TY - GEN
T1 - Modelling Responsible Digital Security Behaviour for Countering Social Media Manipulation
AU - Maathuis, Clara
AU - Chockalingam, Sabarathinam
N1 - Publisher Copyright:
© the authors, 2023. All Rights Reserved.
PY - 2023
Y1 - 2023
N2 - While the digital environment, and in particular social media, surrounds not only human's identity and its societal functions projection, e.g., institutional and financial aspects, it also captures both individual and collective thoughts regarding former, ongoing, and future concepts, trends, and incidents placed in the physical world, in the digital environment, or in both which could impact both individual and collective consciousness, behaviour, and attitude towards different dimensions of reality. Accordingly, an initial attempt to define and model responsible digital security behaviour was made and ongoing discourses and AI-based solutions for tackling and containing social manipulation mechanisms exist in this domain. Noteworthily is that dedicated attention to understanding and modelling responsible digital security behaviour in social media for tackling and/or countering social media manipulation, e.g., disinformation and misinformation, still lacks. To this end, this research aims (i) to capture the factors influencing user behaviour towards tackling and/or countering social media manipulation, (ii) to build a Machine Learning model that assesses user's responsibility in relation to tackling and/or countering social media manipulation mechanisms, and (iii) propose a set of socio-technical recommendations for building resilience to such mechanisms. To accomplish these research objectives, a Design Science Research methodological approach is taken by designing, developing, and evaluating the model proposed through exemplification. Finally, this research aims to enhance digital security awareness and resilience to social media manipulation of users and policy decision-makers to manage and further extend in a responsible and safe way the digital environment.
AB - While the digital environment, and in particular social media, surrounds not only human's identity and its societal functions projection, e.g., institutional and financial aspects, it also captures both individual and collective thoughts regarding former, ongoing, and future concepts, trends, and incidents placed in the physical world, in the digital environment, or in both which could impact both individual and collective consciousness, behaviour, and attitude towards different dimensions of reality. Accordingly, an initial attempt to define and model responsible digital security behaviour was made and ongoing discourses and AI-based solutions for tackling and containing social manipulation mechanisms exist in this domain. Noteworthily is that dedicated attention to understanding and modelling responsible digital security behaviour in social media for tackling and/or countering social media manipulation, e.g., disinformation and misinformation, still lacks. To this end, this research aims (i) to capture the factors influencing user behaviour towards tackling and/or countering social media manipulation, (ii) to build a Machine Learning model that assesses user's responsibility in relation to tackling and/or countering social media manipulation mechanisms, and (iii) propose a set of socio-technical recommendations for building resilience to such mechanisms. To accomplish these research objectives, a Design Science Research methodological approach is taken by designing, developing, and evaluating the model proposed through exemplification. Finally, this research aims to enhance digital security awareness and resilience to social media manipulation of users and policy decision-makers to manage and further extend in a responsible and safe way the digital environment.
KW - Bayesian networks
KW - cyber operations
KW - cyber security
KW - digital security
KW - digital security behaviour
KW - information operations
KW - responsible security
UR - http://www.scopus.com/inward/record.url?scp=85163196954&partnerID=8YFLogxK
U2 - 10.34190/ecsm.10.1.1079
DO - 10.34190/ecsm.10.1.1079
M3 - Conference Article in proceeding
AN - SCOPUS:85163196954
SN - 9781914587658
T3 - Proceedings of the European Conference on Social Media, ECSM
SP - 144
EP - 152
BT - Proceedings of the 10th European Conference on Social Media
A2 - Dr Iwona Lupa-Wójcik,
A2 - Dr Marta Czyżewska ,
PB - Academic Conferences Ltd
T2 - 10th European Conference on Social Media, ECSM 2023
Y2 - 18 May 2023 through 19 May 2023
ER -