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Purpose

In a nutshell, the purpose of this research is to investigate how the social media contributes to the new concept of influence engineering and crisis manipulation, at the same time offering practical frameworks to alleviate the challenges of associated with misinformation and reinforce truth-oriented communication.

Design/methodology/approach

This research makes use of qualitative research design that is carefully guided by the interpretive research paradigm. This is because the study relies broadly on real-world application methods of content analysis in which concepts are carefully discussed to pass across an in-depth understanding of the subject matter being investigated with the aim of bringing new knowledge.

Findings

This study has closely examined the effect of social media on influence engineering and crisis manipulation, appraising the psychological and algorithmic instruments that allow such phenomena to fester, and finally recommends multidimensional approach to extenuating misinformation and encouraging the dissemination of truthful content.

Originality/value

This research provides a comprehensive contribution to the new concept of influence engineering and crisis-induced misinformation within the framework of social media. Extant literature has often treated misinformation as a broad phenomenon, this study centres on the deliberate and premeditated manipulation of crises as a fundamental mechanism for influencing public sentiment.

The proliferation of social media platforms has largely altered the dynamics of communication, rendering traditional gatekeeping models obsolete. In other words, in the rapidly evolving landscape of the digital age, social media has emerged as a dominant force shaping not only the way we communicate but also how we perceive and interrelate within and around the world (Douai et al., 2013). Popular platforms like Facebook, Twitter, YouTube and TikTok enable the prompt spread of information, democratizing content creation at the same time increasing susceptibility to misinformation and manipulation. Influence engineering can be defined as the deliberate determining of public perception through concerted communication strategies, which has become a deliberate tool in both commercial and political contexts (Balmau et al., 2018). The upsurge of fake news, that is, false or misleading information, often packaged as genuine news has generated extensive concern. Various social media platforms have become predominant avenue for the flow and lifeblood of such content, making them a vocal point for both public discourse and intense academic debate. Much recent academic research has indicated that many users who have access to fake news on these platforms consider them to be true (Berger et al., 2025).

Influence engineering is a new concept that relies deeply on behavioural psychology, data analytics and algorithmic amplification. Algorithms prioritizing content that evokes strong emotional responses, often favouring sensationalism and exaggeration over factual and realistic accuracy (Vosoughi et al., 2018). This mechanism produces echo chambers and filter bubbles that often strengthen pre-existing beliefs, nurturing polarization and tumbling exposure to divergent perspectives. In the contemporary digital age, influence engineering has become prominent strategy for determining human decisions and behaviours with the use of technologies and machine learning. Relying on fields such as psychology, behavioural economics and user experience design, in influencing and controlling cognitive biases to chaperon users towards definite line of actions or behavioural pattern and attitudes. Common applications often use for this reason include social media algorithms that usually personalize content, online retail manoeuvres that create urgency, fitness apps using gamification and digital marketing tactics like social proof to inspire engagement and adoption of desired and anticipated behaviours (Albladi and Weir, 2018).

Crisis situations magnify the vulnerability of people to misinformation because of the heightened level of uncertainty and emotional susceptibility. In such circumstances, rapid spreading of dishonest and false information can exacerbate panic, slow down rapid and emergency responses and weaken institutional authority (Cinelli et al., 2020). Artificial intelligence, technology establishments can keenly monitor people’s daily lives, influencing behaviours, manipulating human conducts and even restructuring human identity under the pretence of freedom of expression. Within the social media and digital environment, the traditional boundaries of privacy are gradually becoming blurred, making a previously private matters public. This scenario has expose people to the risks of manipulation, weakening autonomy and cultural shifts.

The popular social media platforms must be redesigned to adopt transparent algorithmic practices that accentuate accuracy and content diversity. Regulatory instrument must be put in place to reveal content promotion policies and subject them to public scrutiny (Gorwa, 2019). This algorithm’s structure must be reform to extend beyond technical adjustments to accommodate legal, ethical, social and practical defences that enable the algorithms template to advance the common good rather than reinforce inequality.

Promoting digital and media literacy is also important to equip users with indispensable skills to evaluate sources of information and identify manipulation. Digital literacy programmes should be incorporated into the school curriculum. By training students to imbibe the ability to analyse authorship, intent, prejudice and evidence. This literacy creativities will enhance users’ capacity to identify misinformation, limit the possibility of deceptive content and boost a more responsible online behaviour (Thomas et al., 2021).

Fact-checking and moderation of content remain vital defences against online manipulation. Fact-checking use to be responsibility of journalist has now been integrated with platform of operations, yet political restrictions frequently weaken accountability (Graves, 2017). New models such as community-based moderation (e.g. community notes) and AI-powered technology has enabled a wider and faster checks but remain imperfect because it is subject to bias, manipulation and calls for human error (Vinhas and Bastos, 2023).

Governments must put in place regulation that discourage harmful disinformation at the same time protecting freedom of expression. Information users must be held accountable for deception and false political advertising. Integrating legal measures, moral principles, technical solutions and broad community involvement is crucial to reinforce truth, consolidation of democratic processes and ensuring public trust in digital communication (Bradshaw and DeNardis, 2024).

Artificial intelligence and machine learning techniques can also be used to detect and limit misinformation, this must be ethically organized to minimize prejudice (Pennycook and Rand, 2021). This technical solution should combine source confirmation, content exposure, ranking and distribution protections and combination of human–AI fact-checking arrangements. This method will promote information integrity, fair use and contextual dependability during crises (Vosoughi et al., 2018).

Social media has suddenly become a paradox in the sense that it has both negative and positive sides, especially in the contemporary information ecosystem. While it has enabled unprecedented connectivity and democratization of content, it has also facilitated sophisticated forms of influence engineering and crisis manipulation. Addressing this challenge requires a collaborative effort involving platform developers, policymakers, educators and civil society. By promoting transparency, accountability and digital literacy, societies can reclaim the integrity of the information landscape and safeguard democratic values.

Albladi
,
S.M.
and
Weir
,
G.R.
(
2018
), “
User characteristics that influence judgment of social engineering attacks in social networks
”,
Human-Centric Computing and Information Sciences
, Vol.
8
No.
1
, p.
5
.
Balmau
,
O.
,
Guerraoui
,
R.
,
Kermarrec
,
A.M.
,
Maurer
,
A.
,
Pavlovic
,
M.
and
Zwaenepoel
,
W.
(
2018
), “
Limiting the spread of fake news on social media platforms by evaluating users’ trustworthiness
”,
arXiv preprint
.
Berger
,
L.M.
,
Kerkhof
,
A.
,
Mindl
,
F.
and
Münster
,
F.
(
2025
), “
Debunking “fake news” on social media: immediate and short-term effects of fact-checking and media literacy interventions
”,
Journal of Public Economics
, Vol.
245
.
Bradshaw
,
S.
and
DeNardis
,
L.
(
2024
), “
Technical infrastructure as a hidden terrain of disinformation
”,
Journal of Cyber Policy
, Vol.
9
No.
3
, pp.
316
-
332
, doi: .
Cinelli
,
M.
,
Quattrociocchi
,
W.
,
Galeazzi
,
A.
, et al. (
2020
), “
The COVID-19 social media infodemic
”,
Scientific Reports
, Vol.
10
No.
1
, p.
16598
, available at: Link to The COVID-19 social media infodemicLink to the cited article.
Douai
,
A.
,
Auter
,
P.J.
,
Wedlock
,
B.C.
and
Rudyk
,
R.B.
(
2013
), “
The influence of social media in the early 21st century: a meta-analysis of a decade of research (2001-2011)
”,
Global Media Journal
, Vol.
3
Nos
1-2
, pp.
90
-
111
.
Gorwa
,
R.
(
2019
), “
The platform governance triangle: conceptualizing the informal regulation of online content
”,
Internet Policy Review
, Vol.
8
No.
2
.
Graves
,
L.
(
2017
), “
Anatomy of a fact check: objective practice and the contested epistemology of fact checking
”,
Communication, Culture & Critique
, Vol.
10
No.
3
, pp.
518
-
537
.
Pennycook
,
G.
and
Rand
,
D.G.
(
2021
), “
The psychology of fake news
”,
Trends in Cognitive Sciences
, Vol.
25
No.
5
, pp.
388
-
402
, doi: .
Thomas
,
P.B.
,
Hogan-Taylor
,
C.
,
Yankoski
,
M.
and
Weninger
,
T.
(
2021
), “
Pilot study suggests online media literacy programming reduces belief in false news in Indonesia [preprint]
”, arXiv, doi: .
Vinhas
,
O.
and
Bastos
,
M.
(
2023
), “
The WEIRD governance of fact-checking and the politics of content moderation
”,
New Media & Society
, Vol.
27
No.
5
, pp.
2768
-
2787
, doi: .
Vosoughi
,
S.
,
Roy
,
D.
and
Aral
,
S.
(
2018
), “
The spread of true and false news online
”,
Science
, Vol.
359
No.
6380
, pp.
1146
-
1151
, doi: .
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at Link to the terms of the CC BY 4.0 licenceLink to the terms of the CC BY 4.0 licence.

Data & Figures

Supplements

References

Albladi
,
S.M.
and
Weir
,
G.R.
(
2018
), “
User characteristics that influence judgment of social engineering attacks in social networks
”,
Human-Centric Computing and Information Sciences
, Vol.
8
No.
1
, p.
5
.
Balmau
,
O.
,
Guerraoui
,
R.
,
Kermarrec
,
A.M.
,
Maurer
,
A.
,
Pavlovic
,
M.
and
Zwaenepoel
,
W.
(
2018
), “
Limiting the spread of fake news on social media platforms by evaluating users’ trustworthiness
”,
arXiv preprint
.
Berger
,
L.M.
,
Kerkhof
,
A.
,
Mindl
,
F.
and
Münster
,
F.
(
2025
), “
Debunking “fake news” on social media: immediate and short-term effects of fact-checking and media literacy interventions
”,
Journal of Public Economics
, Vol.
245
.
Bradshaw
,
S.
and
DeNardis
,
L.
(
2024
), “
Technical infrastructure as a hidden terrain of disinformation
”,
Journal of Cyber Policy
, Vol.
9
No.
3
, pp.
316
-
332
, doi: .
Cinelli
,
M.
,
Quattrociocchi
,
W.
,
Galeazzi
,
A.
, et al. (
2020
), “
The COVID-19 social media infodemic
”,
Scientific Reports
, Vol.
10
No.
1
, p.
16598
, available at: Link to The COVID-19 social media infodemicLink to the cited article.
Douai
,
A.
,
Auter
,
P.J.
,
Wedlock
,
B.C.
and
Rudyk
,
R.B.
(
2013
), “
The influence of social media in the early 21st century: a meta-analysis of a decade of research (2001-2011)
”,
Global Media Journal
, Vol.
3
Nos
1-2
, pp.
90
-
111
.
Gorwa
,
R.
(
2019
), “
The platform governance triangle: conceptualizing the informal regulation of online content
”,
Internet Policy Review
, Vol.
8
No.
2
.
Graves
,
L.
(
2017
), “
Anatomy of a fact check: objective practice and the contested epistemology of fact checking
”,
Communication, Culture & Critique
, Vol.
10
No.
3
, pp.
518
-
537
.
Pennycook
,
G.
and
Rand
,
D.G.
(
2021
), “
The psychology of fake news
”,
Trends in Cognitive Sciences
, Vol.
25
No.
5
, pp.
388
-
402
, doi: .
Thomas
,
P.B.
,
Hogan-Taylor
,
C.
,
Yankoski
,
M.
and
Weninger
,
T.
(
2021
), “
Pilot study suggests online media literacy programming reduces belief in false news in Indonesia [preprint]
”, arXiv, doi: .
Vinhas
,
O.
and
Bastos
,
M.
(
2023
), “
The WEIRD governance of fact-checking and the politics of content moderation
”,
New Media & Society
, Vol.
27
No.
5
, pp.
2768
-
2787
, doi: .
Vosoughi
,
S.
,
Roy
,
D.
and
Aral
,
S.
(
2018
), “
The spread of true and false news online
”,
Science
, Vol.
359
No.
6380
, pp.
1146
-
1151
, doi: .

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