Eugene Veniaminovich Lutsenko

As a method of the research we used Automated system-cognitive analysis (ASC-analysis), which is a new innovative method of artificial intelligence: it also has its own software tool – an intelligent system called "Eidos" (open source software) [1, 2, 3].

The Eidos-X++ system differs from other artificial intelligence systems in the following parameters:

- was developed in a universal setting, independent of the subject area. Therefore, it is universal and can be applied in many subject areas (http://lc.kubagro.ru/aidos/index.htm);

- is in full open free access (http://lc.kubagro.ru/aidos/_Aidos-X.htm), and with the relevant source texts (http://lc.kubagro.ru/__AIDOS-X.txt);

- is one of the first domestic systems of artificial intelligence of the personal level, i.e. it does not take special training in the field of technologies of artificial intelligence from the user (there is an act of introduction of system "Eidos" of 1987) (http://lc.kubagro.ru/aidos/aidos02/PR-4.htm);

- provides stable identification in a comparable form of strengh and direction of cause-effect relationships in incomplete noisy interdependent (nonlinear) data of very large dimension of numerical and non-numerical nature, measured in different types of scales (nominal, ordinal and numerical) and in different units of measurement (i.e. does not impose strict requirements to the data that can not be performed, and processes the data that is) [12];

- contains a large number of local (supplied with the installation) and cloud educational and scientific applications (currently 31 and 152, respectively) (http://lc.kubagro.ru/aidos/Presentation_Aidos-online.pdf);

- provides multilingual interface support in 44 languages. Language databases are included in the installation and can be replenished automatically;

- supports on-line environment of knowledge accumulation and is widely used all over the world (http://aidos.byethost5.com/map5.php);

- the most time-consuming computationally, the operations of the synthesis models and implements recognition by using graphic processing unit (GPU) that some tasks can only support up to the solution of these tasks is several thousand times that really provides intelligent processing of big data, big information and big knowledge;

- provides transformation of the initial empirical data into information, and its knowledge and solution using this knowledge of classification problems, decision support and research of the subject area by studying its system-cognitive model, generating a very large number of tabular and graphical output forms (development of cognitive graphics), many of which have no analogues in other systems (examples of forms can be found in: http://lc.kubagro.ru/aidos/aidos18_LLS/aidos18_LLS.pdf);

- well imitates the human style of thinking: gives the results of the analysis, understandable to experts on the basis of their experience, intuition and professional competence.

Really interesting question. Today's methods use fact-checkability and incongruity between a content and an emotional reaction (emoticon). But there's more than that and we know it from psychological studies. Please have a look at the following paper: Distinguishing irony from deception: Understanding the speaker's second‐order intention by Ellen Winner and Sue Leekam. They hypothesize how children draw this line and how e.g. the intonation is important:

This study investigated how children detect the attitude behind irony and distinguish it from the attitude conveyed by a white lie. Two hypotheses were tested: (1) the ability to distinguish the second‐order intentions of the liar vs. ironist (i.e. what each wants the listener to know) should be a prerequisite for the ability to distinguish ironic from deceptive attitude; (2) the presence of distinctive intonations (sarcastic *** sincere) should facilitate the distinction between ironic and deceptive attitude. Five‐ to 7‐year‐olds heard two stories which ended in either a deceptive or an ironic statement. Children distinguished between the stories in two ways: (a) in terms of whether the speaker wanted the listener to believe him or not (second‐order intention judgement); (b) in terms of whether the speaker was being mean or nice (attitude judgement). In one condition, the final utterances were distinguished by intonation (sarcastic for the irony, sincere for the lie); in the other condition, the utterances were spoken identically, without intonation, in the form of an indirect quote. Results supported the first but not the second hypothesis. Almost all children who failed to make correct second‐order judgements also failed to distinguish which speaker was being mean (ironist) and which was being nice (white liar). However, those who succeeded on the second‐order question but failed the attitude question were equally distributed across the intonation and no‐intonation conditions. Thus, for children of this age, intonation failed to facilitate the ability to distinguish the negative attitude conveyed by irony from the positive attitude conveyed by a white lie.

https://doi.org/10.1111/j.2044-835X.1991.tb00875.x

Ritschel et al. recently published a paper about expressing irony in human-robot dialogue. It covers various features such as intonation, exaggeration and nonverbal clues which should also be helpful for recognizing sarcastic statements from the human's side.

You can find the paper here: Conference Paper Irony Man: Augmenting a Social Robot with the Ability to Use...

Dear Kathrin Janowski Rafal Rzepka Eugene Veniaminovich Lutsenko Mohammad Amin Motamedi thank you so much for your kind replies. I am still going through the references you pointed me to and shall add an update once I am done