Introduction
With the advent of social media, irony and sarcasm detection has become an active area of research in Natural Language Processing (NLP) BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Most computational studies have focused on building state-of-the-art models to detect whether an utterance or comment is ironic/sarcastic or not, sometimes without theoretical grounding. In linguistics and discourse studies, BIBREF4 (2000) and later BIBREF5 (2010) have studied two theoretical aspects of irony in the text: irony factors' and irony markers. Irony factors are characteristics of ironic utterances that cannot be removed without destroying the irony. In contrast, irony markers are a meta-communicative clue that “alert the reader to the fact that a sentence is ironical” BIBREF4 . They can be removed and the utterance is still ironic.
In this paper, we examine the role of irony markers in social media for irony recognition. Although punctuations, capitalization, and hyperboles are previously used as features in irony detection BIBREF6 , BIBREF7 , here we thoroughly analyze a set of theoretically-grounded types of irony markers, such as tropes (e.g., metaphors), morpho-syntactic indicators (e.g., tag questions), and typographic markers (e.g., emoji) and their use in ironic utterances. Consider the following two irony examples from INLINEFORM0 and INLINEFORM1 given in Table TABREF2 .
Both utterances are labeled as ironic by their authors (using hashtags in INLINEFORM0 and the /s marker in INLINEFORM1 ). In the INLINEFORM2 example, the author uses several irony markers such as Rhetorical question (e.g., “are you telling” ...) and metaphor (e.g., “golden age”). In the INLINEFORM3 example, we notice the use of capitalization (“AWESOME”) and emoticons (“:P” (tongue out)) that the author uses to alert the readers that it is an ironic tweet.
We present three contributions in this paper. First, we provide a detailed investigation of a set of theoretically-grounded irony markers (e.g., tropes, morpho-syntactic, and typographic markers) in social media. We conduct the classification and frequency analysis based on their occurrence. Second, we analyze and compare the use of irony markers on two social media platforms ( INLINEFORM0 and INLINEFORM1 ). Third, we provide an analysis of markers on topically different social media content (e.g., technology vs. political subreddits).
Data
Twitter: We use a set of 350K tweets for our experiments. The ironic/sarcastic tweets are collected using hashtags, such as #irony, #sarcasm, and #sarcastic whereas the non-sarcastic tweets do not contain these hashtags, but they might include sentiment hashtags, such as #happy, #love, #sad, #hate (similar to BIBREF8 , BIBREF9 ). As pre-processing, we removed the retweets, spam, duplicates, and tweets written in languages other than English. Also, we deleted all tweets where the hashtags of interest were not located at the very end (i.e., we eliminated “#sarcasm is something that I love”). We lowercased the tweets, except the words where all the characters are uppercased.
Reddit: BIBREF10 (2018) introduced an extensive collection of sarcastic and non-sarcastic posts collected from different subreddits. In Reddit, authors mark their sarcastic intent of their posts by adding “/s” at the end of a post/comment. We collected 50K instances from the corpus for our experiments (denoted as INLINEFORM0 ), where the sarcastic and non-sarcastic replies are at least two sentences (i.e., we discard posts that are too short). For brevity, we denote ironic utterances as INLINEFORM1 and non-ironic utterances as INLINEFORM2 . Both INLINEFORM3 and INLINEFORM4 datasets are balanced between the INLINEFORM5 and INLINEFORM6 classes. We uuse 80% of the datasets for training, 10% for development, and the remaining 10% for testing.
Irony Markers
Three types of markers — tropes, morpho-syntactic, and typographic are used as features.
Tropes:
Tropes are figurative use of expressions.
Metaphors - Metaphors often facilitate ironic representation and are used as markers. We have drawn metaphors from different sources (e.g., 884 and 8,600 adjective/noun metaphors from BIBREF11 and BIBREF12 , respectively, and used them as binary features. We also evaluate the metaphor detector BIBREF13 over INLINEFORM0 and INLINEFORM1 datasets. We considered metaphor candidates that have precision INLINEFORM2 0.75 (see BIBREF13 (2017)).
Hyperbole - Hyperboles or intensifiers are commonly used in irony because speakers frequently overstate the magnitude of a situation or event. We use terms that are denoted as “strong subjective” (positive/negative) from the MPQA corpus BIBREF14 as hyperboles. Apart from using hyperboles directly as the binary feature we also use their sentiment as features.
Rhetorical Questions - Rhetorical Questions (for brevity INLINEFORM0 ) have the structure of a question but are not typical information seeking questions. We follow the hypothesis introduced by BIBREF15 (2017) that questions that are in the middle of a comment are more likely to be RQ since since questions followed by text cannot be typical information seeking questions. Presence of INLINEFORM1 is used as a binary feature.
Morpho-syntactic (MS) irony markers:
This type of markers appear at the morphologic and syntactic levels of an utterance.
Exclamation - Exclamation marks emphasize a sense of surprise on the literal evaluation that is reversed in the ironic reading BIBREF5 . We use two binary features, single or multiple uses of the marker.
Tag questions - We built a list of tag questions (e.g.,, “didn't you?”, “aren't we?”) from a grammar site and use them as binary indicators.
Interjections - Interjections seem to undermine a literal evaluation and occur frequently in ironic utterances (e.g., “`yeah", `wow”, “yay”,“ouch” etc.). Similar to tag questions we assembled interjections (a total of 250) from different grammar sites.
Typographic irony markers:
Capitalization - Users often capitalize words to represent their ironic use (e.g., the use of “GREAT", “SO”, and “WONDERFUL” in the ironic tweet “GREAT i'm SO happy shattered phone on this WONDERFUL day!!!”).
Quotation mark - Users regularly put quotation marks to stress the ironic meaning (e.g., “great” instead of GREAT in the above example).
Other punctuation marks - Punctuation marks such as “?”, “.”, “;” and their various uses (e.g., single/multiple/mix of two different punctuations) are used as features.
Hashtag - Particularly in INLINEFORM0 , hashtags often represent the sentiment of the author. For example, in the ironic tweet “nice to wake up to cute text. #suck”, the hashtag “#suck” depicts the negative sentiment. We use binary sentiment feature (positive or negative) to identify the sentiment of the hashtag, while comparing against the MPQA sentiment lexicon. Often multiple words are combined in a hashtag without spacing (e.g., “fun” and “night” in #funnight). We use an off-the-shelf tool to split words in such hashtags and then checked the sentiment of the words.
Emoticon - Emoticons are frequently used to emphasize the ironic intent of the user. In the example “I love the weather ;) #irony”, the emoticon “;)” (wink) alerts the reader to a possible ironic interpretation of weather (i.e., bad weather). We collected a comprehensive list of emoticons (over one-hundred) from Wikipedia and also used standard regular expressions to identify emoticons in our datasets. Beside using the emoticons directly as binary features, we use their sentiment as features as well (e.g., “wink” is regarded as positive sentiment in MPQA).
Emoji - Emojis are like emoticons, but they are actual pictures and recently have become very popular in social media. Figure FIGREF22 shows a tweet with two emojis (e.g., “unassumed” and “confounded” faces respectively) used as markers. We use an emoji library of 1,400 emojis to identify the particular emoji used in irony utterances and use them as binary indicators.
Classification Experiments and Results
We first conduct a binary classification task to decide whether an utterance (e.g., a tweet or a INLINEFORM0 post) is ironic or non-ironic, exclusively based on the irony marker features. We use Support Vector Machines (SVM) classifier with linear kernel BIBREF16 . Table TABREF23 and Table TABREF24 present the results of the ablation tests for INLINEFORM1 and INLINEFORM2 . We report Precision ( INLINEFORM3 ), Recall ( INLINEFORM4 ) and INLINEFORM5 scores of both INLINEFORM6 and INLINEFORM7 categories.
Table TABREF23 shows that for ironic utterances in INLINEFORM0 , removing tropes have the maximum negative effect on Recall, with a reduction on INLINEFORM1 score by 15%. This is primarily due to the removal of hyperboles that frequently appear in ironic utterances in INLINEFORM2 . Removing typographic markers (e.g., emojis, emoticons, etc.) have the maximum negative effect on the Precision for the irony INLINEFORM3 category, since particular emojis and emoticons appear regularly in ironic utterances (Table TABREF25 ). For INLINEFORM4 , Table TABREF24 shows that removal of typographic markers such as emoticons does not affect the F1 scores, whereas the removal of morpho-syntactic markers, e.g., tag questions, interjections have a negative effect on the F1. Table TABREF25 and Table TABREF26 represent the INLINEFORM5 most discriminative features for both categories based on the feature weights learned during the SVM training for INLINEFORM6 and INLINEFORM7 , respectively. Table TABREF25 shows that for INLINEFORM8 , typographic features such as emojis and emoticons have the highest feature weights for both categories. Interestingly, we observe that for ironic tweets users often express negative sentiment directly via emojis (e.g., angry face, rage) whereas for non-ironic utterances, emojis with positive sentiments (e.g., hearts, wedding) are more familiar. For INLINEFORM9 (Table TABREF26 ), we observe that instead of emojis, other markers such as exclamation marks, negative tag questions, and metaphors are discriminatory markers for the irony category. In contrary, for the non-irony category, positive tag questions and negative sentiment hyperboles are influential features.
Frequency analysis of markers
We also investigate the occurrence of markers in the two platforms via frequency analysis (Table TABREF29 ). We report the mean of occurrence per utterance and the standard deviation (SD) of each marker. Table TABREF29 shows that markers such as hyperbole, punctuations, and interjections are popular in both platforms. Emojis and emoticons, although the two most popular markers in INLINEFORM0 are almost unused in INLINEFORM1 . Exclamations and INLINEFORM2 s are more common in the INLINEFORM3 corpus. Next, we combine each marker with the type they belong to (i.e., either trope, morpho-syntactic and typographic) and compare the means between each pair of types via independent t-tests. We found that the difference of means is significant ( INLINEFORM4 ) for all pair of types across the two platforms.
Irony markers across topical subreddits
Finally, we collected another set of irony posts from BIBREF10 , but this time we collected posts from specific topical subreddits. We collected irony posts about politics (e.g., subreddits: politics, hillary, the_donald), sports (e.g., subreddits: nba, football, soccer), religion (e.g., subreddits: religion) and technology (e.g., subreddits: technology). Table TABREF27 presents the mean and SD for each genre. We observe that users use tropes such as hyperbole and INLINEFORM0 , morpho-syntactic markers such as exclamation and interjections and multiple-punctuations more in politics and religion than in technology and sports. This is expected since subreddits regarding politics and religion are often more controversial than technology and sports and the users might want to stress that they are ironic or sarcastic using the markers.
Conclusion
We provided a thorough investigation of irony markers across two social media platforms: Twitter and Reddit. Classification experiments and frequency analysis suggest that typographic markers such as emojis and emoticons are most frequent for INLINEFORM0 whereas tag questions, exclamation, metaphors are frequent for INLINEFORM1 . We also provide an analysis across different topical subreddits. In future, we are planning to experiment with other markers (e.g., ironic echo, repetition, understatements).

Question: Do they evaluate only on English datasets?
Answer: The twitter dataset is English-only; no information for the reddit dataset is given

Yes

Yes

Question: What type of frequency analysis was used?
Answer: mean of occurrence per utterance and the standard deviation (SD) of each marker

based on their occurrence

Mean of occurrence per utterance and the standard deviation is calculated for every marker type; the means between each pair of types is compared via independent t-tests

Question: What type of classifiers were used?
Answer: Support Vector Machines (SVM) classifier with linear kernel BIBREF16

Support Vector Machines (SVM) classifier with linear kernel BIBREF16 

Support Vector Machines (SVM) classifier with linear kernel

Question: Who annotated the Twitter and Reddit data for irony?
Answer: collected using hashtags, such as #irony, #sarcasm, and #sarcastic

Authors of the tweets and reddit posts

Twitter and Reddit users of the original data