The Role of Artificial Intelligence in Digital Content Creation and

Distribution

Yanmu Yang

Faculty of Arts, Monash University, 900 Dandenong Rd, Caufield East, Australia

Keywords: Artificial Intelligence, Digital Content Creation, Automated Journalism, Recommender Systems, Ethical

Media.

Abstract: This paper analyzes the profound influence of Artificial Intelligence (AI) on the creation and distribution of

digital content by examining key areas such as automated journalism, recommendation systems, and AI-

assisted video creation and editing. It explores how AI technologies are revolutionizing content production

by significantly enhancing productivity, improving user interaction, and enabling personalized content

customization at an unprecedented scale. Through a systematic review of existing literature and empirical

data, the paper showcases the transformative potential of AI in various aspects of digital media. However, the

paper also addresses critical concerns related to the implementation of AI, such as algorithmic bias, the spread

of disinformation, and the erosion of editorial standards, which can undermine the integrity of digital content.

In conclusion, the paper emphasizes the need for responsible AI adoption, urging collaboration between users,

industry stakeholders, and policymakers. By doing so, it highlights the importance of ensuring that innovation

in AI is aligned with ethical principles and societal values, ensuring that advancements contribute positively

to the evolving media landscape while maintaining accountability and transparency.

1 INTRODUCTION

Digital networks keep growing and Artificial

Intelligence(AI) transforms content production and

delivery methods. With AI technology content

creators produce and adapt materials faster while

helping users get customized results. Human workers

previously handled all media production tasks but AI

systems now help generate news reports and visual

designs alongside audio and video content. The move

to AI production happens because computers process

large datasets better than humans while saving money

and responding fast to audience wants. As digital

media evolves it moves beyond serving user needs to

use data and algorithms with AI leadership.

The effects of AI-controlled content generation

and sharing need to be understood by various groups

in our society. Media companies can save money on

labor costs while making more content and reaching

specific audience groups through automated media

production systems. The way AI impacts how people

behave and what they consume affects the way our

society treats news sources and their trustworthiness.

https://orcid.org/0009-0000-1600-5816

To create ethical innovations, it is important to

understand how AI interacts with users and media

systems. A systematic review of how AI makes

content helps us guide digital media toward both

business goals and better public welfare.

People need to know how AI creates content to

build better digital fluency. People need to learn how

to assess content sources better as AI gets smarter and

they must spot between things made by people and

machines plus find the hidden algorithmic problems.

Educational systems and media companies help train

people to use digital technology successfully.

Teaching people about AI strengths and weaknesses

helps them use automated content better which builds

a smarter digital community that uses technology

properly.

The study regarding the influence of AI on the

generation, dissemination, and consumption of digital

content is gaining ground. Carlson examined what is

being called automated journalism, including how

desktop publishing systems are transforming

workflow in newsrooms and the discussions

surrounding the power of journalism (Carlson, 2015).

142

Yang, Y.

The Role of Artiﬁcial Intelligence in Digital Content Creation and Distribution.

DOI: 10.5220/0013680100004670

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Data Science and Engineering (ICDSE 2025), pages 142-147

ISBN: 978-989-758-765-8

Clerwall found that users were often unaware of the

origins of the content and who authored it, especially

when it was produced by automated machines

(Clerwall, 2014). Montal and Reich raised the

concern over content attribution with AI authoring

tools noting that there are some new regulative

problems that arise out of it (Montal and Reich,

2017). Diakopoulos stated that algorithms are now

central to key functions of a media house, such as

news selection and content curation, and so, there’s a

need for policies to mitigate algorithmic prejudice

(Diakopoulos, 2019). Floridi and Chiriatti analyzed

how new information technologies, particularly

recently developed large language models, can

nourish creative thinking while also being a

mechanism for venting out misinformation (Floridi

and Chiriatti, 2020). All these shifts point to one

conclusion: the work and business processes within

organizations are adopting neoteric and more

efficient methods with the use of AI, however, it

raises more multifaceted questions related to moral

issues, ethics, control over the audience, and editorial

policies.

Through a focused review, this article analyzes

how AI supports digital content production and

delivery across three main areas. This section reviews

how AI tools benefit creative tasks like automated

writing and smart video editing while showing if they

enhance or replace human artists. The part explains

how AI systems change user interaction with content

and how they recommend material to their users plus

explains echo chambers. This section explains the

ethical consequences of AI technology and the laws

that control its use. Through a balanced overview of

the opportunities and challenges, this discussion aims

to promote a more nuanced understanding of how AI

can be used responsibly to drive innovation and

enrich the digital media ecosystem.

2 LITERATURE REVIEW

The creation and distribution of content using AI has

resulted in revolutionary transformations in media.

This subsection discusses AI technologies and

models that impact the industry of the current and

future, by focusing their efforts on challenges and

gaps. Some of the strategic aspects of AI in media,

such as the role of AI in automated journalism,

content synopsis systems, and AI-based video editing

will be covered.

2.1 AI in Automated Journalism

The use of AI in Journalism has significantly changed

in the past years. Automated journalism is

characterized by the capability of using AI to

compose news reports without human writers. This

typically involves the deployment of natural language

generation (NLG) tools to transform structured

information automatically into intelligible spoken or

written formats. Carlson discusses how new

technologies are changing the newsrooms by

lessening the amount of time spent creating basic,

data-centric narratives (Carlson, 2015). The financial

and sports industries, where information-centric

journalism is prevalent, have seen an increase in

article output through AI solutions such as Automated

Insights’ Wordsmith, which allows for thousands of

articles to be written per day.

Even with all this improvement, the issue of AI-

produced content still remaining is unanswered. In

Carlson's automated tools, he describes the ability to

write articles without any grammatical errors,

however, they completely miss the different angles

and investigative aspects complex stories require. It

highlights another problem of how AI and machine

learning technologies fail to cover editorial judgment,

ethics, and artistry in more subjective areas of

journalism work.

Figure 1: The Overall logic of intelligent transformation of

digital media (Picture credit: Orignal).

The use of AI in journalism blends journalism

with technology, giving rise to what is referred to as

“fusion media”. The combination of these two

spheres raises the expectation for higher productivity

of content production. As in the above caption, this

change is placed under "Content perspective" of

fusion media, as AI does help in content creation, but

does not supplant humans in meeting the editorial

judgement (Figure. 1).

2.2 AI in Content Recommendation

Systems

AI technology has transformed how media companies

interact with their audiences. Recommender systems

use algorithms that employ machine learning to

The Role of Artiﬁcial Intelligence in Digital Content Creation and Distribution

143

predict and suggest content according to users’

behavior, choices, and activity. In their essay,

Gomez-Uribe and Hunt study the recommendation

engine used by Netflix, which relies on a combination

of collaborative filtering and deep learning models to

customize content for users. These algorithms have

revolutionized user engagement by recommending

content that caters to users’ specific tastes and

viewing habits.

Yet, AI-based recommendations can have their

shortcomings as Ferrer Conill and Tandoc Jr. point

out. One example is the formation of “filter bubbles”

or “echo chambers,” which broadly refers to the

setting wherein users are surrounded solely with

content that exerts a particular influence on them.

This leads to a suppression of the variety of

information and points of view available to users and

may lead to so-called public opinion polarization. The

concern that needs to be solved has to do with the fact

that videos AI recommendations, which narrow

users’ exposure to diversity, permit recluse from

deeper structures of society that rely on digital media

platforms.

Specific context or termoligies are in smart media

(Figure. 1), content recommendation systems works

well in fulfilling the need for personalized content

delivery. This process can be located within

“Technical perspectives” of smart media where AI

facilitates content curation and distribution. The issue

here is how to satisfy the need for personalization and

at the same time, maximize diversity, since

engagement driven AI models cap content variety.

2.3 AI In Smart Video Editing And

Creation

There have been strides in the application of AI in

video editing and content creation as non-manual

powered AI tools are already doing work that was

needed by the creators. Video systems like Adobe

Sensei, Magisto and others work to automate the

video production process. These tools actively scan

raw video footages with the intent of capturing key

moments for edits and even produce custom clips.

Diakopoulos refers to the changing landscape of AI

in the context of video and states that although these

tools usefully streamline workflows, they come with

the challenge of losing creative control over the

production (Diakopoulos, 2019).

The automating nature of AI touches upon several

parts of video content creation, from scene editing to

color correction and audio balance. As it has been

analyzed, AI in video creation falls under both the

“Intelligent Innovation” category referred to as

"Innovation in media forms" and “Performance

space.” As Diakopoulos contends, the more relevant

consideration here is whether AI is capable of

performing the same degree of artistry, intuition,

improvisation, and decision-making that human

editors do (Diakopoulos, 2019). Balancing the

efficiency brought by automation in addition to the

creative detail needed to tell real captivating stories is

the real challenge.

3 LITERATURE ANALYSIS:

APPLICATIONS,

EXPERIMENTAL RESULTS,

AND COMPARISONS

3.1 AI Applications In Digital Media:

A Comparative Overview

The usage of AI in digital media includes many

activities such as content generation and

personalization algorithms. As Carlson claim, one of

the most evident changes AI-enabled journalism has

undergone revolves around the unparalleled speed at

which data-centric stories – financial or sport ones,

for instance – are created (Carlson, 2015). They

harvest data from large databases and in a matter of

seconds build a coherent story. This improvement

presents clear benefits, but also invites worrying

concerns regarding the lack of human supervision and

input regarding the narratives produced (Montal &

Reich, 2017).

At the same time, experiments conducted on

recommendation systems, including the widely

described Netflix one, have proven that user

engagement can be increased through collaborative

filtering and other machine learning techniques

(Gomez-Uribe & Hunt, 2015). Algorithms scan the

actions and moves of users in order to provide

personalized suggestions. It not only increases

retention, but also poses ethical challenges regarding

filter bubbles that may alienate users from opposing

perspectives and ideas (Ferrer Conill & Tandoc Jr,

2018).

AI contributes significantly to the creation and

sharing of short videos using platforms like TikTok

and YouTube, where recommendation systems learn

user behavioral patterns in real time. To some extent,

however, the effectiveness of these recommendation

engines is reliant on knowing what captivates an

audience. For example, the analytics need to make

sense of the likes and comments, and even shares

(Graefe, 2016). Media professionals can take this fact

ICDSE 2025 - The International Conference on Data Science and Engineering

144

into account, as content monetized under concerning

algorithmic filters is frequently given excessive

focus, despite its underlying quality (Diakopoulos,

2019). This, in turn, affects editorial choices and, in

doing so, causes the blurring of completely organic

and fully automated distribution and production

cycles.

3.2 Empirical Data on Digital Media

Dissemination

Advanced tools can research and analyze how an AI-

generated piece of content and its details are

disseminated throughout digital platforms. Table 1

provides details on the metrics commenting,

retweeting, perception status, and likes including

comments, retweets, likes, and viral sensation index

prop. The provided data shows that 87.42%. of the

sample receive 0 – 2,000 comments, while 92.05% of

retweeting stands at below 10,000. Furthermore, it

can also be observed that 75.50% of the short video

sample tend to have a propogation heat value lower

than 20k indicating that most of the sample achieve a

non-viral status.

Table 1: Data on the impact of digital media.

Digital media

Class

Project

Quantit

Proportion

Comment

quantity

0-2000 528 87.42%

2000-

4000

27 4.47%

4000-1w 15 2.48%

1w-2w 18 2.98%

More than

16 2.65%

Forwarding

capacity

0-1W 556 92.05%

1W-2W 18 2.98%

2W-10W 22 3.64%

10W-20W 8 1.32%

0-2W 456 75.50%

2W-10W 106 17.55%

Propagation

heat

10W-20W 18 2.98%

More than

24 3.97%

Thumb up

0-10W 474 78.48%

10-20W 58 9.60%

20-40W 42 6.95%

More than

40W

30 4.97%

This specific descriptive statistics defines the high

level of difficulty that comes with achieving high

level engagement. A lot of short videos stay below the

interaction threshold, which suggests that algorithms

almost never broaden the reach. Wang state that user

engagement can be further influenced by

motivational media, literacy, and the culture of the

specific platform used to disseminate content (Wang

et al., 2019). In other words, AI powered tools are

necessary, but not enough in ensuring exposure to a

large audience.

3.3 Engagement Measures by Content

Themes: Regression Analysis

As seen in table 2, regression analysis was conducted

to find if there is a relationship between particular

content themes and user's engagement measures such

as likes, comments, retweets, and overall dissem-

ination. The data shows that, unlike any other content

themes, Beta for likes for political news was 0.2026

(p<.01), indicating that it has the most positive

influence. Content themes with the lowest Beta

values include military technology (Beta = 0.1637, p

< 0.01) and social news (Beta = 0.1225, p < 0.05).

This is consistent with what Thurman, Dörr and

Kunert discovered that political content tends to

garner high engagement impressions, perhaps due to

people strong opinions and discussions (Thurman,

Dörr and Kunert, 2017).

Table 2: The analysis of the results of digital media content and propagation effect.

Independent

variable

Thumb u

Comment

uantit

Forwardin

acit

Broadcast heat

Beta

P-

Value

VIF Beta

P-

Value

VIF Beta

P-

Value

VIF Beta

P-

Value

VIF

Sudden

difficult

0.0353 0.543 1.049 0.181 0.912 1.0816 0.1874 0.726 1.0816 0.0977 0.543 1.0651

Social news 0.1225 0.009 1.0535 0.1648 0.977 1.0213 0.1708 0.174 1.0213 0.1163 0.946 1.1654

Knowledge

science

0.044 0.137 1.139 0.0204 0.366 1.1928 0.0305 0.647 1.1928 0.0746 0.084 1.0841

Hot s

0.0228 0.423 1.0595 0.1049 0.009 1.1426 0.1756 0.005 1.1426 0.0492 0.003 1.1836

Political

news

0.2026 0.004 1.1236 0.0352 0.001 1.0911 0.0253 0.251 1.0911 0.0919 0.136 1.0897

Film

entertainment

0.0897 0.456 1.1006 0.1065 0.187 1.1045 0.063 0.004 1.1045 0.21 0.007 1.131

Military

technolo

0.1637 0.005 1.0165 0.1705 0.064 1.0786 0.1737 0.912 1.0786 0.1567 0.016 1.0037

The Role of Artiﬁcial Intelligence in Digital Content Creation and Distribution

145

Othe

0.0731 0.058 1.0828 0.0085 0.411 1.185 0.0705 0.188 1.185 0.0814 0.066 1.1111

On the other hand, film entertainment, which

seems to have a low impact on commenting

engagement measure (Beta = 0.1065, p = 0.187), had

a much larger impact on broadcast heat (Beta = 0.21,

p = 0.007). The data supports the view that different

types of content generate different forms of

engagement; entertainment may encourage passive

viewing or wider dissemination, while political or

social news usually requires active attention and

interaction to engage.

These findings highlight the significance of

theme-based recommendation algorithms, which,

from an AI point of view, are critical. In case an

algorithm is able to classify the content that has a

higher likelihood of receiving likes, comments, or

shares, then that content can be pushed effectively in

order to increase engagement on the platform.

However, as Floridi and Chiriatti warn, optimization

can exacerbate matters by creating echo chambers or

overly sensationalized contexts, which are equally as

damaging to the public. It is therefore important to

consider the different ethical implications ambitions

pose on the users and develop algorithms that strive

to achieve a balance between platform goals in

addition to those ethical implications (Floridi and

Chiriatti, 2020).

Furthermore, these results are in line with Montal

and Reich’s case concerning the challenges raised by

AI-assisted editorial work (Montal and Reich’s,

2017). AI can focus on certain content types to

increase audience engagement, but in the process, it

could completely ignore the more nuanced, important

stories that require thorough investigative journalism

(Carlson, 2015). In effect, media houses have to

juggle between short-term engagement statistics and

the effect the discourse has on the society in the long

run.

4 CHALLENGES AND OUTLOOK

Although AI-based tools can simplify the process of

producing digital content and provide better

engagement with it, they also pose significant risks.

The first challenge is one of algorithmic bias, where

specific perspectives are ignored, or certain

demographics are not catered for, further perpetuating

social inequalities. The second concern has to do with

ethics – issues related to the level of transparency and

responsibility expected when automated systems

substitute or profoundly change human editorial

judgement. Third, the risk of spreading false

information is worsened by AI-enhanced content

production, where so much information is produced

and shared, that it is almost impossible to vet the

accuracy of the information presented.

In order to formulate a plan that can solve these

problems, more needs to be done. First, media

institutions should be equipped with proper processes

that blend human judgement with automated systems.

Second, industry stakeholders and government

authorities need to work together to establish

regulations that guarantee transparency and the

prevention of malicious content. Last, interdisci-

plinary approaches to AI bias reduction should be

funded in order to provide better support for content

delivery. Giving users more access to training and

digital evaluation tools will help them question

automated systems and outputs increasing the quality

of information provided. The combined efforts have

the potential to foster an innovation-driven and

ethical AI utilization responsible evolution of digital

content ecosytems.

5 CONCLUSION

This paper provides an in-depth exploration of the

transformative role of AI in the digital content

creation and distribution landscape. By focusing on

key areas such as automated journalism, AI-driven

recommendation systems, and AI-assisted video

editing, it demonstrates how AI is reshaping the way

media is produced and consumed.

The past few years have seen a rapid advancement

of AI as a groundbreaking force for change in

creation, editing and distribution of articles, videos,

and other content around the globe. The AI-enabled

tools highlighted in this paper can make organizations

more efficient by automating certain functions,

personalizing user interactions, and increasing

production volume, which would allow media firms

to create and share more engaging and focused

content. However, the explosion of AI in media is

equally important to discuss for the context of the

editorial processes, audience engagement, and social

culture as a whole. For example, automated text

generation systems can perform various data-heavy

tasks quickly, but they tend to lack creativity and

journalistic finesse. Similar to this, sophisticated

recommendation systems by ne media corporations

can improve user satisfaction by catering to their

specific needs, but such systems also reduce exposure

ICDSE 2025 - The International Conference on Data Science and Engineering

146

to alternative viewpoints and ideas by fostering echo

chambers.

These results point to the necessity of being extra

careful with the usage of AI in the digital world of

today. While some media companies are adopting

proactive automated systems at an unprecedented

rate, they should still consider the ethical aspects

alongside bias issues that may exist with algorithms.

Guidelines that incorporate innovation and

responsibility will be shaped with the cooperation of

all stakeholders such as researchers, policymakers,

industry experts, and even users. Achieving the

benefits of AI whilst minimizing the risks will be

possible when transparent governance structures are

developed, and algorithms are fine-tuned taking into

consideration an array of cultural and social aspects.

Moreover, it can be said that the combination of

powerful machine learning technology with human

critical thinking is where the future of AI in digital

media and information lies. If effectively monitored,

AI can optimize and revolutionize the entire media

ecosystem, boost overall productivity, and create a

more robust and inclusive digital society.

REFERENCES

Carlson, M., 2015. The robotic reporter: Automated

journalism and the redefinition of labor, compositional

forms, and journalistic authority. Digital Journalism,

3(3), 416-431. https://doi.org/10.1080/21670811.2014.

976412

Clerwall, C., 2014. Enter the robot journalist: Users’

perceptions of automated content. Journalism Practice,

8(5), 519-531. https://doi.org/10.1080/17512786.2014.

883116

Diakopoulos, N., 2019. Automating the news: How

algorithms are rewriting the media. Harvard University

Press.

Floridi, L., & Chiriatti, M., 2020. GPT-3: Its nature, scope,

limits, and consequences. Minds and Machines, 30(4),

681-694. https://doi.org/10.1007/s11023-020-09548-1

Ferrer Conill, R., & Tandoc Jr, E. C., 2018. The audience-

oriented editor: Making sense of the audience in the

newsroom. Digital Journalism, 6(4), 436-453. https://

doi.org/10.1080/21670811.2018.1424054

Gomez-Uribe, C. A., & Hunt, N., 2015. The Netflix

recommender system: Algorithms, business value, and

innovation. ACM Transactions on Management

Information Systems (TMIS), 6(4), 13:1-13:19. https://

doi.org/10.1145/2843948

Graefe, A., 2016. Guide to automated journalism. Tow

Center for Digital Journalism, Columbia Journalism

School.

Montal, T., & Reich, Z., 2017. I, Robot. You, Journalist.

Who is the author? Digital Journalism, 5(7), 829-849.

https://doi.org/10.1080/21670811.2016.1209083

Thurman, N., Dörr, K. N., & Kunert, J., 2017. When

reporters get hands-on with robo-writing: Professionals

consider automated journalism's capabilities and

consequences. Digital Journalism, 5(10), 1240-1259.

https://doi.org/10.1080/21670811.2017.1289819

Wang, C., Hsu, H.-C. K., Bonem, E. M., Moss, J. D., Yu,

S., Nelson, D. B., & Levesque-Bristol, C., 2019. Need

satisfaction and need dissatisfaction: A comparative

study of online and face-to-face learning contexts.

Computers in Human Behavior, 95, 114-125.

https://doi. org/10.1016/j.chb.2019.01.034

The Role of Artiﬁcial Intelligence in Digital Content Creation and Distribution

147