=Paper=
{{Paper
|id=Vol-3797/paper3
|storemode=property
|title=
Analysing Linguistic Markers on Fake News to Enhance the Explainability of Deception Detection Systems
|pdfUrl=https://ceur-ws.org/Vol-3797/paper3.pdf
|volume=Vol-3797
|authors=Alba Pérez-Montero
|dblpUrl=https://dblp.org/rec/conf/sepln/Perez-Montero24
}}
==
Analysing Linguistic Markers on Fake News to Enhance the Explainability of Deception Detection Systems==
https://ceur-ws.org/Vol-3797/paper3.pdf
Analysing Linguistic Markers on Fake News to Enhance
the Explainability of Deception Detection Systems
Alba Pérez-Montero
Dept. of Software and Computing Systems, University of Alicante, Apdo. de Correos 99, E-03080, Alicante, Spain.
Abstract
The massive use of social media has increased the ease of dissemination of information. Unfortunately, every type
of information can be massively disseminated (even deceptive information deliberately created to mislead). In this
research we introduce a deep analysis of linguistic cues (i.e., adjectives, pronouns, complex syntax, emotion words,
etc.) that can lead to distinguish which texts can be deceptive. The research focuses on extracting a combination
of features: content-based, context-based, readability, virality and information richness. The objective is to test if
current NLP tools for deception detection can extract in a satisfactory way these features and to examine the grade
of explainability that these systems offer. The methodology starts from a multidisciplinary point of view, focusing
in elaborate an integrative research. To pursue the objective of this research, we also combine both analytical
and empirical methodologies. The expected impact is to enhance the ability to distinguish misinformation by
improving the accuracy and transparency of deception detection systems for everyone.
Keywords
NLP, fake news, readability, explainability, virality, information richness, linguistic markers, inclusive IA, decep-
tion detection
1. Justification of the research
The use of social media has massively multiplied the last years, making very easy the communication
between individuals and the spread of information. According to Gottfried and Shearer [1], nearly
two-thirds of American adults retrieve information via social media. Notwithstanding, every type of
information can spread quickly, even false information. The development of social media platforms has
intensified the diffusion of fake news [2].
The internet not only provides a medium for publishing fake news but also offers tools to actively
promote dissemination [3]. The rapid distribution of fake news is due to the widespread use of social
media which offer a fertile ground for instantly sharing and circulating news with the users having no
means of quality checking over the shared content [4]. This wide dissemination of information has also
been studied as virality. This concept relates to fake news because the more viral a false information is,
the more probable is to cause harm. As Esteban-Bravo et al. [2] show in their research, the potential
virality of fake news can be predicted by analyzing written texts. Their proposal is to implement early
stage strategies that can help to control the dissemination of false information, because once a false
information is spread, debunking it is a major challenge [2].
Moreover, the increasing quantity of information online makes it every time more difficult to individ-
ually analyze it. For this reason, implementation of Natural Language Processing (NLP) techniques and
tools is mandatory. As an example, Esteban-Bravo et al. [2] used machine learning models to classify
fake news by their level of virality. Also Bonet-Jover et al. [5] combined machine learning and deep
learning techniques to create a two-layer model architecture for automatic fake news detection.
To this day, many false information detecting tools have been developed. An example of this is the
Veripol tool created by Quijano-Sánchez et al. [6] in collaboration with the Spanish National Police. It
is a system that detects false reports automatically. Nevertheless, not many researches are focused on
explainability of these tools. The term explainability refers to the ability of a machine learning model
to offer a mechanism by which its decision-making can be analyzed, and possibly visualized [7]. As
Kotonya et al. [7] include in their survey, it is crucial to make every NLP tool sufficiently explainable.
Doctoral Symposium on Natural Language Processing, 26 September 2024, Valladolid, Spain.
$ alba.perezm@ua.es (A. Pérez-Montero)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
In this case, they focus on explanation functionality – that is systems providing claims to support their
predictions. NLP systems need to offer explanations that are actionable, causal, coherent, context-full,
interactive, unbiased, and chronological [7]. For this reason, it is important to bear in mind that every
step in deception detection investigation should be transparent and easily understandable for everyone.
Current technologies are mature enough to provide a sound basis for the development of components
to automatically detect and remove obstacles to reading comprehension[8].
Before going into the next sections, we define some relevant concepts related to false information
that we will use throughout our research:
Fake news: is the term related to fabricated information that mimics news media content in form
but not in organizational process or intent [3].
Deception: is the term related to white lies, omissions, and evasions to bald-faced lies and misrepre-
sentations [9], that is to say, messages transmitted with the objective of creating a false information
different from the verifiable reality.
Misinformation: is the term related to the factually incorrect or misleading information that is not
backed up with evidence [10].
Disinformation: is the term that involves misleading information knowingly being created and
shared to cause harm [11].
In this PhD thesis we mainly focus on the term deception because its intention to confuse the receiver
leaves a "linguistic impression" that can be analyzed. Thus, different research works use that terms to
differentiate nuances of meaning regarding to the writer intention or the background information that
is available.
The motivation of this research arises from the need to unify and compile different approaches to
enhance deception detection and to revise exlpainability of deception detection systems with the aim to
make these systems more accesible and inclusive.
2. Background and Related work
Previous studies have tried to delimit the linguistic markers that allow the detection of the falsehood or
veracity of a message. In 2019, Gravanis et al. [4] review the most complete classifications of linguistic
cues to deception. In this study, they mainly focus on analyzing three taxonomies of linguistic cues to
deception: [12], [13] and [9].
As a result, they extract 27 linguistic markers that respond to dimensions such as complexity,
expression of uncertainty, expressiveness, or degree of formality, among others.
However, the topic of linguistic cues to deception started some years before. From the beginning, in
2003, DePaulo et al. [14] elaborated a exhaustive experiment with participants who were instructed
to write false statements of true statements. This experiment allowed the researches to analyze false
and truthful texts and they extracted 158 cues to deception. This study is based on a psychological
perspective, but it has laid the groundwork for subsequent researches in linguistic analysis of false
statements.
In 2004, Zhou et al. [9] conducted another experiment with participants from which they extracted
9 linguistic constructs: quantity, diversity, complexity, specificity, expressiveness, informality, affect,
uncertainty, and non immediacy.
On their part, Hauch et al. [15] offer a meta-analysis on the linguistic markers of deception. They
review 44 previous works and extract 79 markers, examining each of them to determine whether
they are really discriminatory between false and true information. Their research results show that
constructs as the expression of certainty, expression of emotions, distancing from what it is being said,
details and expression of cognitive processes should be taken into consideration in order to analyze
deceptive texts.
More recently, in 2020, Santos et al. [16] proposed a new taxonomy of linguistic cues that includes
also readability features. As they affirmed, readability features are formed by branches of features from
other linguistic levels, such as morphological, syntactic and semantic, so the robustness of these features
Table 1
Review of studies on linguistic cues to deception and misinformation.
Authors Date Title Criteria
Length, Complexity, Unique Words,
B. M. DePaulo, et al. 2003 Cues to Deception
Sensory Information, etc.
Automating Linguistics-Based Cues
Specificity, Expressivity,
L. Zhou, et al. 2004 for Detecting Deception in Text-Based
Uncertainty, Affect, etc.
Asynchronous Computer-Mediated Communications
Are Computers Effective Lie Detectors?
V. Hauch, et al. 2014 Mistakes, Expressivity, Emotions, etc.
A Meta-Analysis of Linguistic Cues to Deception
Measuring the Impact of Readability Features Readability Index, Concreteness,
R. Santos, et al. 2020
in Fake News Detection Familiarity, etc.
Linguistic characteristics and the dissemination
Persuasive Words, Emotions,
C. Zhou, et al. 2021 of misinformation in social media:
Comparative Words, etc.
The moderating effect of information richness
Predicting the virality of fake news Readability, Pronouns, Informatily,
M. Esteban-Bravo, et al. 2024
at the early stage of dissemination Affect, etc.
could be differential in identifying different writing styles in fake news. In this regard, readability
features are markers related to complexity.
Moreover, Zhou et al. [11] approach this question trying to discriminate if the quality and details
of information can be useful in detecting false information. They studied persuasive, comparative,
emotional and uncertainty words in the misinformation dissemination process. They also analyze if
misinformation dissemination is stronger when it includes multimodal content. Their contribution is
relevant because they categorized three levels of richness in online information: level 1 for text-only,
level 2 for text with image, and level 3 for text with video.
In the most recent study, from 2024, Esteban-Bravo et al. [2] show that to analyze fake news it is
important to consider multiple features: writing style features, readability/complexity features, and
psychological features. Their contribution is a proposal of classification of levels for virality in the
social network X (formerly Twitter): 50 retweets, between 50 and 1000 retweets, between 1000 and 5000
retweets, and more than 5000 retweets, which is the viral category.
These studies helps us to initiate a complete and detailed taxonomy of linguistic markers to detect
deception and they provide us a valuable reference point to improve the state of the art in this topic for
English. A review is made considering date, topic and the criteria they extracted of the researches, as
can be seen in Table 1.
Previous works do not explore further in the linguistic principles or contextual variables that can infer
in the interpretation of false information or analysed different languages. In this case, it is necessary
to add a deeper linguistic point of view in order to elaborate a generalizing taxonomy of linguistic
characteristics that could be applied to more than one language, textual genre or modality.
As Bonet-Jover et al. [5] proved, fake news combines true and false data with the intention of
confusing readers. In this study they analyze digital media by using the traditional journalistic structure
of news 5W1H (What, Who, Where, When, Why and How). They also demonstrate that determining the
veracity of each 5W1H component using only textual information has a limited prediction performance,
so adding high-level features like fact-checking information, semantic relations between components
or contextual features would be beneficial.
On its part, Saquete et al. [17], elaborate a review about fake news detection from the NLP perspective.
They point out that there are different subtasks within fake news detection: deception detection, stance
detection, controversy and polarization, automated fact-checking, clickbait detection and credibility
scores. However, in all cases they indicate that it is necessary to create both resources and standardized
and balanced evaluation metrics that can be applied to every subtask.
On the other hand, conferences frequently include workshops that are competitions to evaluate NLP
tasks. For fake news detection it is relevant the CheckThat! Lab, part of the 2021 Conference and Labs
of the Evaluation Forum (CLEF). Nakov et al. [18] present an overview of the lab, whose main objective
was to evaluate technology supporting tasks related to factuality in five different languages. This lab is
divided in different tasks: check-worthiness estimation, detecting previously fact-checked claims and
fake news detection. More than 130 teams participated and created resources to test their technologies,
what makes a invaluable source of resources and references that can be used to improve the NLP state
of the art.
Besides conference labs, several researches create specific datasets in order to extract information in
a specific domain that can be used to perform different NLP tasks. Therefore, some of the datasets are
publicly available and can be used by researchers. As an example, MultiFC [19] is a corpus collected
from 26 fact-checking websites in English, including metadata as well as evidence pages of reference.
For languages different than English, ForceNLP [20] compile a corpus of news mainly from Mexican
web sources in Spanish. Santos et al. [16] used the corpus created by [21] called Fake.Br Corpus. It was
collected by crowdsourcing and has been used in several researches.
After reviewing previous researches, it becomes clear that the analysis of deception detection is
approached from different disciplines that can entwine and work together to perform a complete a wide
scope definition and description of the topic:
• Linguistics: The studies made from this discipline focus on the analysis of words, sentences and
texts. It looks for words or structures that relate to truthfulness or falsehood, what we can also
understand as modalization or subjectivity marks, that is to say, the linguistic elements that are
present in discursive activity, indicating the attitude of the speaking subject with respect to his
interlocutor and his own utterances [22]. Researches relevant in this field are [14] and [4].
• Psychology: The studies made from this discipline focus on techniques or metrics that analyze
people’s behavior (extralinguistic information) to determine whether they are telling the truth or
lying. In this field, it should be highlighted the research of [23].
• Sociology: The studies made from this discipline focus on the sociological analysis of social
media to extract cues of veracity or falsehood. It is related to the NLP task of fact-checking.
Researches of [3] and [24] are relevant in this field.
• Computer Science / Natural Language Processing: The studies made from this discipline
focus on the development of tools and techniques that enable to automate the detection and
analysis of deception. Relevant researches on this field are [4], [15] and [9].
As can be seen, every discipline provides a valuable approach to deception detection. The combination
of different points of view can help to improve our research and offer a more comprehensive and multi-
level perspective. As Gravanis et al. [4] proved in their research, psychologists in cooperation with
linguistics experts and computer scientists revealed that the potential deceivers use certain language
patterns.
Nevertheless, previous researches primarily present two gaps: (1) they address the extraction of
features from an particular discipline or approach (2) they focus in the impact of misinformation in the
wide public and does not pay attention to the explainability. These different approaches had not been
taken into consideration in a unifying research. It is necessary to fill in the gaps within disciplines and
create a continuum between them, i.e., learning how intentions are embodied in discourse, examining
the way NLP techniques can extract pragmatic information or how sociological theory can be applied
to fake news detection.
For this reason, our expected impact is to enhance the ability to distinguish misinformation, by
unifying and compelling different approaches, and to revise transparency and explainability of deception
detection systems for everyone.
3. Main Hypothesis and Objectives
The main hypothesis that introduces this PhD thesis is that it is possible to delimit a generalizing
taxonomy for deception markers that can be applied to more than one language, textual genre or
discursive modality.
Subsequently, the main objective is to detect deception from written texts automatically extracting
content-based features, contextual-based features, readability features, virality features, and linguistic
richness features (task 1). The extraction of these features had been studied separately, but not as an
integrative study like in this thesis proposal. In addition, our aim is to analyze existing NLP systems that
include text generation to justify whether it is false or true information, focusing on its explainability
to build an inclusive artificial intelligence (task 2).
As Bonet-Jover et al. [5] explain, the research community is approaching the deception detection task
focusing in extracting content-based features or context-based features. In our research we try to unify
and interrelate both types of features, in addition to readabality and virality features. It is necessary an
integrative approach because a piece of misinformation contains physical content (such as body text,
picture or video) and nonphysical content (such as emotion, opinion or feeling) [25].
Basically, the research will focus on analyzing linguistic cues to deception detection. Based on this,
we will find some sub-objectives that will be part of the first task (O1, O2, O3, O4), others that will
respond to the second task (O5) and others that will be common (O6, O7).
The specific scientific sub-objectives are presented below:
O1.To collect and analyze information about linguistic cues (content-based, context-based markers,
virality degree and readability features) that are present in the deceptive texts. We will mainly focus in
the researches of [9], [11], [14], [15], [16] and [2].
At this point, virality features can be analyzed as a complementary element. A deceptive message is
deceptive not for its probability to go viral, but for its own characteristics. However, could be interesting
to study at the same time if it exists any relationship between false information and a hidden intention of
the sender to go viral. As Saquete et al. [17] shows, dissemination of false information can be motivated
by ideological or economic interests. For this reason, virality is considered as a feature, but it is not the
center of our investigation.
O2.Linking the approaches showed in previous researches, to develop a generalizing marker classi-
fication that can be applied to more than one language, textual genre and modality. We analyze the
current researches to unify and extract the features that are relevant to this topic.
O3. To extract the methodology used in previous NLP tools for the deception detection purpose. It is
necessary to revise researches, competitions and available datasets.
O4.To develop a methodology to assess the degree of deceptiveness/credibility of an information.
O5.To employ and test NLP tools that analyze text to detect deception. Analyze if they present a
sufficient degree of explainability that provides a clear and universally accessible justification for the
veracity or falsity of the information. It is necessary to implement explainability measures in NLP
systems that can help every person to understand the reasons why an information is deceptive or
believable in an autonomous way.
O6.To obtain results and compare them with the state of the art. Recognize weak points and implement
improvements in the research, both in terms of features to detect deception and in terms of explainability
degree.
O7.To carry out scientific dissemination of the processes and results obtained from the research
throughout the development of the PhD thesis.
The time planning is divided into four years. As a summary, we show in which objectives the research
will be focused during this project, as can be seen in Figure 1.
4. Methodology
The methodology used in this work starts from a multidisciplinary point of view, focusing in elaborate
an integrative research. As it was said before, the study around deception converges linguistic, psycho-
logical, sociological and computational approaches. To pursue the objective of this research, we will
also combine both analytical and empirical methodologies.
On the one hand, our methodology focus in carrying out an exhaustive analysis of the discourse in
relation with different variables, which can provide a wider view of linguistic markers to apply them
Figure 1: Gantt Chart that presents the time planning of the PhD research.
in NLP tasks. Following previous work, to extract linguistic cues it is necessary to work with written
texts, preferably online resources that can be compiled easily. As happened in similar researches [2],
the compilation of images is a limitation of the study. This integrative analysis extracts, compiles and
test which features are relevant to take into consideration at detecting deception in written texts. The
main goal is to find relevant and distinctive markers that can be generalizing in different languages,
textual genres or modalities.
On the other hand, we present an empirical methodology in which we carry out a process of
experimentation centered in the implementation of existing NLP systems for deception detection and
test their accuracy. After that, our examination focus on their explainability, that it to say, how NLP
tools display their information and outputs to build an inclusive understanding of NLP tools.
Therefore, the study is approached from a conjunction between exploration and action to create
a solid theoretical foundation that can also be put into practice, and ending with a conclusion phase
where the results obtained are evaluated quantitatively and qualitatively.
5. Research issues to discuss
To determine primary research issues of this PhD thesis, we used the ABC of systematic literature
review [26]. In this survey, they introduce various research question development tools. These are
mainly applied to health science, but we can use them to create research questions that are relevant to
establish the basis of this PhD thesis.
To begin the research process of this PhD thesis establishing the following research questions:
• RQ1: Is there a relationship between certain linguistic markers (word classes, verb tenses, pronoun
usage, syntax, etc.) and the expression of truthfulness/falsehood?
As many studies have shown, it is possible to extract falsehood or veracity of a written text from
its linguistic components [9], [14] or [4], among others. However, a compilation and improvement
of a classification is an unfinished task.
• RQ2: Is it possible to create a methodology for falsehood detection that is generalizing (different
textual typologies, registers and contexts), unbiased and applicable?
As we introduced before, we focus in the researches [9], [11], [14], [15], [16] and [2]. Based on
the information collected from this researches (displayed at Table 1), it is possible to collect all the
linguistic deception cues and create a preliminary taxonomy for our research. After analyzing
which cues are repeated or similar, the classification is as follows:
– Expressiveness: terms referring to any type of expression of emotions, mental images or
affection (positive or negative).
– Quantity: referring to any type of measurement related to words or sentences. It is a
content-independent variable, it is only quantifiable.
– Complexity/readability: measured by unique words, complex syntax, etc. There are tools
or algorithms that can calculate readability index.
– Cognitive processes: referring to expressions of internal thinking or perceptual/sensory
processes.
– Certainty: referring to terms that show uncertainty, certainty or concreteness. This
construct can contain two more concrete variables: specificity (what is more specific shows
more certainty), and immediacy (when this specificity is related to time or space).
– Participation: referring to expression of participation or distancing from what is being
said. Mostly use of pronouns (autorreference or outer-reference).
– Informality: measured by mistakes, punctuation marks, etc.
– Virality: measured by the classification by Esteban-Bravo et al. [2].
– Information richness: measured by the classification by Zhou et al. [11].
• RQ3: Is it possible to generate justifications for the veracity or falsehood of a text that are under-
standable and accessible to everyone?
As Kotonya et al. [7] show, explainable machine learning shows a great deal of promise despite
the particularly challenging nature of the problem. This study shows that is necessary to continue
the research on the explainability and accessibility of NLP tools.
• RQ4: Can detection of false information help people to avoid being misled, but especially people
with some difficulty to investigate autonomously whether an information is truthful or not?
As Moreda et al. [8] affirm in the CLEAR.TEXT project, it is important to secure the ability to
access written information for all people, thereby reducing the risk of exclusion for those with
cognitive disability.
• RQ5: Is there any relationship between fake news and virality?
As Vosoughi et al. [24] proved, falsehood diffuses significantly farther, faster, deeper, and more
broadly than the truth. However, this report arises from a exclusively sociological approach,
leaving behind the analysis of linguistic features that can motivate the spread of information
(simpler syntax, briefer sentences, more common words, etc.).
Acknowledgments
This research work is part of the project “NL4DISMIS Natural Language Technologies for dealing with
dis- and misinformation” (CIPROM/2021/21) (funded by Generalitat Valenciana (Conselleria d’Educació,
Investigació, Cultura i Esport)), and of the R-D projects “CORTEX: Conscious Text Generation” (PID2021-
123956OB-I00) (funded by MCIN/ AEI/10.13039/501100011033/ and by “ERDF A way of making Europe”).
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