%. The use of additional features de- racy. The incongruity (resolution) theory is a theory rived from semantic analysis heuristics improved of comprehension which depends on the introduc- classifier performance on joke detection. Using tion of “latent terms”. Humor is found when two combined joke specific features led to 84% preci- scripts overlap and oppose. As a joke narration sion, demonstrating synergistic effect of feature evolves, some “latent” terms are gradually intro- combination. duced, which set the joke on a train of thought. Yet We hypothesize expanding the possibilities of because of ambiguous quality of the terms, the hu- word-level analysis by measuring the utility of fea- morous input advances on two or more interpreta- tures like part of speech frequency, and semantic tion paths. The interpretation shifts suddenly from categories such as generalizing terms, time/space the starting point of the initial sequence: references, positive and negative polarity. In addi- “Is the doctor at home?” the patient asked in a whisper. tion, we incorporate the use of exaggerated lan- “No”, the doctor’s pretty wife whispered back, “Come right guage (e.g., profanity, slang, grammar markers) and in.” (Attardo, Hempelmann, & Mano, 2002: 35) frequent run-on sentences. We take these features as The latter path gains more importance as elements indicators of satirical rhetoric component (III.c, per are added to the current interpretation of the reader, Ermida, 2012). and eventually ends up forming the punch line of the 3.2. Semantic Validity joke (Hempelmann, Raskin, & Triezenberg, 2006). To detect opposing scripts, Polanyi and Zaenen Satirical news contains a higher likelihood of imbal- (2006) suggest a theoretical framework in which the ances between concepts expressing temporal con- context of sentiment words shifts the valence of the sistency, as well as contextual imbalances (Reyes, expressed sentiment. Hempelmann et al. (2006) em- Rosso, & Buscaldi, 2012), for example, well known ployed Text Meaning Representations (TMRs) people in unexpected settings. A similar idea of un- which are data models aimed at preserving vague- expectedness was explored by Burfoot and Baldwin ness in language while using an ontology represen- (2009) who measured the presence of absurdity by tation from a fact repository, a pre-processor, and an defining a notion of “semantic validity”: true news analyzer to transform text. They propose an identi- stories found on the web will contain differences in fication method by checking the number of word the presence of co-occurring named entities than senses of the last word that “make sense” in the cur- those entities found in satire. Shallow and deep lin- rent context, although no performance evaluation is guistic layers may represent relevant information to provided. identify figurative uses of language. For example, Other means of joke classification rely on naive ontological semantics such as ambiguity and incon- Bayes and SVM based on joke-specific features, in- gruity, and meta-linguistic devices, such as polarity and emotional scenarios can achieve precision ac- cluding polysemy and Latent Semantic Analysis curacy of 80% for classifying humorous tweets (LSA) trained on joke data, as well as semantic re- (Reyes et al., 2012). Semantically disparate con- latedness. Mihalcea and Liu (2006) and Mihalcea cepts can also influence absurdity, when judged and Pulman (2007) presented such a system that re- based on semantic relatedness (distance in WordNet lies on metrics including knowledge-based similar- hierarchy), summed and normalized (Reyes &

ity (path between concepts) and corpus based simi- which symbolize abstractions such as overall senti- larity (term co-occurrence from large corpus). They ments and moods. Presence of humor may be corre- conclude that the most frequently observed seman- lated to polarity of positive/negative semantic ori- tic features are negative polarity and human-cen- entation and emotiveness. Using Twitter content, teredness. A correct punch line, which generates models of irony detection were assessed along these surprise, has a minimum relatedness with respect to linguistic characteristics, and positive results pro- the set-up. The highest overall precision of 84% was vide valuable insights into figurative speech in the obtained with models that rely on joke-specific fea- task of sentiment analysis (Reyes & Rosso, 2014; tures, with the LSA model trained on a jokes corpus Reyes, Rosso, & Veale, 2013). (Mihalcea et al., 2010). These methods informed our investigation of Ermida’s lexical component 4. Methodology (III.a) (2012). 4.1. Dataset and Data Collection Methods Past research in humor detection provides useful heuristics for NLP. A shortcoming is that best prac- In this study we collected and analyzed a dataset of tices have yet to be combined in a comprehensive 360 news articles as a wide-ranging and diverse data detection methodology. For example, punchline de- sample, representative of the scope of US and Ca- tection may be employed to longer, discourse layer nadian national newspapers. The dataset was col- content beyond mere one liners, through the com- lected in 2 sets. The first set was collected from 2 parison of constituent text segments. Absurdity, satirical news sites (The Onion and The Beaverton) measured through the presence of atypical named and 2 legitimate news sources (The Toronto Star entities, may be extended to other contexts. Our ex- and The New York Times) in 2015. The 240 articles amination of satire sources hints at the tendency to were aggregated by a 2 x 2 x 4 x 3 design (US/Ca- introduce new, unfamiliar named entities at the end nadian; satirical/legitimate online news; varying of news articles as a form of ironic non-sequitur. across 4 domains (civics, science, business, and “soft” news) with 3 distinct topics within each of the 3.4. Recognizing Sarcasm and Irony 4 domains (see Table 1).

Recognition of sarcasm can benefit many sentiment CIVICS SCIENCE BUSINESS “SOFT” analysis applications and the identification of fake NEWS news. Sarcasm is defined as “verbal irony … the ac- Gun Violence Environment Tech Celebrity tivity of saying or writing the opposite of what you Immigration Health Finance Sports mean” (Tsur, Davidov, & Rappoport, 2010). Elections Other Sci- Corporate An- Local News ences nouncements Utsumi (2000) introduced a cognitive computa- Table 1: Sample News Topicality. 5 Canadian and 5 Ameri- tional framework that models the ironic environ- can satirical and legitimate article pairs were collected on 12 ment from an axiomatic system depending heavily topics across 4 domains. on “world knowledge” and expectations. It requires analysis of each utterance and its context to match For each of the 12 topics, 5 Canadian (from The predicates in a specific logical formalism. Davidov, Beaverton) and 5 American (from the Onion) satir- Tsur, and Rappoport (2010) looked for elements to ical articles were collected. Each satirical piece was automatically detect sarcasm in online products re- then matched to a legitimate news article that was views, achieving a 77% precision and 83% recall. published in the same country, and as closely related They proposed surface features (information about in subject matter as possible. For example, in the the product, company, title, etc.), frequent words or Environment topic, the Beaverton article “Invasive punctuation marks, to represent sarcastic texts. homo sapiens species meet at forestry conference to Ironic expressions often use such markers to safely discuss pine beetles” was paired with a Toronto Star realize their communicative effects (e.g., ‘‘Trees article on invasive species: “'Dangerous and inva- died for this book?” - book review; “All the features you want. Too bad they don’t work!” - smart phone review). Beyond grammar and word polarity, emo- tional scenarios capture irony in terms of elements

sive' Khapra beetle intercepted at Pearson”. See Fig- sharply from the standard “inverted pyramid” arti- ure 2 for the pairing of the articles about Hillary cle structure of legitimate news articles, and it Clinton in the Elections topic. proved useful in identifying satirical journalism. These observations informed the selection of 2 features: Absurdity and Humor. For example, the fi-

nal line of The Beaverton’s “Scientists at University of the Lord discover that Jesus is Lord” introduces new named entities (indicating absurdity) and is se- mantically dissimilar (indicating humor) from the

remainder of the article:

Figure 2: Two news articles about Hillary Clinton: from the “At press time, researchers from Christopher Hitchens

Onion and The New York Times. Memorial University discovered that it was fun to drink a lot of Johnny Walker Red Label and call people sheep.” An additional set of 120 articles was collected in 2016 to expand the inventory of sources and topics, We also observed a high frequency of slang and and to serve as a reliability test for the manual find- swear words in the satirical news pieces, but unlike ings within the first set. The second set, still evenly Burfoot and Baldwin (2009), for our dataset these distributed between satirical and legitimate news, features did not add predictive powers. was drawn from 6 legitimate3 and 6 satirical4 North Sentence Complexity: A noticeable syntactic American online news sources. difference between the satirical and legitimate arti- Analysis: A trained linguist content-analyzed cles was sentence length and complexity. Especially each pair (legitimate vs. satirical), looking for in- for quotations, the satirical articles tend to pack a sights on similarities and differences as well as greater number of clauses into a sentence for come- trends in language use and rhetorical devices. For dic effect. For example, compare these two excerpts machine learning we used the combined set of 360, - the first from The Onion, and the second from its reserving random 25% of the combined 2 sets data paired article in The New York Times: for testing, and performing 10-fold cross-validation (1) “Not too long ago, these early people were alive and on the training set. The complete dataset is available going about their normal daily lives, but sadly, by the time 5 we scaled down the narrow 90-meter chute leading into the from the lab’s public website . cave, they’d already been dead for at least 10,000 dec- ades,” said visibly upset University of the Witwatersrand 5. Results paleoanthropologist Lee R. Berger, bemoaning the fact that they could have saved the group of human predecessors if 5.1. Data-Driven Linguistic Observations they had just reached the Rising Star cave system during the Pleistocene epoch.” - The Onion “Tearful Anthropologists Absurdity and Humor: Similar to Burfoot & Discover Dead Ancestor of Humans 100,000 Years Too Baldwin (2009), we found that the headlines were Late”. especially relevant to detecting satire. While legiti- (2) “With almost every bone in the body represented mate news articles report new material in the first multiple times, Homo naledi is already practically the best- known fossil member of our lineage,” Dr. Berger said.” - line, satirical articles tend to repeat material from The New York Times “Homo Naledi, New Species in Hu- the title. We also found the final line of each article man Lineage, Is Found in South African Cave”. relevant to satire detection. In particular, the final The Onion’s quotation is 3 times longer; it does line was commonly a “punchline” that highlighted not just quote Dr. Berger, but also describes his mo- absurdities in the story or introduced a new element tive and emotional state. The greater number of to the joke. This humorous function diverges clauses increases the number of punctuation marks found in satire, which informed the development of

3 The Globe and Mail, The Vancouver Sun, Calgary Herald, National Post, The Edmonton 4 www.cbc.ca/punchline, thelapine.ca, syruptrap.ca, www.thecodfish.com, urbananomie.com, Journal, The Hamilton Spectator, USA Today, The Wall Street Journal, Los Angeles Times, www.newyorker.com/humor/borowitz-report, dailycurrant.com, www.thespoof.com, na- The New York Post, Newsday, and The Denver Post. tionalreport.net, worldnewsdailyreport.com, and thepeoplescube.com. 5 http://victoriarubin.fims.uwo.ca/news-verification/

our successful Punctuation feature. Our Grammar variability between satirical and legitimate news ar- feature set incorporates the complexity of phrasing. ticles. The process is implemented as a semi-auto- mated pipeline, summarized in Figure 3.

5.2. Our Satirical Detection Approach and

News Satire Features Based on previous methodological advances in irony, humor, and satire detection, and our data- driven linguistic observations, we propose and test a set of 5 satirical news features: Absurdity, Humor, Grammar, Negative Affect and Punctuation. The Figure 3: News satire detection pipeline for distinguishing sa- method begins with performing a topic-based clas- tirical from legitimate news. sification followed by sentiment-based classifica- Feature Selection: The Absurdity Feature (Abs) tion, and feature selection based on absurdity and was defined by the unexpected introduction of new humor heuristics. The training and evaluation of our named entities (people, places, locations) within the model uses a state-of-the-art method of support vec- final sentence of satirical news. To implement Ab- tor machines (SVMs) using a 75% training and 25% surdity detection we used the Natural Language test split of the dataset, and 10-fold cross validation Toolkit6 (NLTK) Part of Speech tagger and Named applied to the training vectors. We combined cross- validation with the holdout method in reporting Entity Recognizer to detect the list of named enti- overall model performance. Cross validation on our ties. We defined the list as the non-empty set (LNE), 75% training produced a performance prediction on and compared this with the set (NE) of named enti- incoming data. We then confirmed this prediction in ties appearing in the remaining article. The article a second stage using our 25% hold out testing set. was deemed absurd when the intersection (LNE ∩ This allowed us to investigate which records from NE) was empty (0=non-absurd, 1=absurd). the set were incorrectly predicted by the model. Humor (Hum) detection was based on the prem- Text Processing and Feature Weighting: The ises of opposing scripts and maximizing semantic text classification pipeline was scripted in Python distance between two statements as method of 2.7 and used the scikit-learn open source machine punchline identification (Mihalcea et al., 2010). learning package (Pedregosa et al., 2011) as the pri- Similarly, in a humorous article, the lead and final mary SVM classification and model evaluation API. sentence are minimally related. Our modification of In our approach, we described news articles as the punchline detection method assigned the binary sparse feature vectors using a topic-based classifi- value (humor=1) when the relatedness between the cation methodology with the term frequency-in- first and last article sentences was the minimum verse document frequency (tf*idf) weighting with respect to the remaining sentences. scheme. The baseline results for our news corpus We used a knowledge-based metric to measure was achieved using a linear kernel classifier the relatedness between statement pairs in the article (Pedregosa et al., 2011) that assigns positive in- and sought to minimize relatedness of the lead and stances to satire. First, news article text was pre-pro- last line. Given a metric for word-to-word related- cessed, transforming the raw text to a Pandas data ness, we define the semantic relatedness of two text structure for use in Python. Stop words were re- segments S1 and S2 using a metric that combines moved, and unigrams and bigrams were tokenized. the semantic relatedness of each text segment in turn Both the training and test data were converted to tf- with respect to the other text segment. For each idf feature vectors. Term frequency values were also word w in the segment S1 we identified the word in normalized by article length to account for length the segment S2 that has the highest semantic relat- edness, as per (Wu & Palmer, 1994) word-to-word similarity metric. The depth of two given concepts in the WordNet taxonomy, and the depth of the least common subsumer (LCS) were combined into a similarity score (Wu & Palmer, 1994). The same

6 http://www.nltk.org/

process was applied to find the most similar word in used Sklearn.svm.SVC (Support Vector Classifica- S1 starting with words in S2. The word similarities tion) for supervised training with a linear kernel al- were weighted, summed, and normalized with the gorithm, which is suitable for 2 class training data. length of each text segment. The resulting related- Our model was trained on 270 and tested on a set of ness scores were averaged. 90 news articles, with equal proportions of satirical Grammar (Gram) feature vector was the set of and legitimate news. Table 2 presents the measures normalized term frequencies matched against the of precision, recall, and F-score with associated 10- Linguistic Inquiry and Word Count (LIWC) 2015 fold cross validation confidence results for our sat- dictionaries, which accounts for the percentage of ire detection model. The F-score was maximized in words that reflect different linguistic categories the case when Grammar, Punctuation and Absurd- (Pennebaker, Boyd, Jordan, & Blackburn, 2015). ity features were used. Precision was highest when We counted the presence of parts of speech terms Punctuation and Grammar were included. Absurd- including adjectives, adverbs, pronouns, conjunc- ity showed the highest recall performance. tions, and prepositions, and assigned each normal- FEATURES PRECI- F- CONFI- RECALL ized value as the element in a feature array repre- SION SCORE DENCE senting grammar properties. Base (tf-idf) 78 87 82 85 Negative Affect (Neg) and Punctuation (Pun) Base+Abs 85 89 87 83 were assigned as feature weights representing nor- Base +Hum 80 87 83 83 malized frequencies based on term-for-term com- Base+Gram 93 82 87 82 parisons with LIWC 2015 dictionaries. Values Base+Neg 81 84 83 84 were assigned based on the presence of negative Base+Pun 93 82 87 87 affect terms and punctuation (periods, comma, co- Base+Gram+Pun+Abs 90 84 87 84 lon, semi-colon, question marks, exclamation, Base+All 88 82 87 87 quotes) in the training and test set. Features repre- senting Absurdity, Humor, Grammar, Negative Af- 7. Discussion fect and Punctuation were introduced in succession to train the model, combined overall. The predictive Table 2: Satirical news detection evaluation results with 10- performance was measured at the introduction of fold cross-validation. [Legend: Base= baseline tf-idf topic vector; Abs= Absurdity [0,1]; Hum= Humor [0,1]; Gram= Grammar features each new feature and the best performing features (pronoun, preposition, adjective, adverb, conjunction); Neg = Negative were combined and compared to the overall perfor- Affect; Pun= Punctuation; All= all features combined.] mance of all 5. Our findings produced a set of observations about the benefits of detection methods for satire in news, 6. Evaluation as well as what methods were not useful and how they can be improved. We were able to integrate We conducted multiple experiments to identify word level features using an established machine the best-performing combination of features for sa- learning approach in text classification, SVM tirical news identification. We used scikit-learn (Burfoot & Baldwin, 2009), to derive empirical cues (Pedregosa et al., 2011) and the tf*idf F-measure as indicative to deception. This included establishing a the baseline (Base, Table 2), with features added in- baseline model in the form of tf-idf feature weights, crementally. Scikit-learn library contains several while measuring net effects of additional features tools designed for machine learning applications in against the baseline. Our baseline model performed Python7, and has been utilized in the supervised with 82% accuracy, an improvement on Mihalcea et learning applications of real and fake news detec- al. (2010) who achieved a 65% baseline score with tion (Pisarevskaya, 2015; Rubin & Lukoianova, tf-idf method on similar input data. Contrary to our 2014). The Sklearn.svm package is a set of super- expectations, we discovered that individual textual vised learning methods used for classification, re- features of shallow syntax (parts of speech) and gression and outlier detection, capable of perform- punctuation marks are highly indicative of the pres- ing multi-class classification. We assigned two clas- ence of satire, producing a detection improvement ses: satirical news (1) and legitimate news (0), and

7 http://scikit-learn.org

of 5% (87% F-score). This suggests that the rhetor- up another avenue of extending opposing scripts to ical component of satire may provide reliable cues a news corpus. to its identification. Based on our manual analysis, The presence of polarity in satire has been noted this finding may be due to the presence of more in previous methods (Reyes et al., 2013); such as in- complex sentence structures (prevalence of depend- dicated by positive or negative semantic orientation, ent clauses) in satirical content, and strategic use of emotiveness, and emotional words. Our findings run-on sentences for comedic affect. However, this partially bolster this conclusion when we demon- pattern did not translate to longer sentences per se, strated that features representing negative affect im- since our average words-per-sentence feature did proved the performance to 83% in the identification not increase predictive accuracy. Also contrary to task. However, we found no similar improvement our expectation, markers such as profanity and when we measured the contrasting effect of positive slang, word level features deemed significant by semantic orientation. Burfoot and Baldwin (2009), produced no measura- ble improvements in our trials. Our dataset may not 8. Conclusions & Future Work have included more extreme examples of satire. One major contribution of the current research is In this paper, we have translated theories of humor, that we were able to integrate a heuristic for Absurd- irony, and satire into a predictive method for satire ity feature (Abs, Table 2) derived from the concept detection that reaches relatively high accuracy rates of semantic validity, the stark and strategic use of (90% precision, 84% recall, 87% F-score). Since sa- mismatched named entities in a story as a comic de- tirical news is at least part of the time deceptive, vice. Burfoot and Baldwin’s (2009) method of identifying satirical news pieces can aid in minimiz- translating search results on co-occurring entities to ing the potential deceptive impact of satirical news. an absurdity measure informed our approach, when By analyzing the current news production landscape we noticed that satirical sources often introduce pre- captured within our dataset, we demonstrate the fea- viously non-occurring entities in the final sentence. sibility of satire detection methods even when di- Compared to Burfoot and Baldwin’s 65% perfor- vorced from attribution to a satirical source. Our mance, our results show a 5% improvement (F- conceptual contribution is in linking deception de- score 87%), when we added this feature to our base- tection and computational satire, irony and humor line. Our intuition about named entities proved to be research. Practically, this study frames fake news as a defining empirical feature of satirical news. a worthy target for filtering due to its potential to Another concept derived from the theoretical lit- mislead news readers. Areas of further investigation erature on humor is the idea of shifting reference can include ways to translate more complex charac- frames, and incongruity resolution based on the se- teristics of the anatomy of satire into linguistic cues. mantic derivation of textual components. We Critique and call to action, combined with mockery, adapted methods based on identifying latent terms is a key component of satire, but this critical com- (Sjöbergh and Araki, 2007) in punchline detection ponent (II, Ermida, 2012) has not yet received much against a setup statement. Instead of relying on more attention in the field of NLP. This feature could be complex methods of representing longer text subject to automated discourse-level quantification (through TMRs per Hempelmann et al. (2006), we through the presence of imperative sentences. Also, modified the semantic distance approach between our positive results of shallow syntax features the lead sentence (setup) and the last sentence showed us that more complex language patterns, for (punchline) which is hypothesized to be maximized example deep syntax and the ordering of grammati- in satire. The results of Mihalcea and Pulman (2007) cal patterns might also be detectable in satire showed a performance of 84% using word-wise se- through techniques such as regular expression pat- mantic distance in WordNet classification. Our tern matching against a grammatical parse of article model showed a comparable performance of 83% content. when this feature was added to the baseline, opening Acknowledgments This research has been funded by the Government

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