# Concepts and Experiments on Psychoanalysis Driven Computing

Minas Gadalla

Department of Computer Engineering & Informatics
University of Patras, Greece
email<EMAIL_ADDRESS>Sotiris Nikoletseas

University of Patras and CTI, Greece
email<EMAIL_ADDRESS>José Roberto de A. Amazonas
José D. P. Rolim
Department of Computer Architecture
Technical University of Catalonia - UPC
Barcelona - Spain
and email<EMAIL_ADDRESS>University of Geneva, Switzerland
email<EMAIL_ADDRESS>
###### Abstract

This research investigates the effective incorporation of the human factor and
user perception in text-based interactive media. In such contexts, the
reliability of user texts is often compromised by behavioural and emotional
dimensions. To this end, several attempts have been made in the state of the
art, to introduce psychological approaches in such systems, including
computational psycholinguistics, personality traits and cognitive psychology
methods.

In contrast, our method is fundamentally different since we employ a
psychoanalysis-based approach; in particular, we use the notion of Lacanian
discourse types, to capture and deeply understand real (possibly elusive)
characteristics, qualities and contents of texts, and evaluate their
reliability. As far as we know, this is the first time computational methods
are systematically combined with psychoanalysis. We believe such
psychoanalytic framework is fundamentally more effective than standard
methods, since it addresses deeper, quite primitive elements of human
personality, behaviour and expression which usually escape methods functioning
at “higher”, conscious layers. In fact, this research is a first attempt to
form a new paradigm of psychoanalysis-driven interactive technologies, with
broader impact and diverse applications.

To exemplify this generic approach, we apply it to the case-study of fake news
detection; we first demonstrate certain limitations of the well-known
Myers–Briggs Type Indicator (MBTI) personality type method, and then propose
and evaluate our new method of analysing user texts and detecting fake news
based on the Lacanian discourses psychoanalytic approach.

##  1 Introduction

User-related and generated data (for simplicity referred to as user data)
constitute a core component for social applications based on interactive media
technologies. However, in various contexts where user perception is more
susceptible to emotional or some other form of bias, user data reliability is
often compromised.

A scenario that accurately exemplifies the impact of the lack of reliability
of user data can be illustrated in disaster management. In a hypothetical
event of a car accident on a frequented highway, where lots of other cars are
passing by, we can imagine loads of social media posts describing details
about it, before any truly reliable information (e.g. the police arriving) is
conveyed. In such a case, if the high volume/uncertain quality user-based
information could be instantly filtered, so that a reliable source which e.g.
accurately reports the degree of passenger injury severity could be
identified, the timely arrival of an ambulance could be of life-saving impact.
Of course, the above is a non-trivial task as it implies mechanisms which are
yet non-existent; it is certain, though, that an interdisciplinary approach is
necessary to capture the diverse aspects of human social way of expressing
perception in terms of data and model it in a formal way, in order to
conceptualise such mechanisms and infer the reliability of the information and
knowledge that can be acquired from such data.

Because of this lack of reliability associated with user data, interactive
media technologies-based applications that depend on the characterisation and
prediction of the information and knowledge acquired from such data may be
severely impaired and are rarely adopted by the actors involved in real life
scenarios.

A relevant, recently established research topic is on detecting fake news. In
particular, “fake news detection” is defined as the task of categorising news
along a continuum of veracity, with an associated measure of certainty;
veracity is compromised by the occurrence of intentional deceptions [1]. The
state-of-the-art methods for detecting the spread of fake news can be coarsely
classified into two categories. The linguistic approaches are based on
“language leakages” that take place when someone tries to conceal a lie [2].
Here, certain verbal aspects are monitored, such as frequencies and patterns
of pronouns, conjunctions, and negative emotion word usage; a task that is
found very difficult to achieve. On the other hand, the network approaches are
based on corresponding properties and behaviour of how the news is spread.
Here, linked data and social network behaviour are studied [3].

We conjecture that psychoanalysis theories may be used to provide the tools
for a third methodology to be developed. One may choose to disseminate fake
news for several reasons; e.g., due to being irrational or because there is
something to gain. Independently of the different motives, certain text
qualities characteristic of fake news can be captured by a psychoanalytic
examination of the texts.

The overarching aim of this research work is to develop a radically new
theoretical framework for interpreting user-generated data in the context of
social interactions. In particular, by combining elements from two very
divergent disciplines – Computer Science and Psychoanalysis – this work will
develop the theoretical methods and tools for gaining a deep, holistic
understanding of the behavioural context of individuals, groups and crowds
from the data they generate. This work focus on improving and providing a
fundamentally new perspective in terms of the corresponding technologies.

In this context, this research has the ambitious goal of laying the
foundations for a new paradigm of psychoanalysis-driven technologies.

The specific contributions of this paper, within the broader aims stated
previously, may be summarised as follows:

  * •

evaluation of the fake news detection accuracy method based on the personality
traits concept demonstrating its limitations;

  * •

proposal of a new method to classify user data based on the Lacanian
discourses psychoanalytical concept;

  * •

evaluation of the fake news detection accuracy based on the Lacanian
discourses approach;

  * •

definition of a framework and roadmap for the future development of
psychoanalysis-driven computing.

After this Introduction, Section 2 describes and compares related works
published in the recent years, Section 3 describes the personality traits
concept and introduces the novel psychoanalysis-driven approach based on
Lacanian discourses, Section 4 evaluates the potential of the adopted
Psychological and Psychoanalytic approaches to identify reliability related
characteristics of enunciations, Section 5 presents the computational approach
followed, and Section 6 summarises the conclusions and proposes a roadmap for
future work.

##  2 Related Work

Relevant, yet different, approaches of combining psychological and social
dimensions with computational methods include computational psycholinguistics,
personality traits, behavioural analysis, emotional states and cognitive
psychology methods. Compared to all those approaches, our approach is
fundamentally different since we adopt a psychoanalytic perspective; in
particular, we employ the powerful notion of Lacanian discourse types. To the
best of our knowledge, this is the first attempt of systematically bringing
together psychoanalysis and computing. We believe that such a psychoanalytic
approach is eventually more effective compared to the previously mentioned
methods, since it addresses deeper, fundamental elements of human personality,
behaviour and expression which usually escape methods operating at a “higher”
conscious layer.

Having stressed this general novelty of our research methodology, we below
discuss relevant, recent research related to the particular case study (fake
news detection) which we use in order to exemplify our method.

A fundamental approach of combining psychology with computational linguistics
(based on abstract formulations of phrases via a collection of finite-state
transition networks) is described in [4]; in particular, the author envisions
sophisticated natural language technologies as a key factor for improving the
(rather poor) performance of current conversational systems used by modern
technology. The abundant availability of massive data along with effective AI
methods (including deep learning) is expected to further facilitate this
vision. We note that, although the notion of conversation is directly relevant
to the notion of discourse, the approach taken in that paper is more limited
(psychological) than the directly psychoanalytic attempt we pursue in our
research.

For the more concrete aspect of detecting misinformation in online social
networks, [5] suggests the application of cognitive psychology concepts. An
efficient algorithm for detection of spread of misinformation in Twitter is
proposed, based on text and network-wide qualities such as the consistency of
message, the coherency of message, the credibility of source and the general
acceptability of message in the network. Again, no psychoanalytic elements are
considered when evaluating the qualitative properties of the text. Also, the
use of objective, global information is employed, in contrast to our approach
which focuses on each text separately (however, our method can also be
extended to include global information about texts).

Psychological factors (in particular, emotions as expressed in Reddit
conversations) are addressed in [6], to propose a model for passively
detecting mental disorders. The suggested model is based entirely on emotional
states and the transitions between these states identified in Reddit posts, in
contrast to content-based representations (e.g., n-grams, language model
embeddings etc.) in the relevant state of the art. The scope is to overcome
the domain and topic bias of content-based representations, towards more
general applicability. Our approach aims to avoid a content-specific bias,
focusing on underlying qualities of texts captured by the discourse type
identification. In fact, discourse types are an even more “primitive” aspect
of texts, more so than emotions; so the generality of our method might be
broader.

In another psychology-based research for fake news detection, a behavioural
analysis approach is taken [7]. In particular, the authors use supervised
learning algorithms to profile fake-news spreaders, based on the combination
of Big Five personality traits and stylometric features. The method is
evaluated on a tweeter dataset in both English and Spanish languages. In a
similar spirit, [8] aims to understand the motivations for sharing fake news
and the corresponding personality traits among social media users in India; in
particular, the findings suggest that the passing time, information sharing
and socialisation gratifications lead to instant sharing news. Also, people
who exhibit extraversion, neuroticism and openness share news on social media
platforms instantly; in contrast, agreeableness and conscientiousness
personality traits lead to authenticating news before sharing. We note that
that work focuses on fake news spreading, not fake news identification itself.
Also, as stated previously, although those works exhibit a psychology-based
flavour too, our own research is based on psychoanalytic methods, particularly
for identifying the power of characteristic Lacanian discourse types to
identify specific characteristics in texts in interactive media as, for
example, the fake news identification that we selected to develop the case
study of this work.

##  3 Psychological and Psychoanalytic Approaches Taken

In this section, we describe two distinct approaches to potentially infer
reliability-related information from user data. The first one is based on the
psychological concept of personality traits, and the second one on the
psychoanalytical concept of Lacanian discourses.

###  3.1 Personality Type Prediction

Two potential answers to the question ”What makes people unique and different
from one another?” are motives or traits [9]. Regarding motives, we can find
the work ”Motivation and personality: Handbook of thematic content analysis”
[10] and Murray’s list of needs [11]. However, no computational approach has
been implemented or proposed. Personality traits and types, on the other hand,
have numerous theories, taxonomies, and frameworks available, of which the
most well known are the Big Five [12] and the Myers–Briggs-Type-Indicator
(MBTI) [13]. More specifically, the trait approach, rather than the motives
approach, is still the most widely used and most accepted conceptual framework
for describing human personality and successfully predicting human behavior,
and has been implemented and applied multiple times in computer-based
applications, such as in social media advertising [14], trait prediction from
facial images [15], and many others. Both these systems are intended to
indicate or predict the subject’s personality, and thus his or her behavior
and preferences, by breaking them down into many dimensions, or groups. In
this research, we are focusing mainly on the traits approach and particularly
the MBTI system.

The Big Five personality traits comprise the following: extraversion,
conscientiousness, agreeableness, openness to new experiences, and neuroticism
[12]. Meanwhile, the MBTI personality model describes an individual’s
preferences/behaviour in four dimensions/groups which are also illustrated in
Figure 1:

  * •

Extraversion-Introversion: This dimension measures how a person gets their
energy. Extraverts are energized by being around people, while introverts are
energized by being alone.

  * •

Sensing - Intuition: This dimension measures how a person takes in
information. Sensors rely on their five senses to gather information, while
Intuitives rely on their gut feelings and intuition

  * •

Thinking- Feeling: This dimension measures how a person makes decisions.
Thinkers use logic and reason to make decisions, while Feelers use their
emotions and values.

  * •

Judging - Perceiving: This dimension measures how a person lives their life.
Judgers like structure and order, while Perceivers are more flexible and
spontaneous.

Figure 1:  Personality types groups

This way, each individual is classified in terms of one of 16 possible four-
letter codes, like ESFJ, indicating a person who may:

  * •

Extraverted (E): is more concerned with the outside world of people and things
than with the inner world of ideas

  * •

Sensing (S): would rather work with known facts and solid experience than
explore possibilities or meanings

  * •

Feeling (F): steps into situations to weigh human values and motives. Prefers
to make decisions on the basis of values.

  * •

Judging (J): prefers a planned, determined orderly way of life to a flexible
spontaneous.

MBTI was developed by Isabel Briggs Myers in the 1940s [13] to implement
Jung’s [16] theory of psychological types and the results from such an
approach are obtained classically from test booklets, answer sheets, and score
keys, and are produced professionally [17] [13]. In Sections 4 and 5 we
discuss and use some computational ways of determining personality types using
semantic and linguistic analysis and present the results from a Fake/Real news
detection application.

###  3.2 Lacanian Discourses

The Four Discourses theory constitutes an attempt of formalisation of the
different ways people relate to each other and the economy of knowledge and
enjoyment in social relationships. The Lacanian framework defines a more
complex representation of the roles assumed by two interacting parties,
formulating four discrete discourse types [18], [19] and [20]: Discourse of
the Master – Struggle for mastery/domination/penetration; Discourse of the
University – Provision and worship of “objective” knowledge – usually in the
unacknowledged service of some external master discourse; Discourse of the
Hysteric – Symptoms embodying and revealing resistance to the prevailing
master discourse; Discourse of the Analyst  – Deliberate subversion of the
prevailing master discourse.

Later, Lacan defined an additional fifth discourse, which is not considered in
this work, the discourse of the Capitalist [21], where the subject is
commanded to enjoy in the form of commodities.

Figure 2 presents the general representation of the Lacanian discourses. The
four places of the discourse: the “agent”, the giver of the discourse; the
“other”, the one to whom the discourse is addressed; under the message of the
agent is hidden the “truth”, which is masked by the official statement; and
hidden under the other is the “production”, or what the agent gets out of the
relationship. It is important to realise that the influence of the hidden
“truth” upon the “agent” and the enunciation interpretation performed by the
“other” are subjective unconscious processes of the communicating parties

It is possible to draw a parallel between the terms of a discourse and the
components of a communication process, in such a way that the dynamics of a
given discourse, i.e., the internal relations between elements arranged in
different places, can serve to characterise the dynamics of a given media
process.

Figure 2: General representation of the Lacanian discourses.

The elements of a discourse or the roles assumed by the communicating parties
are the following:

  * •

S1: the master signifier represents the true essence of the subject. It may be
summarised by _who I am_.

  * •

S2: represents the knowledge of the subject. It may be summarised by _what I
know_.

  * •

a: represents the object cause of desire. It may be summarised by _what I
want_.

  * •

$: represents the barred subject castrated by the language. It may be
summarised by _what I speak_.

The discourses are defined by the position each element occupies in the
general representation depicted in Figure 2. Each discourse representation is
called, in the Lacanian jargon, a _matheme_. The definitions given below are
reproduced from [20].

> $\left\uparrow\dfrac{\text{S1}}{\$}\hskip
> 8.5359pt\overrightarrow{\mathchoice{{\ooalign{$\displaystyle\nearrow$\cr\hfil$\displaystyle\nwarrow$\hfil\cr}}}{{\ooalign{$\textstyle\nearrow$\cr\hfil$\textstyle\nwarrow$\hfil\cr}}}{{\ooalign{$\scriptstyle\nearrow$\cr\hfil$\scriptstyle\nwarrow$\hfil\cr}}}{{\ooalign{$\scriptscriptstyle\nearrow$\cr\hfil$\scriptscriptstyle\nwarrow$\hfil\cr}}}}\hskip
> 8.5359pt\dfrac{\text{S2}}{a}\right\downarrow$ Master Discourse: the master
> signifier (S1) (of the Master) is the agent of communication and instead of
> addressing the other (a), addresses the “knowledge” (S2) in his/her place;
> in other words, the Master is addressing the other not as a Subject but in
> his/her functional role, because of his/her ability or knowledge (S2). The
> truth of the master signifier is actually a barred Subject ($) and, as
> lacking as everyone else, is hidden; however, beneath that master signifier
> (S1), the barred Subject ($) is enjoying the production of the knowledge
> (what comes from a).

> $\left\uparrow\dfrac{\text{S2}}{S1}\hskip
> 8.5359pt\overrightarrow{\mathchoice{{\ooalign{$\displaystyle\nearrow$\cr\hfil$\displaystyle\nwarrow$\hfil\cr}}}{{\ooalign{$\textstyle\nearrow$\cr\hfil$\textstyle\nwarrow$\hfil\cr}}}{{\ooalign{$\scriptstyle\nearrow$\cr\hfil$\scriptstyle\nwarrow$\hfil\cr}}}{{\ooalign{$\scriptscriptstyle\nearrow$\cr\hfil$\scriptscriptstyle\nwarrow$\hfil\cr}}}}\hskip
> 8.5359pt\dfrac{\text{a}}{\$}\right\downarrow$ University Discourse: the
> discourse of the University makes a point about the functioning of
> institutions, and by extension of the individuals within them in their
> capacity of incarnating the institution. Knowledge (S2) occupies the place
> of the agent, which addresses itself to the object cause of desire (a), as
> the desire for knowledge is the supposed reason why the student is there.
> However, in this relationship, one can see that the _objet petit_ a is also,
> and perhaps as importantly, fed into by the master signifiers of the
> institution (S1), and these contribute endlessly to the castratedness of the
> Subject of the student. In addressing the knowledge not to the Subject, but
> to the object cause of desire of the Subject, what is “produced” in, in
> fact, more castratedness ($). Beneath the appearance of dispensing knowledge
> (S2), the University controls the Subject ($) by means of its master
> signifiers (S1) and enjoys the “production” of the castrated student ($).
> The institution is also guilty of giving the impression to the student that
> by careful attention and absorption of its master signifiers, she/he may
> overcome his/her castration. This is a system of functioning that is common
> to all institutions: corporations, professions, and government departments,
> indeed in any institution where “knowledge” (S2) in some form takes the
> place of the agent which addresses discourse, and acts as a lure to the
> others desire (a).

> $\left\uparrow\dfrac{\text{a}}{S2}\hskip
> 8.5359pt\overrightarrow{\mathchoice{{\ooalign{$\displaystyle\nearrow$\cr\hfil$\displaystyle\nwarrow$\hfil\cr}}}{{\ooalign{$\textstyle\nearrow$\cr\hfil$\textstyle\nwarrow$\hfil\cr}}}{{\ooalign{$\scriptstyle\nearrow$\cr\hfil$\scriptstyle\nwarrow$\hfil\cr}}}{{\ooalign{$\scriptscriptstyle\nearrow$\cr\hfil$\scriptscriptstyle\nwarrow$\hfil\cr}}}}\hskip
> 8.5359pt\dfrac{\text{\$}}{S1}\right\downarrow$ Analyst Discourse: in the
> discourse of the Analyst, the Analyst has accepted becoming symbolically,
> the _objet petit_ a of the analysand. This is one of the most usual roles
> the analyst has to accept; he/she is an empty mirror upon which everything
> may be reflected, and when in full transference, the analysand (S1) will be
> addressing his/her object cause of desire (a). In this case, in his/her role
> as the _objet petit_ a, the Analyst addresses his/her discourse to the
> castratedness ($), the anxiety, of the patient, and his/her questioning
> pushes the analysand to produce a master signifier (S1) which is reflected
> back to the Analyst, while the hidden knowledge of the Analyst (S2), in the
> place of truth, is fed into the castrated Subject ($), fuelling the
> production by it of master signifiers (S1). The analysand will discover that
> the knowledge of its own desire (S2 hidden beneath a) is not held by the
> Analyst but revealed through its master signifier (S1). The Analyst does not
> adopt a position of power like the Master, or of knowledge, like the
> University, and because of that, is often considered subversive by
> institutions.

> $\left\uparrow\dfrac{\text{\$}}{a}\hskip
> 8.5359pt\overrightarrow{\mathchoice{{\ooalign{$\displaystyle\nearrow$\cr\hfil$\displaystyle\nwarrow$\hfil\cr}}}{{\ooalign{$\textstyle\nearrow$\cr\hfil$\textstyle\nwarrow$\hfil\cr}}}{{\ooalign{$\scriptstyle\nearrow$\cr\hfil$\scriptstyle\nwarrow$\hfil\cr}}}{{\ooalign{$\scriptscriptstyle\nearrow$\cr\hfil$\scriptscriptstyle\nwarrow$\hfil\cr}}}}\hskip
> 8.5359pt\dfrac{\text{S1}}{S2}\right\downarrow$ Hysteric Discourse: one
> doesn’t have to be hysterical in the clinical sense to hold the Discourse of
> the Hysteric; indeed, Lacan made it clear that this type of discourse in
> non-hysterical people, is precisely what leads to true learning. The agent
> of the discourse is the castrated ($) shortage of the Hysteric; hidden
> beneath its bar is his/her object cause of desire (a). This barred Subject
> ($), driven by its _objet petit_ a, addresses the master signifiers of the
> other (S1), which respond with the production of knowledge (S2), beneath the
> bar. It is to the master signifier (S1) that the Hysteric ($) addresses
> his/her questions, but she/he receives as an answer only the knowledge (S2)
> of that person, which the Hysteric enjoys for want of anything better,
> although these answers never constitute a satisfactory response to his/her
> desire (a). The Discourse of the Hysteric is held by anyone who is on the
> path to knowledge. It is a position that requires perfect acceptance of
> one’s ignorance, no great desire to pretend to any other status, and a
> hunger for the object cause of desire111It is a characteristic of the
> Hysteric discourse to be charged with emotion.

Precisely, the idea is that by distinguishing the discourse type, the $\ll$
truth $\gg$ status of an enunciation can be qualified; i.e., it is the formal
characteristics of the discourse which will inform if the truth is based on
authority, on documented sources, on the needs to identify the essence of the
other, or on the needs to dig for information, respectively. The formal
characteristics will be a key to measure the “reliability” likelihood of the
enunciation. While the personality traits tell us about the speaker, the
Lacanian discourses tell us about what is said. A trained psychoanalyst can
detect the above signifiers based on semantical analysis of the language.

It can be argued how the Lacanian discourse approach can be extended to groups
with more than two interacting parties. The answer to this concern comes from
recognising that psychoanalysis is a general framework for the interpretation
of situations expressed in any format and by any number of people. Since its
development by Freud it has been stated and shown that it can be used to
interpret works of art [22], to analyse social situations [23], [24] or to
conjecture about the future of civilisation [25], [26]. These are just a few
examples of Freud’s works applying Psychoanalysis techniques out of the
psychoanalytical setting of a patient and an analyst.

Lacanian discourses are a formal framework to apply psychoanalytical concepts
to interpret any real life situation. As the aforementioned Freud’s works,
they can be used as a reference model to interpret works of art, social
situations, and so on.

Nevertheless, the association of an interaction with one of the discourses is
not an easy task and has to be dealt with caution. An example that illustrates
how a single media phenomenon can be seen from various angles according to the
discourse theory is provided by [27]: “When Google scans the Internet
collecting information from each site, we are in the discourse of University.
When it meets our demand for providing results, we are in the discourse of
hysteria. When we deify it, we are in the discourse of the master. When it
computes our data and customises the results it offers us, as if it knew us,
knew our preferences and anticipate what we want, we are in the discourse of
capitalism”. Therefore, it is paramount that the context is well defined
before proceeding to association, so that the stakeholders of the discourse
are clearly identified.

Context depends on the kind of application. The knowledge of the application
implies the knowledge of the context and establishes the contextual elements
to be considered.

##  4 Preliminary Evaluation of the Psychological and Psychoanalytic
Approaches

In this section, we empirically evaluate the potential of the Psychological
and Psychoanalytic approaches described in Sections 3.1 and 3.2 to identify
reliability related characteristics of enunciations.

###  4.1 Dataset used for evaluation

The selected dataset was obtained from Kaggle
222https://kaggle.com/datasets/rchitic17/real-or-fake,AccessedMay.25,2022 and
consists of a number of news headlines and content that have been used to
develop Real-Fake news algorithms as, for example by [28]; they obtained a 97%
accuracy of Fake/Real news prediction utilizing the headlines and content,
using the state-of-the-art NLP methods. In our approach, from this dataset,
only the headlines were used and submitted to the personality traits
assignment algorithm developed by
333https://github.com/wiredtoserve/datascience/tree/master/PersonalityDetection,AccessedMay.25,2022.
Each headline also has an associated label. When the label is equal to 0, the
corresponding news is Real, otherwise it is Fake.

Figure 3:  Label Real=0 and Fake=1 distribution

For the needs of this application after analyzing the content we decided to
remove any headline which was less than 4 words, as most of them don’t give
any semantic information if it is fake news or not. The final dataset,
containing 5860 unique _headlines_ , is balanced, ideal for the task of
applying the MBTI theory and the labels are equally distributed as shown in
Figure 3. Finally, for illustration purposes, Table I contains three random
selected rows from the dataset:

TABLE I: Three random selected headlines from the dataset for illustration purposes Headline | Label
---|---
Police Turn In Badges Rather Than Incite Violence Against Standing Rock Protestors | 1
Republican race enters a new volatile phase | 0
First Take Wall Street bids goodbye to June hike | 0

###  4.2 Evaluation of the personality traits (MTBI) approach

An algorithm was developed in the R language to evaluate the potential of the
personality traits approach to identify reliability related characteristics of
the dataset’s _headlines_. R is a free software environment for statistical
computing and graphics. It compiles and runs on a wide variety of UNIX
platforms, Windows and MacOS444R can be downloaded from
https://www.r-project.org.. The main steps of the algorithm are:

  * Step 1:

the dataset is read and stored in a dataframe called raw.comments.df.

  * Step 2:

the dataset is split into test and evaluation datasets, test.comments.df and
eval.comments.df. The test dataset comprises 40% of the _headlines_.

The personality traits are designated as:

    * -

Extroverts - Introverts – EI;

    * -

Sensors - Intuitives – SN;

    * -

Thinkers - Feelers – TF;

    * -

Judgers - Perceivers – JP.

  * Step 3:

the test dataset is split into two datasets, one containing the Real
_headlines_ (test.comments.0.df), i.e, the _headlines_ for which Label = 0,
and one containing the Fake _headlines_(test.comments.1.df), i.e., those
headlines for which Label = 1.

The number of Real and Fake test _headlines_ are 1156 and 1188, respectively.

  * Step 4:

the cumulative distribution function (cdf) for each personality trait given
the value of the label, given by Eq. 1, is evaluated.

$\text{cdf}_{x}(a|v)=\text{Prob}(x\leq a|\text{label}=v)$ (1)

where $x\in\\{EI,SN,TF,JP\\}$, $0\leq a\leq 1$ and $v\in\\{0,1\\}$.

  * Step 5:

using the Bayes theorem, the probability of the label assuming a determined
value given the probability of a personality trait is less than or equal to a
certain value is evaluated by Eq. 2.

$\text{Prob}(\text{label}=v|x\leq a)=\dfrac{\text{Prob}(x\leq
a|\text{label}=v)}{\sum_{v\in\\{0,1\\}}\text{Prob}(x\leq a|\text{label}=v)}$
(2)

where $x\in\\{EI,SN,TF,JP\\}$, $0\leq a\leq 1$ and $v\in\\{0,1\\}$.

Figures 4 and 5 show the cumulative distribution function for each personality
trait, Label = 0 and Label =1. It can be seen that the distributions are very
similar indicating that it may not be possible to differentiate between the
labels using the personality traits.

Figure 4: Cumulative distribution function for each personality trait and
Label = 0. Figure 5: Cumulative distribution function for each personality
trait and Label = 1.

Figures 6, 7, 8 and 9 show the probability of the label assuming a determined
value given the probability of a personality trait (EI, SN, TF or JP) is less
than or equal to a certain value.

Figure 6: Probability of the label assuming a determined value given the
probability of a personality trait is less than or equal to a certain value.
Figure 7: Probability of the label assuming a determined value given the
probability of a personality trait is less than or equal to a certain value.
Figure 8: Probability of the label assuming a determined value given the
probability of a personality trait is less than or equal to a certain value.
Figure 9: Probability of the label assuming a determined value given the
probability of a personality trait is less than or equal to a certain value.

It can be seen in Figures 6, 7, 8 and 9 that: i) JP does not help at all to
infer the labels values; ii) from a certain value of the EI, SN and TF
personality trait it is not possible to infer the labels values. It could
conjectured that low values personality traits have some differentiation
power.

  * Step 6:

a threshold limit of 0.25 was defined for the personality traits values and
several simple algorithms were developed to evaluate their differentiation
power. The first one is based on the total number of low value personality
traits, assuming at least two traits with low values. The following three
algorithms, each focuses on a single trait of low value. The final one
requires that one of the traits has low value.

The obtained results are summarised as follows:

##### Global evaluation:

total number of evaluation _headlines_ : 3516.

  * -

tested condition: if the total number of low value personality traits is
greater than 1 then the predicted label is set to 0; otherwise the predicted
label is set to 1;

  * -

total number of errors: 1674;

  * -

accuracy: 52.39%

  * -

tested condition: if the total number of low value personality traits is
greater than 2 then the predicted label is set to 0; otherwise the predicted
label is set to 1;

  * -

total number of errors: 1792;

  * -

accuracy: 49.03%

  * -

tested condition: if the total number of low value personality traits is
greater than 3 then the predicted label is set to 0; otherwise the predicted
label is set to 1;

  * -

total number of errors: 1796;

  * -

accuracy: 48.93%

##### Low EI values performance:

number of _headlines_ with low EI personality trait values: 55.

  * -

tested condition: if the _headline’s_ EI personality trait value is less than
the threshold limit and the SN and TF values are greater than the threshold
limits then the predicted label is set to 0; otherwise the predicted label is
set to 1;

  * -

number of correct predictions: 37;

  * -

accuracy: 62.27%

##### Low SN values performance:

number of _headlines_ with low SN personality trait values: 364.

  * -

tested condition: if the _headline’s_ SN personality trait value is less than
the threshold limit and the EI and TF values are greater than the threshold
limits then the predicted label is set to 0; otherwise the predicted label is
set to 1;

  * -

number of correct predictions: 210;

  * -

accuracy: 57.69%

##### Low TF values performance:

number of _headlines_ with low SN personality trait values: 582.

  * -

tested condition: if the _headline’s_ SN personality trait value is less than
the threshold limit and the EI and TF values are greater than the threshold
limits then the predicted label is set to 0; otherwise the predicted label is
set to 1;

  * -

number of correct predictions: 299;

  * -

accuracy: 51.37%

##### Low personality traits values global performance:

total number of _headlines_ with low personality trait values: 1001.

  * -

tested condition: if any of the last three tested conditions is satisfied then
the predicted label is set to 0; otherwise the predicted label is set to 1;

  * -

number of correct predictions: 546;

  * -

accuracy: 54.54%

The results suggest that the use of personality traits to predict if a
_headline_ is Real or Fake leads to no better than a random assignment. Even
the 62.27% accuracy obtained for the low EI values case cannot be considered a
good result as just 55 _headlines_ fall into this category.

As the results presented in this section correspond to very simple and
specific criteria, the MTBI approach is further examined in Section 5.

###  4.3 Evaluation of the Lacanian discourses approach

In this section we examine the potential of the Lacanian discourses being used
to predict Real and Fake news from their _headlines_.

At this initial stage, it has been decided to adopt a very simple way to
quantify the presence of each possible Lacanian discourse in an enunciation.
Consider the vector $\mathcal{L}=(M,A,U,H)$ where M, A, U, H stands for
Master, Analyst, University and Hysteric, respectively, and may take the value
1 to indicate the presence of that type of discourse in the enunciation, and 0
otherwise. For example, $\mathcal{L}=(M,A,U,H)=(1,0,1,0)$ indicates that in
the corresponding enunciation traces of Master and University discourses have
been identified.

The following procedure has been adopted to evaluate the Lacanian discourses
approach:

  * Step 1:

blind assignment of the Lacanian discourses. An expert having no access to the
_headlines’_ labels assign $\mathcal{L}=(M,A,U,H)$ to a set of 100
_headlines_.

  * Step 2:

identification of ambiguities. The expert accesses the labels of the 100 used
_headlines_ and verifies if the same values of $\mathcal{L}=(M,A,U,H)$ have
been assigned to _headlines_ with different labels.

  * Step 3:

non-blind re-assignment of the Lacanian discourses to get Zero ambiguities.
For each of the identified ambiguities in Step 2, the expert verifies if it is
possible to assign a psychoanalytically valid alternative value of
$\mathcal{L}=(M,A,U,H)$ to solve the ambiguity.

  * Step 4:

no-blind extension the Lacanian discourses assignment. Using the same criteria
identified in Step 3 to solve the ambiguities the assignment of
$\mathcal{L}=(M,A,U,H)$ was made for additional 200 _headlines_.

Using the aforementioned procedure it was possible to find a partition of the
$\mathcal{L}=(M,A,U,H)$ codes for the 300 _headlines_. Table II shows the
obtained partition.

TABLE II: $\mathcal{L}=(M,A,U,H)$ assignment for Labels equal to 0 and 1. Label = 0 | Label = 1
---|---
M | A | U | H | M | A | U | H
0 | 1 | 0 | 0 | 0 | 0 | 0 | 1
0 | 1 | 0 | 1 | 0 | 0 | 1 | 0
0 | 1 | 1 | 0 | 0 | 0 | 1 | 1
1 | 0 | 0 | 0 | 0 | 1 | 1 | 1
1 | 0 | 0 | 1 | 1 | 0 | 1 | 0
1 | 1 | 0 | 0 | 1 | 0 | 1 | 1
1 | 1 | 0 | 1 | 1 | 1 | 1 | 1
1 | 1 | 1 | 0 |  |  |  |

It can be argued that the use of a non-blind reassignment of
$\mathcal{L}=(M,A,U,H)$ is not fair and is just trick to get zero ambiguity.
It is an understandable argument, however the procedure is justified by the
following reasons:

  1. 1.

Psychoanalysis is not a hard science and different experts adopting different
points of view may arrive at different valid conclusions. The important point
to be emphasised is that a psychoanalytically valid partition of the
$\mathcal{L}=(M,A,U,H)$ codes have been found.

  2. 2.

The length of the _headlines_ is very short, much shorter than a normal
patient’s narrative. In this case the assignment of $\mathcal{L}=(M,A,U,H)$ is
much harder and a second turn of re-assignment is mandatory to arrive at
useful results.

  3. 3.

The assignment of $\mathcal{L}=(M,A,U,H)$ to _headlines_ is quite different
from the identification of the discourse adopted by the patient in a
psychoanalytical setting where besides the narrative, body language, clothes,
emotion representative gestures help the analyst to build a full
representation of the patient.

To illustrate the difficulty of assigning $\mathcal{L}=(M,A,U,H)$ consider the
following _headline_ :

> _Police Turn In Badges Rather Than Incite Violence Against Standing Rock
> Protestors_

to which has been assigned $\mathcal{L}=(M,A,U,H)=(1,0,1,0)$.

The _Master_ assignment is justified by the mention of “Police” that is a
figure of authority. The _University_ assignment is justified because this
_headline_ is disclosing an information, some kind of knowledge. However, it
could be argued that a $\mathcal{L}=(M,A,U,H)=(1,0,1,1)$ assignment is better
because the reference to “Violence” is a trace of a _Hysteric_ discourse. Is
this assignment was adopted nothing would change because both codes are in the
Label = 1 partition. On the other hand, it could also be argued that a
$\mathcal{L}=(M,A,U,H)=(1,1,0,0)$ is better because the verb “Incite” may be
taken more as an opinion and interpretation than a mere disclosure of
information. If this assignment were adopted, the new code belongs to the
Label = 0 partition and an ambiguity would have been introduced.

This simple example shows that the assignment of Lacanian discourses is not an
easy task and requires further investigation.

From Table II a deterministic model to predict the labels can be easily
derived.

The Label should be set to Zero if $\mathcal{L}=(M,A,U,H)$ satisfies Eq. (3),
i.e., the substitution of $\mathcal{L}=(M,A,U,H)$ into Eq. (3) results in 1.

$A.\overline{U}+M.\overline{U}+A.U.\overline{H}$ (3)

The Label should be set to One if $\mathcal{L}=(M,A,U,H)$ satisfies Eq. (4),
i.e., the substitution of $\mathcal{L}=(M,A,U,H)$ into Eq. (4) results in 1.

$\overline{A}.U+\overline{M}.\overline{A}.H+U.H$ (4)

Equations (3) and (4) are minimum, but are not unique. They complement each
other in the sense that if the substitution of the $\mathcal{L}=(M,A,U,H)$
values satisfy (3) they will not satisfy (4), and vice-versa.

Table II and Equations (3) and (4) suggest that the presence of the Analyst
discourse, $\mathcal{L}=(M,A,U,H)=(*,1,*,*)$ almost always means that the
corresponding _headline_ refers to a Real news. On the other hand, the
presence of the University discourse, $\mathcal{L}=(M,A,U,H)=(*,*,1,*)$ almost
always means that the corresponding _headline_ refers to a Fake news.

Finally the assignment of $\mathcal{L}=(M,A,U,H)$ values was extended to
another 300 _headlines_ to allow further evaluation of the procedure in
Section 5.

##  5 Computational Approach

This section describes the Machine Learning (ML) experiments of our approach
to classify and predict whether a _headline_ is Fake or Real. The following
subsections describe the various adopted procedures and showcase the results.

###  5.1 Algorithmic Personality Type (MTBI) Assignment to the Dataset’s
Headlines

Several open-source codes and projects have been developed to extract the
personality types from a text as, for example, the works available in [29] and
[30].

In this work, the dataset file was submitted to the personality traits
assignment algorithm developed by [29], which is implemented in the
programming language Python555https://www.python.org/. The analysis is
performed using linguistic cues like word repetition, number of verbs, nouns,
detection of emotion in text and so on. We are mainly interested in the
application of such a theory and not in the implementation of the theory
itself as it has already been done and evaluated by other researchers as in
[31]. Some of the approaches are able to determine types like E/I, S/N and T/F
with at least 90% accuracy [32] and, more recently, the J/P type with 81% and
65% accuracy, depending on the used dataset [33].

The MBTI algorithm takes as input a text and gives as output 4 groups of
variables (Extroversion/Introversion, Thinking/Feeling and so on) in the range
of [0,1], which represent the dimensions described in Section 3.1, and the
binary classification task of the ML models is to classify the _headlines_
whether they are Real (dataset label is 0) or Fake (dataset label is 1).

The mean of each personality type group is presented in Table III.

TABLE III: Mean, rounded to 2 decimals, of each variable (type) obtained from the algorithm Personality Type | Mean
---|---
Introversion - Extroversion | 0.45 & 0.55
Sensing - Intuiting | 0.43 & 0.57
Thinking - Feeling | 0.33 & 0.67
Judging - Perceiving | 0.49 & 0.51

The distribution of the obtained types is illustrated in Figures 10 (Thinking
- Feeling), 11 (Introversion - Extroversion), 12 (Judging - Perceiving) and 13
(Sensing - Intuiting).

Figure 10: Distribution of types Feeling (mean 0.67) and Thinking (mean 0.33)
Figure 11:  Distribution of types Introversion (mean 0.49) and Extroversion
(mean 0.55) Figure 12:  Distribution of types Judging (mean 0.45) and
Perceiving (mean 0.51) Figure 13:  Distribution of types Sensing (mean 0.43)
and Intuiting (mean 0.57)

###  5.2 MTBI Fake or Real news prediction using ML

We selected four different ML algorithms, namely: i) Support Vector Classifier
(SVC), ii) LightGBM, iii) Extra-Trees-Classifier and iv) GaussianNB (GNB) for
comparison and tested them with the following groups of independent
personality types in an attempt to identify the impact of different
personality types in the field of Fake/Real news detection:

  * •

Type 1: Introversion, Intuition, Thinking, Judging (INTJ)

  * •

Type 2: Extroversion, Sensing, Feeling, Perceiving (ESFP)

  * •

Type 3: Introversion, Sensing, Thinking, Perceiving (ISTP)

Each algorithm is run using default parameters, is trained with 25% of data,
and the results are presented in Table IV. It can be seen that Types 1, 2 led
to 59% accuracy by the LightGBM algorithm, while Type 3 had almost always the
lowest score. The GaussianNB had the worst performance among all. It is
noticeable that hyperparameter tuning didn’t improve the accuracy and didn’t
provide any additional insight about the characteristics of the task and
dataset.

TABLE IV: The summary of the performance of the ML algorithms and the relative achieved balanced accuracy Model | Type 1 | Type 2 | Type 3
---|---|---|---
Extra-Trees-Classifier | 58% | 57% | 56%
LightGBM | 59% | 59% | 58%
SVC | 57% | 57% | 55%
GaussianNB | 54% | 54% | 55%

The confusion matrix derived from the LightGB algorithm, Type 1 experiment, is
shown in Figure 14. It can be seen that the best-scoring model is able to
correctly predict a fake news _headline_ as Fake 30.2% of the time (bottom
right corner of figure), while there is a probability of approximately 22% to
categorize a fake news _headline_ as Real (bottom left). Regarding the real
news label, approximately 27.9% correctly categorize correctly a real news
_headline_ as Real (top left) and 19.8% correctly to categorize a real news
headlines as Fake (top right).

Figure 14: Confusion Matrix of the LightGBM algorithm for the Type 1
experiment

The obtained results confirm what has already been stated in Section 4.2 that
MBTI is not the ideal tool for predicting _headline_ labels, as none of the ML
algorithms achieved an accuracy score much better than a random prediction.

###  5.3 Lacanian Discourses Fake or Real news prediction using ML

This is a two steps procedure:

  * •

Step 1: Assign the Lacanian Discourses to the _headlines_ either manually or
automatically.

  * •

Step 2: Using the results from Step 1, predict the labels of the _headlines_ ,
as Fake or Real.

This gives rise to four possible combinations of discourses assignment and
labels prediction:

  * •

_Manually_ assign the Lacanian Discourses and predict labels using the
deterministic model discussed in Section 4.3 (Section 5.3.1).

  * •

_Manually_ assign the Lacanian Discourses and predict labels using ML
algorithms (Section 5.3.2).

  * •

_Automatically_ assign the Lacanian Discourses using the language model GPT-3
and predict labels using the deterministic model discussed in section 4.3
(Section 5.3.3).

  * •

_Automatically_ assign the Lacanian Discourses using the language model GPT-3
and predict labels using ML algorithms (Section 5.3.4).

For the first two approaches, we randomly selected 600 _headlines_ and
manually assigned the corresponding Lacanian discourses.

In the case of the latter two approaches, we used the aforementioned 600
manually assigned _headlines_ to train the language model GPT-3 (Generative
Pre-trained Transformer 3) in an attempt to achieve automatic Lacanian
Discourses assignment to solve the problem of _Step 1_.

GPT-3 is a neural network machine learning model, developed by OpenAI, trained
using internet data to generate any type of text that enables developers to
train and deploy AI models. It provides a variety of tools and services for
data preprocessing and model training, and its capabilities include but are
not limited to: i) generating, classifying, translating and summarising text;
ii) generating and answering questions; iii) generating images and audio from
text [34] and can be accessed and used on OpeanAI’s main website
666https://openai.com/api/.

####  5.3.1 Manual Lacanian discourses assignment and deterministic model
labels prediction (no ML used)

This case was introduced and implemented in Section 4.3. The accuracy is
almost 100%, however, it is not a practical alternative because the manual
assignment of Lacanian discourse is only feasible for a very small number of
_headlines_. Nevertheless, it is an important theoretical case because it has
shown the existence of a psychoanalytic valid partition of the codes.

####  5.3.2 Manual Lacanian discourses assignment and ML labels prediction

The ML algorithms presented in Section 5.2 were trained and evaluated with the
600 manually Lacanian discourses assigned to the _headlines_. The accuracy of
the labels prediction is summarised in Table V.

TABLE V: Performance of the ML algorithms and the relative achieved balanced accuracy of labels prediction for manually assigned Lacanian discourses Model | Accuracy
---|---
Extra-Trees-Classifier | 97%
LightGBM | 97%
SVM | 97%
GaussianNB | 92%

As expected, the accuracy is slightly decreased compared to the first
approach. It is important to realise that this is not a typical ML scenario as
the dataset is very small and the models may be overfitting: the model fits
perfectly against its training data and thus achieves very high accuracy.

Once again we emphasise that the manual assignment of Lacanian discourses is
rather impractical but it confirms the potential of the psychoanalytic-based
method.

####  5.3.3 Automatic Lacanian discourses assignment and deterministic model
labels prediction

OpenAI777https://beta.openai.com/docs/models/gpt-3 provides an API to work
with four different models: Davinci, Curie, Babbage, and Ada. Each one of them
has different capabilities, advantages and disadvantages, and after some
experimenting we decided to work with the model Ada because of its speed and
its capabilities more adapted to the nature of the classification under study.

We first created a set of classification Ada models using 100, 200, and 500
out of 600 manually Lacanian discourses assigned _headlines_ to evaluate its
accuracy in predicting the discourses of the last 100 _headlines_.

The results of this experiment are shown in Figure 15. It can be seen that as
the model is getting trained with more assigned _headlines_ , it gets better
at classifying and labeling the Master discourse, while it gets worse at
labeling the Analyst discourse and a bit better at labeling the University
discourse when it is trained with 200 headlines, but it ends up at the same
accuracy of 100 assigned _headlines_ when the input is 500 assigned
_headlines_.

Figure 15: The X axis represents the number of manually assigned _headlines_
used to train the classification model; the Y axis represents the accuracy of
predicting the discourses of the last 100 manually assigned _headlines_.

The overall accuracy of exactly predicting exactly the four discourses of the
last 100 _headlines_ is illustrated in Figure 16. It can be seen that the
accuracy is below 30% but with a slight increase as the number of assigned
discourses increases.

Figure 16: The X axis represents the number of manually assigned _headlines_
used to train the classification model; the Y axis represents the accuracy of
exactly predicting the four discourses of the last 100 manually assigned
_headlines_.

Giving the 600 manually assigned _headlines_ as training in the Ada model and
evaluating the whole dataset using the deterministic model described in
Section 4.3, the labels prediction accuracy is only 66%. This is a poor
result, however much better than what was obtained with the MTBI approach, due
to a poor automatic discourses assignment accuracy, as discussed before.

####  5.3.4 Automatic Lacanian discourses assignment and ML labels prediction

Following the same procedure as described in Section 5.3.3, using 100, 300,
and 600 as training points, the results of the ML models labels prediction of
the _headlines_ are shown in Table VI. The Extra-Trees-Classifier, LightGBM,
SVC, and GaussianNB models best accuracy is 66%.

TABLE VI: The ML models’ performance and the relative achieved balanced labels prediction accuracy Model | Number of training points
---|---
| 100 | 300 | 600
Extra-Trees-Classifier | 63% | 64% | 66%
LightGBM | 63% | 64% | 66%
SVC | 63% | 64% | 66%
GaussianNB | 62% | 63% | 65%

The accuracy gets better as the number of training points increases, as shown
in Figure 17.

Figure 17: The X axis represents the number of manually assigned _headlines_
used to train the classification model; the Y axis represents the ML models
dataset labels prediction accuracy.

To estimate how many headlines should be used for training the models to
achieve a 70% accuracy threshold we did a log-fitting of the obtained points
using Eq. (5):

$a\log(bx+1)+0.5$ (5)

.

Figure 18, that is in logarithmic scale, shows the results.

Figure 18: The X axis represents the number of manually assigned _headlines_
used to train the classification model; the Y axis the dataset labels
prediction accuracy of the GNB (blue), SVC (yellow) ML models.

The parameters for the log function are:

  * –

GNB: a = 0.0198352, b = 3.56483;

  * –

SVC: a = 0.0130760, b = 312.088.

The number of points needed to a 70% accuracy threshold is 6700 for the GNB
model, and 14000 for the SVC model. With 1600 points the two models provide
the same 67% accuracy, and after that point the GNB model performs better.

Despite the power of models provided by the OpenAI the problem of
automatically assign the Lacanian discourses remains to be solved to benefit
from the great potential of psychoanalytic-based approach to be used to
extract information from enunciations.

##  6 Conclusions

Different approaches of combining psychological and social dimensions with
computational methods which include computational psycholinguistics,
personality traits, behavioural analysis, emotional states and cognitive
psychology methods have been used to derive information from user-related and
generated data. However, in various contexts where user perception is more
susceptible to emotional or some other form of bias, the reliability of user
data is often compromised.

Compared to all those approaches, the approach proposed in this work is
fundamentally different since we adopt a psychoanalytic perspective; in
particular, we employ the powerful notion of Lacanian discourse types. To the
best of our knowledge, this is the first attempt to systematically bring
together psychoanalysis and computing.

From the experimental point of view, as a preliminary case study scenario, we
looked at the problem of identifying from the _headlines_ of published news if
they are Real or Fake.

To start with, we evaluated the psychological-based approach of deriving the
Real or Fake characteristic from the personality traits associated to each
_headline_. In Section 4, it has been shown that this approach is not better
than a random prediction. This conclusion has been confirmed in Section 5.2,
where different ML algorithms achieved at most 56% accuracy in label
prediction.

Then, we evaluated the psychoanalytic-based approach. It is a two steps
procedure: i) each _headline_ received a Lacanian discourses assignment
$\mathcal{L}=(M,A,U,H)$, where M, A, U, H stands for Master, Analyst,
University and Hysteric, respectively, and may take the value 1 to indicate
the presence of that type of discourse in the enunciation, and 0 otherwise.;
ii) Real or Fake label prediction from the $\mathcal{L}=(M,A,U,H)$.

A partition of the $\mathcal{L}=(M,A,U,H)$ between Real and Fake labels have
been obtained and a deterministic model for label prediction has been derived.
This indicates the possibility of achieving a theoretical 100% accuracy of
label prediction. However, the described procedure is not practical as the
Lacanian discourses assignment was manually done. The results were confirmed
in Section 5.3.

A first attempt to automatically assign the Lacanian discourses was done using
the environment provided by OpenAI. The label prediction accuracy decreased to
66%.

In summary, we conclude that the psychoanalytic-based approach has a great
potential to extract information from user-generated data.

As future work, an automatic procedure to assign the Lacanian discourses will
be developed, and the technique will be applied to other problems rather than
the Real / Fake news detection. The procedure will be extended to non-binary
classification problems and to multimedia user-generated data.

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