=Paper=
{{Paper
|id=Vol-3762/549
|storemode=property
|title=Artificial intelligence tools in the ongoing fight against bullying and cyberbullying: a multidisciplinary approach
|pdfUrl=https://ceur-ws.org/Vol-3762/549.pdf
|volume=Vol-3762
|authors=Giulia Orrù,Vincenzo Gattulli,Guido Colaiacovo,Stefano Marrone,Giovanni Puglisi,Lucia Sarcinella,Grazia Terrone,Donatella Curtotti,Donato Impedovo,Gian Luca Marcialis,Carlo Sansone
|dblpUrl=https://dblp.org/rec/conf/ital-ia/OrruGC0PSTCIMS24
}}
==Artificial intelligence tools in the ongoing fight against bullying and cyberbullying: a multidisciplinary approach==
https://ceur-ws.org/Vol-3762/549.pdf
Artificial intelligence tools in the ongoing fight against
bullying and cyberbullying: a multidisciplinary approach
Giulia Orrù1,* , Vincenzo Gattulli2 , Guido Colaiacovo4 , Stefano Marrone3 , Giovanni Puglisi1 ,
Lucia Sarcinella2 , Grazia Terrone5 , Donatella Curtotti4 , Donato Impedovo2 ,
Gian Luca Marcialis1 and Carlo Sansone3
1
University of Cagliari, piazza d’Armi, 09123, Cagliari, Italy
2
University of Bari, Via Edorado Orabona 4, 70121, Bari, Italy
3
University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy
4
University of Foggia, Via Antonio Gramsci 89, 71122 Foggia, Italy
5
Tor Vergata University, Via Columbia 1, 00133 Roma, Italy
Abstract
The problem of bullying and cyberbullying is growing and is a phenomenon that negatively influences our society: for this
reason, it requires advanced solutions aimed at prevention. Starting from a previous project called BullyBuster which paved
the way for the application of artificial intelligence (AI) in this area, exploiting interdisciplinary skills to develop algorithms
capable of identifying and mitigating bullying behaviours, in this paper, we present the follow-up, called BullyBuster 2
(BB2). BB2 aims to broaden the spectrum of intervention to include adult populations, reflecting the universal nature of
bullying across all age demographics. Furthermore, it updates and improves the original project’s methodologies with new
psychological insights and broader AI applications, ensuring a more inclusive anti-bullying strategy. Furthermore, BB2 aims
to form a large research consortium to set a new standard for interdisciplinary collaborations in preventive and response
strategies against such social challenges.
Keywords
bullying, detection, cyberbullying, artificial intelligence, interdisciplinarity
1. Introduction bullying in both physical and virtual contexts and for
this reason it has been included in the Global Top 100 list
The worrying rise in bullying and cyberbullying in re- of AI projects addressing the 17 United Nations Strate-
cent years has brought attention to the urgent need for gic Development Goals by the International Research
efficient prevention measures and solutions [1]. In fact, Center for Artificial Intelligence under the auspices of
such violent behaviours not only compromise individual UNESCO1 .
feelings of security and dignity, but they also have signif- This paper introduces “BullyBuster 2 – the ongo-
icant psychological effects that may reverberate across ing fight against bullying and cyberbullying with the
society. Bullying has changed throughout time and is help of artificial intelligence for the human wellbeing”
now prevalent in a variety of adult demographics and project (BB2, hereinafter), the follow-up to the Bully-
settings, which has made it clear that creative and flexible Buster project. Funded by the European Union - NextGen-
solutions are required. erationEU and within the PRIN 2022 PNRR, it again in-
In this context, the “BullyBuster - A framework for volves four multidisciplinary research groups belonging
bullying and cyberbullying action detection by computer to four universities in Southern Italy (University of Bari
vision and artificial intelligence methods and algorithms” Aldo Moro, University of Cagliari, University of Foggia,
project [2](BB1 in the following) was proposed: funded University of Naples Federico II) with the aim of combin-
under the tender relating to Projects of Relevant National ing artificial intelligence, technology, law and psychology
Interest (PRIN) in 2017 and recently concluded, it marked skills to develop a comprehensive framework.
a pioneering effort by interdisciplinary teams from four BB2’s goal is to advance bullying and cyberbullying
universities in Southern Italy to tackle this threat through detection capabilities by incorporating advanced models
cutting-edge artificial intelligence tools [3]. This initia- that take into account a broader range of victim profiles
tive sought to combine psychological insights with tech- and refining its detection algorithms to accommodate
nological advances to create tools capable of identifying new forms of interpersonal aggression and IT misconduct.
BB2 also aims to broaden the objectives of the previous
Ital-IA 2024: 4th National Conference on Artificial Intelligence, orga-
nized by CINI, May 29-30, 2024, Naples, Italy
*
Corresponding author. 1
https://ircai.org/top100/entry/bullybuster-a-framework-for-
$ giulia.orru@unica.it (G. Orrù) bullying-and-cyberbullying-action-detection-by-computer-vision-
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0). and-artificial-intelligence-methods-and-algorithms/
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
2.1. Legal, juridical and psychological
aspects
Bullying and cyberbullying activities, characterised by
aggressive and arrogant attitudes, repeated over time
and perpetrated to the harm of children, adolescents,
and even adults, have become a critical emergency [4].
Despite legislative efforts [5], research into protecting
individuals and their well-being prior to the occurrence
Figure 1: BullyBuster2 project logo.
of an incident through the prevention of bullying and cy-
berbullying behaviours is still in its early stages. In fact,
intervening in the preventative phase serves to protect
BB1 to include adults, reflecting the broader spectrum the subject’s psychophysical integrity and avert victimi-
of bullying victims. This initiative is driven by recent sation. To ensure the individual’s safety, law enforcement
social challenges and legislative demands, recognizing activities focused at punishing the perpetrator after the
that bullying is a pervasive threat not limited to young damaging event has happened are no longer sufficient.
people but a general public health issue. However, it is critical to respond before a potentially
A further objective of the project is the creation of an detrimental or dangerous occurrence affects the victims.
interdisciplinary reference laboratory to combat the phe- The methodological approach that BullyBuster adopts
nomenon of bullying and cyberbullying. This laboratory, is based on the creation of technological tools to com-
in which expert technologists, jurists, psychologists, so- bat the phenomenon in an integrated approach, both
ciologists and economists will participate, will contribute teleologically oriented towards the protection of psycho-
to greater dissemination and expansion of the research logical profiles based on the well-being of the subject and
community in the field, including stakeholders such as attentive to legal aspects, with a view to prevention.
institutions and companies and subjects potentially in-
terested in the engineering of anti-bullying tools.
2.2. Multimedia contents artifacts
The term "deepfake" refers to any way of creating fake
2. The BullyBuster framework multimedia material representing one or more individ-
uals [6]. The modification may specifically target the
In this section, we explore the key elements of Bully-
individual’s identity, speech, or expression. Although
Buster’s architectural framework (Fig. 2), which includes
this technology is of considerable appeal to some sectors
the tools created in BB1 as well as the enhancements
of society, such as the film and video game industries, the
planned for BB2. The architecture of the system is struc-
harmful applications are vast and particularly concerning.
tured into five primary modules: (1) a manipulated mul-
In fact, deepfakes may position a person in non-real hu-
timedia content detection module, to combat the spread
miliating or compromising situations, such as scenes of
of malicious and fake multimedia content, as deepfakes;
sex or violence, or force her/him to say something she/he
(2) a physical violence detection module, to monitor and
never said. Deepfake can thus be used to insult, stalk,
identify potential bullying incidents in the physical do-
propagate revenge pornography, and abuse or cyberbully
main; (3) a stress detection module, able to assesses the
someone. In BB1, we exploited the complementarity of
emotional state of individuals by analyzing the timing
several individual video deepfake detectors with appro-
and rhythm of keystrokes and other behavioral biomet-
priate fusion rules to increase the generalization ability
rics; (4) a verbal abuse detection module, focused on
of modern deepfake detection systems [7]. The goal of
the analysis of textual communications to detect verbal
BB2 is to extend the deepfake video detection capabili-
signs of bullying or cyberbullying; (5) a internet addic-
ties and add voice cloning detection to make multimedia
tion detection module, to detect psychological malaise by
content analysis complete.
automatic behavioral analyses. When combined, these
components provide a complete system that is suited to
address the complex problem of bullying in both physi- 2.3. Anomaly detection of events
cal and digital scenarios. In order to address the critical
The battle against bullying may be greatly aided by hav-
issues of privacy and data protection, this solutions have
ing a visual understanding of human behaviour through
been examined from a legal and juridical perspective.
video analysis. Behavioural "indicators," whether in the
Below, we describe the individual modules of the Bully-
form of victimisation or bullying, might include physi-
Buster framework.
cal aggression, seclusion, or other physical patterns like
encircling. In BB1, systems have been created that can
Figure 2: The BullyBuster 2 project framework.
use one or more surveillance cameras to monitor groups [10]. The goal was to develop a system that automati-
of people who are not individually identifiable [8]. The cally identified aggressive behaviour in texts, especially
data is then processed to report "anomalous" events like in Italian-language comments. To do this, the language
violent or panicked episodes based on behavioural mod- patterns used in aggressive comments were analyzed,
els that have been suitably codified. In BB2, the crowd noting the frequent use of vulgar and negative language,
analytics model will specialize in bullying contexts by insults, and offensive words. In addition, many of these
generating synthetic data: obtaining contextualised train- comments began with "no/not," which is used to contra-
ing data proved to be a challenge during the creation of dict or deny a statement. Based on these observations,
the crowd analyzer in BB1, made worse by the start of several features were developed for feature engineering,
the COVID-19 epidemic, which made it impossible to including the number of negative words, the number of
mimic actual bullying scenarios. In addition, a substan- "no/not," use of capitalisation, positive/negative comment
tial amount of video is needed for deep-learning models. weight calculated with WordNet and SentiWordNet, use
In BB2, we aim to generate a sufficient amount of data of the second person, presence of threats and bullying
and to include the typical behaviours of bullies and vic- terms [10]. These features were used to create several
tims, highlighted in the psychological analysis, through dictionaries of terms and a dataset of aggressive and non-
the synthetic creation of this data. aggressive comments. Several shallow learning models
were tested to evaluate the effectiveness of these features
2.4. Verbale abuse models in detecting verbal aggression [10].
In the context of the BB2 project, this phenomenon
Cyberbullying is a widespread phenomenon involving can also be analysed by considering different age groups,
the use of digital technologies to perpetrate aggressive especially the preponderance of adults. Adults may re-
behavior, threats, or harassment toward other individu- ceive significant criticism, fueling a cycle of online neg-
als. This bullying occurs primarily through online plat- ativity and conflict that can also be reflected in real life.
forms, such as social media, instant messaging, and fo- BB2 aims to address this problem by implementing and
rums. It can take many forms, including name-calling, improving existing text models, with particular atten-
defamation, social exclusion, and disclosure of private tion to the users’ ages. While BB1 primarily addressed
information. Cyberbullying can have serious emotional, adolescents, BB2 includes an analysis of adult language,
psychological, and social consequences on victims and recognizing that verbal aggression and communication
poses a significant challenge for parents, educators, and dynamics are not limited exclusively to the younger gen-
mental health professionals in ensuring a safe and re- eration. BB2’s approach is based on analyzing textual
spectful online environment for all users. One of the dynamics found in social media comments, examining
most predominant vectors is textual language [9]. how different users interact and communicate. This ap-
In the BB1 project, UNIBA focused on identifying ver- proach allows for the identification of common linguistic
bal aggression, a crucial aspect of cyberbullying that pri- and behavioral patterns. The categories used are simi-
marily involves texts and comments on online platforms
larly taken by assigning the label of cyberaggression or 2.6. Digital devices addiction
non-cyberaggression. Through this in-depth analysis,
Examining data from mobile devices has emerged as a
BB2 aims to provide users, social platforms, and mental
prominent approach for understanding individuals’ be-
health professionals with a better understanding of how
havior in today’s digital era [17]. However, the abun-
verbal aggression occurs online and the factors that in-
dance of features on these devices, such as web browsing,
fluence it. This can enable the development of targeted
online gaming, digital photography, GPS navigation, and
interventions to prevent and address cyberbullying, im-
various social applications, can easily engage users’ at-
prove online safety, and promote a healthier and more
tention, potentially causing significant distractions from
respectful virtual environment for all users.
real-world activities. Unfortunately, excessive engage-
The text also fits within the framework of the Behav-
ment with these features can lead to various health is-
ioral Biometrics approach, in which touch-related fea-
sues, particularly psychological challenges, such as lack
tures, called Touch Dynamics, are extracted along with
of self-control, withdrawal symptoms, sleep disruptions,
textual output. This could be a starting point for future
social isolation, depression, and difficulties in maintain-
work [11].
ing focus. Additionally, users may display symptoms of
irritability, restlessness, stress, and mood fluctuations. In
2.5. Stress detection response to these concerns, the goal of BB2 is to introduce
Biometrics involves the utilization of body measurements an innovative module aimed at assessing individuals from
and statistical analysis to extract and quantify human a psychological standpoint, based on their interactions
characteristics [12]. Initially utilized for user authentica- with different features of their mobile devices.
tion and identification purposes [13], , this technology is
now increasingly utilized across various domains, includ- 3. The interdisciplinary laboratory
ing entertainment and personalized user experiences [14].
Within the field of biometrics, there exist two distinct cat- Based on the results of the completed BB1 project and the
egories of approaches: i) Physiological biometrics, which ongoing BB2 project, an interlaboratory group named
involves the direct measurement of physical attributes "BullyBuster" is being formed with the goal of attracting
such as facial features, fingerprints, iris patterns, reti- membership and engagement from individuals who are
nal structures, and vocal characteristics; ii) Behavioral particularly interested in solving the issue of bullying and
biometrics, which focuses on capturing specific human cyberbullying. The interdisciplinary laboratory brings to-
behaviors such as handwriting, typing, or speaking. gether specialists from various fields to develop strategies
Among various forms of behavioral biometrics, for analyzing bullying and cyberbullying behaviours, ex-
keystroke dynamics has emerged as a highly efficient tending beyond just school-aged individuals. This team
and economical technique, easily deployable using read- includes: (1) experts in information security, computer
ily available hardware. In recent years, it has seen in- vision, and artificial intelligence, to create and propose
creased utilization for user authentication, analyzing the innovative models and methods to be implemented in
habitual rhythm patterns exhibited while typing on both appropriate demonstration products; (2) psychologists,
physical and virtual keyboards [15]. to understand the behaviour patterns of individual in-
In the context of combating cyberbullying, we employ volved in bullying events; (3) sociologists, to contribute
keystroke dynamics for user emotion recognition. The by examining the effects of bullying and aggressive be-
aim is to exploit its potential as a cost-effective and widely haviours in social media and other environments; (4) ju-
accessible method for emotion recognition, requiring ridical and legal professionals, to explore new legislation
only a standard keyboard as hardware [16]. Furthermore, that can address (cyber)bullying, enabling both preventa-
since a keystroke recorder can be implemented as hard- tive and punitive technological solutions; (5) economists
ware or software, with the latter being inconspicuous, and healthcare professionals, to assess the financial and
individuals using the keyboard must be made aware of health impacts of (cyber)bullying, whether stemming
the monitoring process. In BB2, keystroke dynamics will from the attacks themselves or from the origins of such
be examined in real-world settings, considering both key- behaviours. The laboratory aims to create a genuine sup-
board inputs and tapping habits to characterize a subject port system and reference services for preventing and
based on their mobile device usage behavior. Addition- fighting bullying.
ally, this analysis can incorporate touch dynamics on
mobile devices.
4. Conclusions
Bullying and cyberbullying are significant societal issues
that have an adverse impact on people and communities.
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