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  <front>
    <journal-meta />
    <article-meta>
      <title-group>
        <article-title>Artificial Intelligence Generated Virtual Influencers in Online Social Media⋆</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <string-name>Vasyl Buhas</string-name>
          <email>buhas.vv@knutd.edu.ua</email>
          <xref ref-type="aff" rid="aff0">0</xref>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Ihor Ponomarenko</string-name>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Dmytro Ponomarenko</string-name>
        </contrib>
        <contrib contrib-type="author">
          <string-name>Viktoriia Zhebka</string-name>
          <email>viktoria_zhebka@ukr.net</email>
        </contrib>
        <aff id="aff0">
          <label>0</label>
          <institution>Kyiv National University of Technologies and Design</institution>
          ,
          <addr-line>2 Mala Shyianovska str., 01011 Kyiv</addr-line>
          ,
          <country country="UA">Ukraine</country>
        </aff>
      </contrib-group>
      <fpage>119</fpage>
      <lpage>125</lpage>
      <abstract>
        <p>At this stage of human development, there is an increase in the availability of digital technologies, which is associated with the intensive evolution of information technologies. Technological transformations affect the development of most types of economic activity and stimulate companies to actively introduce innovations to secure high positions in a highly competitive environment. The development of server technologies leads to the growth of databases, and the speed of processing large arrays of information, including the implementation of complex mathematical models. Among the effective areas of working with big data, it is advisable to pay attention to machine learning algorithms, which allow the identification of hidden relationships and contribute to the optimization of management decisions in all areas of human activity. Artificial intelligence refers to one of the branches of computer science and involves the use of various approaches, including machine learning. This approach is gaining popularity in practice due to the ability to process large volumes of heterogeneous information and optimize results due to learning algorithms by the action of external factors. The possibility of self-learning and the flexibility of artificial intelligence approaches allow for expanding the scope of the practical application of its use and finding more effective approaches for implementing business processes. Among the important areas of modern companies' activity, it is also advisable to pay attention to digital marketing, since brands need to constantly interact with the target audience on the Internet. Taking into account the characteristics of modern users, companies primarily implement marketing campaigns in social media. Thanks to the application of artificial intelligence in social media, it is possible to establish long-term communications by the principles of personalization.</p>
      </abstract>
      <kwd-group>
        <kwd>eol&gt;artificial intelligence</kwd>
        <kwd>big data</kwd>
        <kwd>content</kwd>
        <kwd>machine learning</kwd>
        <kwd>marketing</kwd>
        <kwd>social media</kwd>
        <kwd>target audience</kwd>
        <kwd>virtual influencers</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec id="sec-1">
      <title>1. Introduction</title>
      <p>
        Digital marketing is an important area of activity for companies because, in the conditions of
globalization and the active introduction of innovations, there is a need for the formation of brand
recognition and constant interaction with the target audience. Thanks to the use of modern digital
marketing tools, it is possible to ensure a high level of targeting, which will contribute to the
optimization of interaction with the target audience. The process of targeting in the digital
environment involves the selection of characteristics that correspond to the characteristics of the
company’s potential customers. Incorrect selection of the target audience leads to losses, and can
also negatively affect the image of the company in the functioning market. The social orientation
of the vast majority of modern users involves taking into account relevant factors when
establishing communications between companies and the target audience. It is thanks to the focus
on interaction between users with common interests that various social media have gained
significant popularity. As part of the implementation of communication strategies, companies
actively use social media marketing as an effective tool for interaction with prospective and
potential customers. Depending on the characteristics of users, companies should use both various
social media and specific communication models. At this stage, the main users of social media are
representatives of generations Z and Alpha. Along with this, a high level of activity is
demonstrated by generation Y, which has spent part of its life outside the digitalization era but is
characterized by a high level of interest in innovative technologies [
        <xref ref-type="bibr" rid="ref1 ref2">1, 2</xref>
        ].
      </p>
      <p>The interaction of companies with the target audience in social media involves the use of
various approaches, however, any communication is not possible without the use of relevant
content. The formation of user interest by placing thematic content (text messages, audio, photo,
and video materials) involves the creation of a scientifically based content plan and its
implementation on an ongoing basis. Thanks to interesting content, the company manages to
ensure a high level of attendance on the brand page in the relevant social media and the loyalty of
a significant number of users on an ongoing basis.</p>
      <p>Modern users form a significant demand for interaction with opinion leaders in various fields of
activity. Thanks to specific experience and specialized knowledge, certain personalities achieve a
high level of popularity in social media and enjoy authority among followers. The cooperation of
companies with influencers allows them to ensure the promotion of brand products to the target
audience thanks to the authority of the relevant opinion leader and his interaction with a large
number of followers. Influencers can advertise the company’s products, or implement covert
promotion of products by conveying their own experience of using a certain product or service.</p>
      <p>
        The gradual growth of the role of representatives of generations Z and Alpha leads to the
formation of new models of interaction between companies and opinion leaders. In the conditions
of digitalization, the number of virtual influencers is gradually increasing, which have several
advantages compared to real opinion leaders. Thanks to the introduction of artificial intelligence,
virtual influencers gain the ability to independently choose a model of behavior from the identified
audience. AI-generated virtual influencers quickly adapt to the needs of users and can interact
simultaneously with a large number of followers 24 hours per day. Thanks to the interaction of
artificial intelligence, in the process of communication with the target audience, it is possible not
only to generate text messages but also to create unique audio, photo, and video content. The
features of the behavior of the corresponding visitor and his text comments act as parameters for
content generation. The presented approach allows companies to achieve personalized marketing
communications in social media and ensures a high level of audience loyalty in the long term.
2. The Aim
Implementation of effective marketing strategies in the digital environment, including social media,
requires companies to search for innovative technologies to identify specific user groups on an
ongoing basis and develop effective communication strategies. The intensive development of
artificial intelligence involves researching and determining the optimal directions for using the
presented technology to achieve the maximum possible results when the company implements a
marketing strategy in social media. A comprehensive analysis of scientific research and the
practice of using machine learning algorithms by companies is expected. Along with this, a
company in the process of introducing artificial intelligence into a social media marketing
campaign must constantly test the presented technologies and choose optimal approaches from the
point of view of ensuring consumer loyalty and achieving economic results [
        <xref ref-type="bibr" rid="ref3">3</xref>
        ].
      </p>
      <p>
        The presented study is dedicated to the study of the features of using AI-generated virtual
influencers in online social media to optimize the marketing strategies of companies and ensure an
economically justified level of conversion [
        <xref ref-type="bibr" rid="ref4 ref5">4, 5</xref>
        ]. Artificial intelligence is seen as a tool to create a
virtual thought leader based on generative algorithms, natural language processing, and other
machine learning approaches. Thanks to the use of computer graphics, it is possible to achieve a
high level of avatar images, the visualized images of which are formed by the needs of the target
audience. Modern advances in the field of information technologies allow the creation of digital
influencers who cannot be distinguished from living people [
        <xref ref-type="bibr" rid="ref6 ref7">6, 7</xref>
        ].
      </p>
      <p>It is appropriate to analyze the main directions of using virtual influencers in the digital
environment and the benefits for companies from the integration of AI-generated opinion leaders
into marketing strategies. It is also important to assess the prospects for the further development of
technologies for the formation of virtual opinion leaders, which will improve their interaction with
users in social media.</p>
    </sec>
    <sec id="sec-2">
      <title>3. Models and Methods</title>
      <p>
        Social media is used by companies to achieve various goals in the process of interaction with the
target audience. The implementation of marketing strategies on the Internet is focused on finding
the target audience, attracting them, and forming long-term communications with the relevant
brand. Web analytics allows companies to accumulate large amounts of information and analyze
the effectiveness of marketing activities implemented in social media on an ongoing basis. If
necessary, it is possible to quickly adjust the marketing strategy on the Internet to optimize the
obtained results. Also, the digital environment is seen as an important channel for e-commerce,
which allows the selling of goods and services to different groups of users. Modern consumers in
many cases find the necessary products on social media and receive comprehensive information
about consumer characteristics thanks to posted relevant content [
        <xref ref-type="bibr" rid="ref10 ref8 ref9">8–10</xref>
        ].
      </p>
      <p>
        When implementing marketing strategies in the digital environment, many companies use
influencers who are popular in certain areas and have a large number of followers. The
effectiveness of cooperation with opinion leaders is confirmed by the active growth of the relevant
services market during 2016–2023 (see Fig. 1). Along with this, there is a decrease in the demand
for real influencers, because thanks to digital avatars, companies get the opportunity to spend less
money and increase the level of interaction with the target audience. Due to hyper-realism,
AIgenerated virtual influencers are perceived by users as real characters, which positively affects the
level of trust in the specified opinion leaders. Avatars can simultaneously interact with a large
number of users in different regions of the world and do not need time to rest, which allows
companies to provide communications 24 hours a day [
        <xref ref-type="bibr" rid="ref11 ref12">11, 12</xref>
        ].
The development of technologies and their introduction into marketing strategies in the digital
environment leads to a change in interaction with users. The behavior of avatars with integrated
artificial intelligence is also evolving due to the expansion of functionality and the implementation
of more complex behavioral models. Among the main directions of using AI-generated virtual
influencers in social networks, it is advisable to pay attention to the following:
      </p>
      <p>
        1. Authentic persona creation. The use of artificial intelligence allows companies to generate
unique virtual influencers whose appearance and behavior patterns meet the expectations of the
target audience. The process of self-learning based on large arrays of information allows to quickly
transform various avatar characteristics according to the prevailing wishes of subscribers [
        <xref ref-type="bibr" rid="ref14">14</xref>
        ].
      </p>
      <p>
        2. Audience engagement through generated content. Chatbots with integrated artificial
intelligence allow to generate text responses to user queries. Digital innovations allow the use of
algorithms to generate messages, images, and video content during the interaction of a virtual
influencer with users in social media. Communication with individual users acts as a source of
information for the generation of unique content, which is created taking into account the context
and other characteristics. Due to adaptation to the behavior model of a specific subscriber, a virtual
influencer is perceived as an interesting interlocutor, which leads to trusting long-term
relationships. Avatars with integrated artificial intelligence are characterized by high efficiency and
allow to significantly increase the level of engagement of the target audience of the respective
brand [
        <xref ref-type="bibr" rid="ref15">15</xref>
        ].
      </p>
      <p>
        3. Versatility and high productivity. By training artificial intelligence models on large databases
with heterogeneous information, avatars get the opportunity to discuss various questions with
users and provide competent answers. Virtual opinion leaders with integrated artificial intelligence
can be used to promote different brands and a wide range of products. By copying the behavior
patterns of real opinion leaders, virtual avatars can promote products or demonstrate positive
experiences of their personal use by the principles of hidden advertising messages. The number of
simultaneous contacts of the AI-generated virtual influencer with users depends on the power of
the servers and the machine learning algorithms. Simultaneous interaction with a large number of
subscribers can take place in the tete-a-tete mode, which in the vast majority of cases does not
affect the quality of communications [
        <xref ref-type="bibr" rid="ref16">16</xref>
        ].
      </p>
      <p>
        4. Determination of new directions of interaction with the target audience. A characteristic
feature of artificial intelligence is the processing of large arrays of disparate information and the
identification of hidden relationships. The use of various machine learning algorithms to optimize
the work of virtual influencers allows for identifying the transformation of users and
distinguishing characteristic features in the process of interaction with the target audience. In the
conditions of digitalization, there is a dynamic change in the behavior of modern users, especially
representatives of generations Z and Alpha, who use specific models of behavior and are
characterized by a progressive system of values. Communication of AI-generated virtual
influencers with younger generations and adjustment to appropriate interaction models contribute
to increasing the effectiveness of marketing strategies of companies in the digital environment and
obtaining loyal consumers in the long term [
        <xref ref-type="bibr" rid="ref17 ref18">17, 18</xref>
        ].
      </p>
      <p>
        When using AI-generated virtual influencers to interact with the target audience, in many cases
machine learning algorithms are used to optimize the communication process and achieve a high
level of customer loyalty. An important role in achieving effective results must be played by both
information collection systems and the development of suitable mathematical algorithms for
processing large data and creating relevant thematic content [
        <xref ref-type="bibr" rid="ref19">19</xref>
        ]. Fig. 2 shows the scheme of using
machine learning algorithms to ensure the functioning of the AI-generated virtual influencer [
        <xref ref-type="bibr" rid="ref20 ref21 ref22">20–
22</xref>
        ].
      </p>
      <p>
        At the first stage, it is advisable to use Generative Adversarial Networks, which allow to
generation of images for virtual influencers. Thanks to the availability of a large number of
architectures and settings, the system can quickly learn based on large data and create high-quality
images that reproduce human features. In the process of interaction with the target audience in
social media, algorithms can adjust the appearance of the corresponding avatar [
        <xref ref-type="bibr" rid="ref23">23</xref>
        ].
      </p>
      <p>
        Reinforcement Learning allows to identify the personalities of the different groups users
behavior, and the obtained results will be used in the process of interaction of the avatar with
users. The presented approach makes it possible to adapt the behavior of a digital influencer to
each of the users, which significantly increases the level of loyalty of the audience as a whole.
Copying the behavior of each user is perceived by customers as communication with a like-minded
person.
Natural Language Processing allows to generation of text messages in the process of providing
communications with the target audience. Natural language recognition involves solving a system
of questions about determining the needs of different users and identifying the content load since
in many cases messages contain a certain context. Modern models with a high level of probability
allow to correctly identify user messages and generate a competent response or comment [
        <xref ref-type="bibr" rid="ref27">27</xref>
        ].
      </p>
      <p>
        Deep Learning contains a large number of algorithms, among which an important direction is
the generation of visualized content with subsequent placement in social media. The interaction of
digital avatars with the target audience thanks to unique, relevant photos and videos contributes to
a significant increase in the number of subscribers [
        <xref ref-type="bibr" rid="ref28">28</xref>
        ].
      </p>
    </sec>
    <sec id="sec-3">
      <title>4. Further Research</title>
      <p>
        The obtained results show the expediency of further research on the creation of digital avatars
with integrated artificial intelligence. The active development of information technologies and
specialized infrastructure leads to an increase in the effectiveness of machine learning algorithms.
Alongside this, there has been an evolution in the digital marketing tools used by companies to
engage with their target audience online. In the outlined conditions, there is a need to study the
multiplier effect from the combination of innovative approaches in the field of artificial intelligence
and advanced digital marketing tools, which will allow companies to reach a qualitatively new
level of interaction with users and compliance with personalized principles. The creation of
metauniverses will lead to the transformation of social communication through the introduction of
virtual reality technologies. Social media in the form of metauniverses will require the use of new
AI-generated virtual influencers, which will be positively perceived by the target audience and will
allow brands to promote their products in an innovative environment [
        <xref ref-type="bibr" rid="ref29">29</xref>
        ].
      </p>
    </sec>
    <sec id="sec-4">
      <title>Conclusions</title>
      <p>The use of AI-generated virtual influencers in marketing campaigns of brands in social networks is
gaining significant popularity in today’s environment. Depending on the strategic vision and
financial capabilities, various companies either have their digital avatars or use the services of
third-party companies. The cost of services from digital avatars correlates with the number of their
subscribers. Digital opinion leaders are most popular among Generation Z and Alpha, who use
relevant social media. So, on Instagram at the beginning of 2024, the most popular AI-generated
virtual influencers are Lu do Magalu (6.7M followers), Lil Miquela (2.6M followers), and K/DA
(520K followers). Further digitization and virtualization of daily life require companies to develop
new directions of interaction with the target audience, which stimulates the development of virtual
influencers. Thanks to innovative approaches, avatars will have the opportunity to interact more
effectively with users, increasingly acquiring human features and more precisely adapting to the
needs of an individual social media visitor.</p>
      <p>Declaration on Generative AI
While preparing this work, the authors used the AI programs Grammarly Pro to correct text
grammar and Strike Plagiarism to search for possible plagiarism. After using this tool, the authors
reviewed and edited the content as needed and took full responsibility for the publication’s content.</p>
    </sec>
  </body>
  <back>
    <ref-list>
      <ref id="ref1">
        <mixed-citation>
          [1]
          <string-name>
            <given-names>D.</given-names>
            <surname>McGruer</surname>
          </string-name>
          ,
          <article-title>Dynamic Digital Marketing: Master the World of Online and Social Media Marketing to Grow Your Business</article-title>
          , John Wiley &amp; Sons,
          <year>2020</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref2">
        <mixed-citation>
          [2]
          <string-name>
            <given-names>A.</given-names>
            <surname>Anjum</surname>
          </string-name>
          ,
          <string-name>
            <given-names>M. R.</given-names>
            <surname>Thomas</surname>
          </string-name>
          ,
          <string-name>
            <given-names>P. K.</given-names>
            <surname>Prakash</surname>
          </string-name>
          , Digital Marketing Strategies: Effectiveness on Generation Z,
          <string-name>
            <surname>SCMS J. Indian Manag</surname>
          </string-name>
          .
          <volume>17</volume>
          (
          <year>2020</year>
          ).
        </mixed-citation>
      </ref>
      <ref id="ref3">
        <mixed-citation>
          [3]
          <string-name>
            <given-names>O.</given-names>
            <surname>Plaksiuk</surname>
          </string-name>
          , et al.,
          <article-title>Analysis and Assessment of Human Capital in the Regions of Slovakia, Economics</article-title>
          .
          <source>Ecology. Socium</source>
          <volume>7</volume>
          (
          <year>2023</year>
          )
          <fpage>13</fpage>
          -
          <lpage>25</lpage>
          . doi:
          <volume>10</volume>
          .31520/
          <fpage>2616</fpage>
          -
          <lpage>7107</lpage>
          /
          <year>2023</year>
          .7.3-
          <fpage>2</fpage>
        </mixed-citation>
      </ref>
      <ref id="ref4">
        <mixed-citation>
          [4]
          <string-name>
            <given-names>V.</given-names>
            <surname>Buhas</surname>
          </string-name>
          , et al.,
          <string-name>
            <surname>AI-Driven</surname>
            <given-names>Sentiment</given-names>
          </string-name>
          <article-title>Analysis in Social Media Content, in: Digital Economy Concepts</article-title>
          and Technologies Workshop, DECaT, vol.
          <volume>3665</volume>
          (
          <year>2024</year>
          )
          <fpage>12</fpage>
          -
          <lpage>21</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref5">
        <mixed-citation>
          [5]
          <string-name>
            <given-names>O.</given-names>
            <surname>Mykhaylova</surname>
          </string-name>
          , et al.,
          <article-title>Person-of-Interest Detection on Mobile Forensics Data-AI-Driven Roadmap</article-title>
          ,
          <source>in: Cybersecurity Providing in Information and Telecommunication Systems</source>
          , vol.
          <volume>3654</volume>
          (
          <year>2024</year>
          )
          <fpage>239</fpage>
          -
          <lpage>251</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref6">
        <mixed-citation>
          [6]
          <string-name>
            <given-names>M.</given-names>
            <surname>Conti</surname>
          </string-name>
          ,
          <string-name>
            <given-names>J.</given-names>
            <surname>Gathani</surname>
          </string-name>
          ,
          <string-name>
            <given-names>P. P.</given-names>
            <surname>Tricomi</surname>
          </string-name>
          , Virtual Influencers in Online Social Media,
          <source>IEEE Communications Magazine</source>
          <volume>60</volume>
          (
          <year>2022</year>
          )
          <fpage>86</fpage>
          -
          <lpage>91</lpage>
          . doi:
          <volume>10</volume>
          .1109/
          <string-name>
            <surname>MCOM</surname>
          </string-name>
          .
          <volume>001</volume>
          .2100786
        </mixed-citation>
      </ref>
      <ref id="ref7">
        <mixed-citation>
          [7]
          <string-name>
            <given-names>V.</given-names>
            <surname>Buhas</surname>
          </string-name>
          , et al.,
          <source>AI-Driven Sentiment Analysis in Social Media Content, in: Digital Economy Concepts and Technologies Workshop</source>
          <year>2024</year>
          ,
          <year>2024</year>
          ,
          <fpage>12</fpage>
          -
          <lpage>21</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref8">
        <mixed-citation>
          [8]
          <string-name>
            <given-names>J.</given-names>
            <surname>Yu</surname>
          </string-name>
          , et al.,
          <source>Artificial Intelligence-Generated Virtual Influencer: Examining the Effects of Emotional Display on User Engagement, J. Retailing Consumer Serv</source>
          .
          <volume>76</volume>
          (
          <year>2024</year>
          )
          <volume>103560</volume>
          .7. doi:
          <volume>10</volume>
          .1016/j.jretconser.
          <year>2023</year>
          .103560
        </mixed-citation>
      </ref>
      <ref id="ref9">
        <mixed-citation>
          [9]
          <string-name>
            <given-names>I.</given-names>
            <surname>Ponomarenko</surname>
          </string-name>
          , et al.,
          <article-title>Use of Neural Networks for Pattern Recognition in E-Commerce</article-title>
          , in: IT&amp;
          <string-name>
            <given-names>I</given-names>
            <surname>Workshops</surname>
          </string-name>
          ,
          <year>2021</year>
          ,
          <fpage>407</fpage>
          -
          <lpage>415</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref10">
        <mixed-citation>
          [10]
          <string-name>
            <given-names>R.</given-names>
            <surname>Motoryn</surname>
          </string-name>
          , et al.,
          <source>Evaluation of Regional Features of Electronic Commerce in Europe, Statistical J. IAOS</source>
          <volume>38</volume>
          (
          <year>2022</year>
          )
          <fpage>1339</fpage>
          -
          <lpage>1347</lpage>
          . doi:
          <volume>10</volume>
          .3233/SJI-220938
        </mixed-citation>
      </ref>
      <ref id="ref11">
        <mixed-citation>
          [11]
          <string-name>
            <given-names>S.</given-names>
            <surname>Sands</surname>
          </string-name>
          , et al.,
          <string-name>
            <surname>False</surname>
            <given-names>Idols</given-names>
          </string-name>
          :
          <article-title>Unpacking the Opportunities and Challenges of Falsity in the Context of Virtual Influencers</article-title>
          , Business
          <string-name>
            <surname>Horizons</surname>
          </string-name>
          (
          <year>2022</year>
          ). doi:
          <volume>10</volume>
          .1016/j.bushor.
          <year>2022</year>
          .
          <volume>08</volume>
          .002
        </mixed-citation>
      </ref>
      <ref id="ref12">
        <mixed-citation>
          [12]
          <string-name>
            <given-names>I.-I.</given-names>
            <surname>Gradinescu</surname>
          </string-name>
          , E. Bostanica,
          <string-name>
            <given-names>M.</given-names>
            <surname>Orzan</surname>
          </string-name>
          ,
          <article-title>The Impact of New Technologies on the Future of Marketing: the Challenges of Adopting Artificial Intelligence-Generated Influencers in Marketing Strategies. Is the Romanian Market Ready for This Emerging Trend?</article-title>
          , Ovidius University Annals,
          <source>Economic Sciences Series</source>
          <volume>23</volume>
          (
          <year>2023</year>
          )
          <fpage>617</fpage>
          -
          <lpage>620</lpage>
          . doi:
          <volume>10</volume>
          .61801/ OUAESS.
          <year>2023</year>
          .
          <volume>1</volume>
          .
          <fpage>80</fpage>
        </mixed-citation>
      </ref>
      <ref id="ref13">
        <mixed-citation>
          [13]
          <string-name>
            <given-names>Influencer</given-names>
            <surname>Marketing Market Size Worldwide</surname>
          </string-name>
          from
          <year>2016</year>
          to
          <year>2023</year>
          . URL: https://www.statista.com/ statistics/1092819/global-influencer
          <string-name>
            <surname>-</surname>
          </string-name>
          market-size/
        </mixed-citation>
      </ref>
      <ref id="ref14">
        <mixed-citation>
          [14]
          <string-name>
            <given-names>B.</given-names>
            <surname>Koles</surname>
          </string-name>
          , et al.,
          <source>The Authentic Virtual Influencer: Authenticity Manifestations in the Metaverse, J. Business Res</source>
          .
          <volume>170</volume>
          (
          <year>2024</year>
          )
          <article-title>114325</article-title>
          . doi:
          <volume>10</volume>
          .1016/j.jbusres.
          <year>2023</year>
          .114325
        </mixed-citation>
      </ref>
      <ref id="ref15">
        <mixed-citation>
          [15]
          <string-name>
            <given-names>D.</given-names>
            <surname>Du</surname>
          </string-name>
          ,
          <string-name>
            <given-names>Y.</given-names>
            <surname>Zhang</surname>
          </string-name>
          , J. Ge,
          <article-title>Effect of AI Generated Content Advertising on Consumer Engagement</article-title>
          ,
          <source>in: Int. Conf. on Human-Computer Interaction</source>
          , Springer,
          <year>2023</year>
          ,
          <fpage>121</fpage>
          -
          <lpage>129</lpage>
          . doi:
          <volume>10</volume>
          .1007/978-3-
          <fpage>031</fpage>
          - 36049-
          <issue>7</issue>
          _
          <fpage>9</fpage>
        </mixed-citation>
      </ref>
      <ref id="ref16">
        <mixed-citation>
          [16]
          <string-name>
            <given-names>F.</given-names>
            <surname>Liu</surname>
          </string-name>
          ,
          <string-name>
            <given-names>Y.-H.</given-names>
            <surname>Lee</surname>
          </string-name>
          , Virtually Responsible?
          <article-title>Attribution of Responsibility toward Human vs. Virtual Influencers and the Mediating Role of Mind Perception</article-title>
          ,
          <source>J. Retailing Consumer Serv</source>
          .
          <volume>77</volume>
          (
          <year>2024</year>
          )
          <article-title>103685</article-title>
          . doi:
          <volume>10</volume>
          .1016/j.jretconser.
          <year>2023</year>
          .103685
        </mixed-citation>
      </ref>
      <ref id="ref17">
        <mixed-citation>
          [17]
          <string-name>
            <given-names>H.</given-names>
            <surname>Khuat</surname>
          </string-name>
          , From Pixels to Fame:
          <article-title>An Empirical Study of Virtual Influencers</article-title>
          and
          <string-name>
            <surname>Gen Z Customer Engagement</surname>
          </string-name>
          ,
          <year>2023</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref18">
        <mixed-citation>
          [18]
          <string-name>
            <given-names>X.</given-names>
            <surname>Zhao</surname>
          </string-name>
          , et al.,
          <source>Virtual Fashion Influencers: Towards a More Sustainable Consumer Behaviour of Generation Z?</source>
          ,
          <year>2022</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref19">
        <mixed-citation>
          [19]
          <string-name>
            <given-names>J.</given-names>
            <surname>Huh</surname>
          </string-name>
          ,
          <string-name>
            <given-names>M. R.</given-names>
            <surname>Nelson</surname>
          </string-name>
          ,
          <string-name>
            <given-names>C. A.</given-names>
            <surname>Russell</surname>
          </string-name>
          , ChatGPT,
          <source>AI Advertising, and Advertising Research and Education</source>
          ,
          <year>2023</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref20">
        <mixed-citation>
          [20]
          <string-name>
            <given-names>V.</given-names>
            <surname>Zhebka</surname>
          </string-name>
          , et al.,
          <article-title>Methodology for Predicting Failures in a Smart Home based on Machine Learning Methods</article-title>
          ,
          <source>in: Workshop on Cybersecurity Providing in Information and Telecommunication Systems, CPITS</source>
          , vol.
          <volume>3654</volume>
          (
          <year>2024</year>
          )
          <fpage>322</fpage>
          -
          <lpage>332</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref21">
        <mixed-citation>
          [21]
          <string-name>
            <given-names>V.</given-names>
            <surname>Buhas</surname>
          </string-name>
          , et al.,
          <article-title>Using Machine Learning Techniques to Increase the Effectiveness of Cybersecurity</article-title>
          ,
          <source>in: Cybersecurity Providing in Information and Telecommunication Systems</source>
          , vol.
          <volume>3188</volume>
          , no.
          <issue>2</issue>
          (
          <year>2021</year>
          )
          <fpage>273</fpage>
          -
          <lpage>281</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref22">
        <mixed-citation>
          [22]
          <string-name>
            <given-names>M.</given-names>
             
            <surname>Adamantis</surname>
          </string-name>
          ,
          <string-name>
            <given-names>V.</given-names>
             
            <surname>Sokolov</surname>
          </string-name>
          ,
          <string-name>
            <surname>P.</surname>
          </string-name>
           Skladannyi,
          <article-title>Evaluation of state-of-the-art machine learning smart contract vulnerability detection method, Advances in Computer Science for Engineering and Education VII, vol</article-title>
          .
          <volume>242</volume>
          (
          <year>2025</year>
          )
          <fpage>53</fpage>
          -
          <lpage>65</lpage>
          . doi:
          <volume>10</volume>
          .1007/978-3-
          <fpage>031</fpage>
          -84228-
          <issue>3</issue>
          _
          <fpage>5</fpage>
        </mixed-citation>
      </ref>
      <ref id="ref23">
        <mixed-citation>
          [23]
          <string-name>
            <given-names>B.</given-names>
            <surname>Wu</surname>
          </string-name>
          , et al.,
          <article-title>Using Improved Conditional Generative Adversarial Networks to Detect Social Bots on Twitter, IEEE Access 8 (</article-title>
          <year>2020</year>
          )
          <fpage>36664</fpage>
          -
          <lpage>36680</lpage>
          . doi:
          <volume>10</volume>
          .1109/ACCESS.
          <year>2020</year>
          .2975630
        </mixed-citation>
      </ref>
      <ref id="ref24">
        <mixed-citation>
          [24]
          <string-name>
            <given-names>F.</given-names>
            <surname>Ferrari</surname>
          </string-name>
          ,
          <string-name>
            <given-names>F.</given-names>
            <surname>McKelvey</surname>
          </string-name>
          ,
          <article-title>Hyperproduction: A Social Theory of Deep Generative Models</article-title>
          ,
          <source>Distinktion: J. Social Theory</source>
          <volume>24</volume>
          (
          <year>2023</year>
          )
          <fpage>338</fpage>
          -
          <lpage>360</lpage>
          . doi:
          <volume>10</volume>
          .1080/1600910X.
          <year>2022</year>
          .2137546
        </mixed-citation>
      </ref>
      <ref id="ref25">
        <mixed-citation>
          [25]
          <string-name>
            <given-names>Y.</given-names>
            <surname>Wang</surname>
          </string-name>
          ,
          <article-title>Synthetic Realities in the Digital Age: Navigating the Opportunities and Challenges of AI-Generated Content</article-title>
          , Authorea Preprints,
          <year>2023</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref26">
        <mixed-citation>
          [26]
          <string-name>
            <given-names>L.</given-names>
            <surname>Labajov</surname>
          </string-name>
          ,
          <article-title>The State of Ai: Exploring the Perceptions, Credibility, and Trustworthiness of the Users towards AI-</article-title>
          <string-name>
            <surname>Generated</surname>
            <given-names>Content</given-names>
          </string-name>
          ,
          <year>2023</year>
          .
        </mixed-citation>
      </ref>
      <ref id="ref27">
        <mixed-citation>
          [27]
          <string-name>
            <given-names>M.</given-names>
            <surname>Chong</surname>
          </string-name>
          , G. Swapna,
          <source>Social Media Influencers and Instagram Storytelling: Case Study of Singapore Instagram Influencers, J. Appl. Business Econom</source>
          .
          <volume>22</volume>
          (
          <year>2020</year>
          )
          <article-title>81</article-title>
          . doi:
          <volume>10</volume>
          .33423/jabe.v22i10.
          <fpage>3714</fpage>
        </mixed-citation>
      </ref>
      <ref id="ref28">
        <mixed-citation>
          [28]
          <string-name>
            <given-names>L.</given-names>
            <surname>Kugler</surname>
          </string-name>
          , Virtual Influencers in the Real World,
          <source>Communications of the ACM</source>
          <volume>66</volume>
          (
          <year>2023</year>
          )
          <fpage>23</fpage>
          -
          <lpage>25</lpage>
          .
        </mixed-citation>
      </ref>
      <ref id="ref29">
        <mixed-citation>
          [29]
          <string-name>
            <given-names>H.</given-names>
            <surname>Jeon</surname>
          </string-name>
          , et al.,
          <article-title>Blockchain and AI Meet in the Metaverse</article-title>
          ,
          <source>Advances in the Convergence of Blockchain and Artificial Intelligence</source>
          <volume>73</volume>
          (
          <year>2022</year>
          ).doi:
          <volume>10</volume>
          .5772/intechopen.99114
        </mixed-citation>
      </ref>
    </ref-list>
  </back>
</article>