Spam filters are algorithms that analyze whether digital content contains unwanted and potentially harmful messages. These systems consider factors such as keyword density, link quality, and content format ( Guzella & Caminhas, 2009 ).

Spam filtering systems are software mechanisms that aim to detect, block, or separate unwanted, irrelevant, or harmful content in digital communication environments ( Gurjar et al., 2019 ). These systems, which are used in a wide variety of digital areas from e-mail systems to social media platforms, from forums to online advertising networks ( Rowley, 2008 ), are developed with the aim of ensuring content security, improving user experience, and reducing digital information pollution ( Goh et al., 2013 ; Hearn, 2016 ).

Spam filtering technologies work on content-based analyses (e.g., word lists, keyword density), data on user behavior (e.g., click-through rate, reporting history), and classification models supported by more advanced machine learning algorithms ( Canter et al., 2013 ; Opreana & Vinerean, 2015 ). In particular, AI-based filtering methods provide high accuracy in analyzing contextual meaning and pattern recognition in content ( Baltes, 2015 ; Holliman & Rowley, 2014 ). These systems not only filter traditional e-mail advertisements or malicious links, but also serve as a control mechanism against misleading content, malicious bot accounts, or manipulative user-generated content spread over social media today ( Kee & Yazdanifard, 2015 ). However, the effectiveness of spam filtering systems may vary depending on the context and purpose of the content ( Daugherty et al., 2008 ; Poch & Martin, 2015 ). There may be cases where some authentic and organic content is mistakenly classified as spam ( Muniz & Schau, 2007 ). This situation has become an important factor that directly affects the spread and reach of user-generated content on digital platforms ( Wang, 2021 ).

The consumer journey is a process that defines the customer’s relationship with the brand, including awareness, interest, evaluation, purchase and loyalty stages. User production content can take on different roles at various stages of this process ( Lemon & Verhoef, 2016 ).

The consumer journey refers to a multi-stage and dynamic interaction process that begins with the awareness process before individuals make a decision to purchase a product or service, extending to the moment of purchase and their experiences afterwards ( Akehurst, 2009 ). This concept is not only the act of purchasing ( Ashley & Tuten, 2015 ). It also includes behaviors such as the consumer’s definition of needs, search for information, evaluation of alternatives, decision making and post-purchase satisfaction or complaints ( Barger et al., 2016 ).

The consumer journey, traditionally depicted as linear, has taken on a much more complex, omnichannel and interactive structure today with the impact of digitalization ( Bitner, 1990 ). The increase in digital touchpoints (websites, social media, mobile applications, user reviews, etc.) is constantly reshaping consumers’ relationships with brands ( Moran et al., 2014 ). In this context, the consumer journey is no longer a process driven solely by businesses, but is also considered as a network of experiences that consumers actively shape and influence each other ( Kee & Yazdanifard, 2015 ). Different touchpoints and content types come into play at each stage of the consumer journey ( Pulizzi, 2014 ). This necessitates brands to structure their content strategies, data analytics, and digital marketing techniques in a more sensitive and personalized manner ( Moran et al., 2014 ). The impact of user-generated content on the consumer journey becomes particularly evident in the awareness and trust-building stages ( Lecinski, 2011 ). It can directly affect consumer decisions thanks to its authenticity and experience-based nature ( Scholz & Smith, 2016 ).

Spam filters use machine learning algorithms to determine whether content is organic or manipulative ( Dessart et al., 2015 ). Excessive keyword use, low-quality links, and repetitive sentences increase the risk of being marked as spam ( Brodie et al., 2013 ).

Basic criteria of algorithms refer to the basic criteria that determine the functionality, effectiveness, and accuracy of the structures used in data processing, decision-making, and execution of automated processes in digital systems ( Chan & Astari, 2017 ). Especially in artificial intelligence, big data, and machine learning-based systems, algorithms analyze inputs and produce outputs in line with certain goals and process them based on various criteria in this process ( Ashley & Tuten, 2015 ).

Today, algorithms used in social media, search engines and content distribution platforms are optimized according to these basic criteria and shape user experience, content visibility and even digital interaction styles ( Wallace et al., 2014 ). In this context, algorithms are not only technical structures but also considered as strategic guiding elements in the digital media ecosystem ( Van Doorn et al., 2010 ).

The organic nature of user-generated content allows the content to easily pass through spam filters because it is based on authentic user behavior ( Tsai & Men, 2013 ). However, “fake” user content directed by brands can be caught by these filters. In this context, the language structure of the content, its visual use and interaction intensity are important factors ( Ahuja & Medury, 2010 ).

The ability of user-generated content to bypass spam algorithms refers to the ability of individual-generated content on digital platforms to circulate without being identified as unwanted or low-quality content by automatic filtering and classification systems ( Lee et al., 2018 ). This directly affects the organic dissemination potential of content, digital interaction levels, and user access ( Lee et al., 2018 ; Vivek et al., 2014 ). Spam filtering algorithms generally limit the visibility of certain content by evaluating content based on technical criteria such as repetitive language patterns, deceptive redirects, keyword density, link structures, and user complaint history ( Ha, 2004 ). However, user-generated content is less likely to be caught by traditional spam detection models because it mostly contains original, experience-based, and contextually rich expressions ( Chu & Kim, 2011 ). This uniqueness enables user-generated content to spread naturally by bypassing filter systems, as it differs from patterns defined by algorithms as “spam” ( Lee et al., 2018 ; Laroche et al., 2012 ).

In addition, user-generated content is often supported by social proof and interaction data (such as likes, shares, comments) ( Chaudhuri & Holbrook, 2001 ). Such feedback is perceived by algorithms as positive signals about the trustworthiness and interaction value of the content, which reduces the risk of the content being classified as spam ( Gummerus et al., 2012 ).

However, the fact that spam algorithms have become more contextual with developing artificial intelligence models can sometimes cause user-generated content to be misclassified ( Moran et al., 2014 ; Montoya-Weiss et al., 2003 ). User-generated content, especially those produced for promotional or manipulative purposes (e.g., fake comments, commercial posts), can be categorized as spam by algorithms ( Braun & Clarke, 2021 ). This situation reveals that user-generated content should be evaluated not only on the quality of the content but also on the way it interacts with the algorithms ( Okazaki et al., 2009 ; Pigg & Crank, 2004 ).

Therefore, the ability of user-generated content to bypass spam algorithms provides a significant advantage in terms of content strategies ( Moran et al., 2014 ; Montoya-Weiss et al., 2003 ). For brands, it offers a more visible, authentic and trust-based communication platform ( Park & Lee, 2009 ). In this context, the structural and strategic features of user-generated content in digital marketing and content management processes should be designed in line with algorithmic dynamics ( Lee et al., 2018 ; E M Steenkamp et al., 2003 ).

User-generated content serves as a natural information tool in users’ first contact with the brand ( Lee et al., 2018 ; Hair Jr. et al., 2010 ). Social media posts, hashtag campaigns and user videos are effective at this stage ( Baron & Kenny, 1986 ). The awareness stage is the first stage of the consumer decision-making process and refers to the point at which an individual becomes aware of a need, problem or desire ( Lecinski, 2011 ). At this stage, the consumer enters a general awareness process rather than being informed about a specific brand or solution ( Chan & Astari, 2017 ). This stage, where mental awakening begins regarding the relevant product or service, is a critical touchpoint in terms of digital marketing strategies ( Lee et al., 2018 ; Chan & Astari, 2017 ).

Creating awareness in the digital ecosystem is becoming more effective not only through advertising and promotion; but also through content-based approaches, especially with the contribution of user-generated content ( Moran et al., 2014 ; Montoya-Weiss et al., 2003 ). Original content where users share their experiences makes it easier for potential consumers to relate to a problem and moves them from a passive viewer to an active researcher ( Wang, 2021 , Chan & Astari, 2017 ). In the awareness stage, content strategies should be designed to be informative, attention-grabbing and emotionally connected ( Lee et al., 2018 ). The types of content used at this stage are generally information-oriented tools such as blog posts, social media posts, videos, guide content and user comments ( Chan & Astari, 2017 ). Algorithms also come into play at this point, highlighting personalized content according to the user’s interests, search history and digital behavior. Thus, awareness is built in a more targeted and effective way ( Chan & Astari, 2017 ; Lecinski, 2011 ).

In this context, the awareness stage is not only the starting point of the consumer’s mental process; it is also a strategic stage where brands try to build trust and establish the first emotional contact with the consumer ( Hair Jr. et al., 2010 ). In particular, the authentic nature of user-generated content makes a significant contribution to the trust-building process at this stage ( Lee et al., 2018 ).

In the digital consumer journey, the evaluation and decision-making stages represent the crucial phase in which consumers assess alternatives and commit to a purchase. While existing literature has emphasized the role of user reviews and ratings in fostering trust ( Braun & Clarke, 2021 ; Chan & Astari, 2017 ), recent studies point to a more nuanced understanding: trust formation is shaped not only by informational credibility but also by perceived social proximity ( Lee et al., 2018 ). This indicates that consumers are increasingly influenced by content created by peers or relatable individuals, rather than by corporate marketing messages ( Cheong & Morrison, 2008 ).

User-generated content (UGC), particularly in video and micro-influencer formats, reduces perceived risk by offering experiential insights rather than promotional claims. This content acts as a form of social validation, enabling consumers to align their decision with a broader community consensus ( Braun & Clarke, 2021 ). Moreover, algorithmic features—such as recommendation systems and behavioral targeting—amplify the relevance and visibility of this content at the decision point ( Lecinski, 2011 ). However, the increasing automation of content visibility raises questions about user autonomy and the transparency of decision-support systems. A critical challenge for marketers and platforms is to balance personalization with ethical content governance, ensuring that emotional or biased content does not disproportionately sway decisions ( Hair Jr. et al., 2010 ).

Rather than viewing decision-making solely as the culmination of rational processing, it should be understood as a complex interplay between digital affordances, emotional heuristics, and peer influence. Thus, future research should adopt a mixed-method approach to capture not only behavioral outcomes but also the psychological dimensions of digital decision-making.

Brands’ encouragement of users to produce original content reduces the risk of spam and increases algorithmic visibility ( Wang, 2021 ). Authentic content promotion refers to the support for the production and sharing of natural, sincere and experience-based content by consumers or users within the framework of digital marketing strategies ( Goh et al., 2013 ; Hearn, 2016 ). This approach is built on the capacity of user-generated content to increase the level of reliability and interaction on digital platforms ( Braun & Clarke, 2021 ; Lemon & Verhoef, 2016 ). Authentic content emerges based on personal experience, insight or emotional feedback rather than a specific commercial purpose ( Lemon & Verhoef, 2016 ). For this reason, it is found more sincere, reliable and convincing by the target audience. Especially in algorithm-based content rankings, such content stands out due to its high interaction rate and potential for positive user feedback, increasing its spreading power ( Braun & Clarke, 2021 ; Wang, 2021 ).

Authentic content promotion is also a strategic communication tool for brands. This promotion can be achieved through different mechanisms such as direct financial rewards, social recognition, special access or loyalty programs ( Goh et al., 2013 ; Hearn, 2016 ). For example, by asking its customers to share their experiences, a brand both supports natural content production and strengthens the social proof effect ( Lemon & Verhoef, 2016 ). Such incentives transform the user into a part of the digital brand community, not only a consumer but also a content partner ( Braun & Clarke, 2021 ; Wang, 2021 ). In addition, authentic content production provides advantages in terms of visibility and access, as it is a type of content that spam filtering systems intervene less and algorithms reward with positive signals ( Lemon & Verhoef, 2016 ). Content with high authenticity establishes a stronger relationship of trust because it does not carry suspicions of artificiality or manipulation ( Goh et al., 2013 ; Hearn, 2016 ). However, the main element to be considered in encouraging authentic content is to carry out the incentive without damaging the naturalness of the content production process ( Barger et al., 2016 ). Overly directed or formally controlled sharing can question the authenticity of the content and have an adverse effect ( Goh et al., 2013 ; Hearn, 2016 ). For this reason, it is important for brands to establish an open, transparent and voluntary relationship with the user in order to maintain the sincerity level of the content ( Braun & Clarke, 2021 ; Gurjar et al., 2019 ). While much of the existing literature highlights the benefits of authentic user-generated content in enhancing trust and engagement ( Goh et al., 2013 ; Lemon & Verhoef, 2016 ), there is a need to move beyond descriptive summaries toward actionable frameworks. Authentic content promotion should not be treated merely as a technical strategy for boosting algorithmic visibility but as a value-based communication approach that strengthens consumer-brand co-creation.

Strategic authenticity can be operationalized through three mechanisms:

1. Contextual Incentivization: Rather than offering generic rewards, brands should design incentives aligned with users’ intrinsic motivations—such as exclusive previews for early adopters or storytelling contests tailored to lifestyle niches ( Wang, 2021 ).

2. Content Autonomy: Encouraging genuine storytelling requires relinquishing control. Instead of dictating content formats, brands should provide open-ended prompts or themes, which preserve creativity while aligning with brand identity ( Barger et al., 2016 ).

3. Relational Recognition: Social acknowledgment—such as featuring user content on official channels or granting community roles—reinforces the social value of authentic contributions ( Hearn, 2016 ; Gurjar et al., 2019 ).

Moreover, brands must guard against the instrumentalization of authenticity, where overly incentivized or manipulated UGC loses its organic appeal. Content that appears staged or commercially motivated is often penalized by users and algorithms alike, leading to reduced trust ( Braun & Clarke, 2021 ). To mitigate this, companies should implement transparency protocols, such as disclosing partnerships while preserving the creator’s narrative voice.

In sum, authentic content promotion should not be viewed as a promotional tactic alone but as part of a broader trust-building and community-forming strategy. Future implementations should emphasize authenticity not just as an aesthetic, but as a process rooted in voluntary participation, mutual value, and transparency.