Dissemination of User Generated Content without Being Caught by Spam Filters and Its Impact on the Consumer Journey in Terms of Content Strategies ()
1. Introduction
The increasing power of individuals to produce content in the digital age poses both opportunities and risks for brands (Koiso-Kanttila, 2004). User-generated content has the potential to reach large audiences, especially on social media and online platforms (Gokhale, 2016). However, the perception of this content as spam can limit its spread and prevent it from reaching consumers (Barger et al., 2016). This study examines the ways in which user-generated content can be effectively spread without being caught by spam filters and the effects of this on consumer behavior (Smartinsights, 2018; Wang, 2021).
The transformation from traditional media tools to digital platforms with digitalization has deeply affected consumer behavior and brands’ communication strategies (Wallace et al., 2014). In this transformation process, the capacity of users to produce content has increased, especially through social media and online interaction channels (Okazaki et al., 2009). This situation has brought the concept of user-generated content to the forefront, which points to an important paradigm in marketing communication literature (Pigg & Crank, 2004). User-generated content refers to individuals sharing their product, service or brand experiences independently in digital environments. These contents serve as digital references for potential customers (Mondal, 2021).
Despite the increasing impact of user-generated content in the context of marketing, the visibility of these contents in digital media can be limited by various algorithmic barriers (Smartinsights, 2018; Wang, 2021). Spam filtering systems, in particular, are one of the critical factors affecting the spread of content. Spam filters are automated systems developed to prevent the spread of deceptive, misleading or overly promotional content (Morgan-Thomas, Dessart, & Veloutsou, 2020). In this context, even organic and user-generated content is likely to be deprived of visibility as a result of incorrect classification. This situation directly affects both the consumer’s process of accessing information and the content strategies of brands (Montoya-Weiss et al., 2003). The consumer journey is a multi-layered process consisting of stages such as awareness, interest, evaluation, purchase and loyalty (Biçer, 2020). In this process, user-generated content plays an important role in directing consumer behavior by triggering both cognitive and emotional decision-making mechanisms (Moran et al., 2014). However, if these contents are not structured in a way that is compatible with algorithmic filtering processes, they may lose visibility before reaching the consumer. In this context, user-generated content that can be spread without being caught by spam filters needs to be strategically planned and managed (Moran et al., 2014). In today’s information society, where digitalization is gaining momentum, it is seen that individuals are evolving from passive consumers to active content producers (Kee & Yazdanifard, 2015; Barger et al., 2016). This transformation, in particular, re-opens the discussion on the quality, source and reliability of content spread through digital platforms; user-generated content is increasingly assuming a central role in marketing communication literature in this context (Moran et al., 2014). User-generated content includes the types of content that individuals create and share with large audiences through social media, blogs, video platforms or forums based on their personal experiences, observations and comments, and has a decisive effect on the formation of brand perception and consumer decision-making processes (Biçer, 2020). The consumer journey, which has become the focal point of digital marketing strategies in particular, is a multi-layered and dynamic process that extends from the potential customer’s first contact with a product or service to the loyalty stage (Mondal, 2021). Throughout this journey, the consumer encounters various types of content at different touchpoints; their perceptions of the reliability and authenticity of these contents are of critical importance in the decision-making mechanism (Kotler et al., 2016). In this context, user-generated content stands out as a type of content that reinforces consumer trust and increases interaction with the brand by functioning as social proof throughout the entire consumer journey, especially in the awareness and evaluation stages (Biçer, 2020).
However, the expansion of digital communication networks and the democratization of content production have also brought with them problems such as content pollution, spam spread, and manipulation. Spam filtering algorithms developed to combat these problems have become one of the basic technological mechanisms that determine the visibility of content on platforms (Smartinsights, 2018; Wang, 2021). However, these algorithms generally aim to filter out commercial, repetitive, or artificial content, while keeping naturally generated user content separate from these categories and highlighting authentic content at a higher rate. This situation reveals that user-generated content is in an advantageous position in terms of algorithmic visibility. This study aims to address the spread of user-generated content on digital platforms without being caught by spam filters and the impact of this spread on the consumer journey from the perspective of content strategies (Kee & Yazdanifard, 2015; Barger et al., 2016). Within the scope of the study, firstly, the concept of user-generated content will be explained on a theoretical basis, then the functioning of spam filtering mechanisms and the interaction with user-generated content will be discussed (Moran et al., 2014). Finally, the transformative role of content strategies in this process will be evaluated and suggestions for brands will be presented. This approach aims to contribute to the studies on digital content management and user interaction in the academic literature (Kotler et al., 2016).
2. User-Generated Content
User-generated content is content created by users about a product, service or brand and usually shared through digital platforms. This content can be in the form of comments, blog posts, videos, photos or reviews (Kaplan & Haenlein, 2010).
User-generated content refers to the types of content that individuals voluntarily create, share and mostly offer without expecting any commercial return in digital media environments (Kotler et al., 2016). This content can be in various forms such as text, visuals, videos, comments or reviews and is mostly located in digital areas such as social media platforms, blogs, forums and e-commerce sites (Zahra & Noruzi, 2018). Unlike professional content producers, user-generated content is directly fed by consumers’ experiences, perceptions and individual interactions (Gurjar et al., 2019). In this respect, it is considered both an authentic source of information and a dynamic component of the digital interaction process (Koiso-Kanttila, 2004). This form of content production based on consumer participation offers a two-way communication structure, distinguishing it from traditional marketing communication models (Basal & Suzen, 2023; Opreana & Vinerean, 2015). In addition, since it directly reflects users’ perceptions of their interactions with brands, it has a significant impact potential in terms of reliability and social proof (Pulizzi, 2012). The prevalence of user-generated content is not limited to individual sharing, but is shaped by algorithms, platform policies and content strategies (Andaç et al., 2016). This causes it to become a strategic tool in marketing and communication studies (Wu & Liu, 2018).
2.1. Spam Filtering Systems
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).
2.2. Consumer Journey
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).
3. The Role of Spam Algorithms in the Dissemination of User-Generated Content
3.1. Basic Criteria of Algorithms
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).
3.2. The Power of User-Generated Content to Bypass Spam
Algorithms
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).
4. The Effect of User-Generated Content on the Consumer Journey
4.1. Awareness Stage
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).
4.2. Evaluation and Decision-Making Stages
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.
5. User-Generated Content Management in Terms of Content Strategies
Authentic Content Promotion
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.
6. Conclusion and Recommendations
User-generated content can become a powerful communication tool for brands by bypassing spam filters with the right strategies. User-generated content, which provides value at every stage of the consumer journey, should be one of the cornerstones of content strategies. Adhering to the principle of authenticity, encouraging user interaction and planning content compatible with algorithms will increase their digital visibility. In this context, it is recommended that content be qualitatively analyzed, data on spam trends be monitored and strategies be developed that are sensitive to user behaviors.
This study examines the ability of user-generated content (UGC) to bypass spam filtering algorithms and gain visibility in digital environments and its effects on the consumer journey in the context of content strategies. In line with the findings obtained, it has been understood that user-generated content offers multi-layered contributions to digital marketing processes at both technical and perceptual levels (Mansour & Basal, 2024). Despite the development of spam filtering systems based on artificial intelligence and machine learning, UGC has a high potential to bypass these filter systems due to its originality, contextuality and high user interaction. In particular, the fact that algorithms classify and rank content based on user behavior puts UGC, which contains a natural and sincere narrative, in an advantageous position. At every stage of the consumer journey (awareness, evaluation, decision-making, and loyalty), user-generated content stands out with its functions of creating social proof, building trust, and filling information gaps. With factors such as the speed of dissemination of content at the awareness stage, authentic experience sharing during the evaluation process, persuasive effect at the moment of decision, and community connection at the loyalty stage, UGC has a much higher conversion power compared to traditional marketing content. In this context, content strategies need to be restructured, and it has become mandatory for brands to include not only their own messages but also user-generated content within a strategic plan. Encouraging trust, transparency, and participation in digital ecosystems is also critical for UGC to maintain its visibility without being caught up in spam perceptions. Brands should build their content strategies not only on corporate discourses, but also on multi-channel and interaction-based structures to which users can contribute. UGC should be positioned as a complementary element of the marketing mix; It should be actively used, especially in product promotion, brand reputation and digital visibility strategies. Guidelines should be prepared to encourage users to produce content while at the same time not being caught by spam filters. Users should be informed about avoiding spam triggers such as keyword density, link placement and content repetition. Authentic content production should be increased with applications such as loyalty programs, user competitions and social media hashtag campaigns that support the natural sharing of consumer experiences. However, these incentives should be structured on a voluntary basis without damaging the naturalness of the content.
Video comments, micro content, short text evaluations and visual-based user shares are prioritized more by digital algorithms. Therefore, users should be directed to produce such content with high interaction rates. Different types of UGC examples should be used in each of the awareness, evaluation, decision and loyalty stages and the impact of these contents should be analyzed regularly. For example, while detailed product reviews are highlighted in the evaluation stage, short social media experiences will be more appropriate in the awareness stage. In order to ensure that UGC spreads without being caught by spam filters and to measure its impact, content performance metrics (views, clicks, interaction rate, conversion) should be monitored; content strategies should be constantly updated in light of this data. When developing strategies to increase the visibility of UGC, the impact of algorithmic biases on user diversity should also be taken into account; fair and inclusive practices should be adopted in terms of content visibility. In this context, user-generated content should be considered not only as a type of content, but also as a strategic asset that plays a central role in building digital trust, interaction, and loyalty. The success of digital content strategies is now shaped by what users say, rather than what brands say.
This study contributes to the existing literature by offering a multi-dimensional analysis of user-generated content (UGC) as both a technical artifact capable of bypassing algorithmic spam filters and a strategic communication tool that enriches the consumer journey at multiple stages. Unlike much of the existing work that focuses either on the psychological impact of UGC or on algorithmic ranking in isolation, this research integrates both perspectives to argue that UGC is a hybrid structure—simultaneously affective and algorithmically functional.
One of the unique contributions of this study lies in its demonstration that UGC’s effectiveness is not solely derived from its authenticity or emotional appeal, but also from its adaptive compatibility with algorithmic visibility systems. This dual role of UGC—as a social trust mechanism and a system-optimized format—has been under-theorized in prior literature. While previous research (e.g., Goh et al., 2013; Lemon & Verhoef, 2016) has emphasized UGC’s persuasive power in consumer decision-making, this study foregrounds its resilience in digital filtration systems, which significantly enhances its marketing utility in highly competitive, noise-saturated digital environments.
Another original aspect is the operational framework proposed for integrating UGC into strategic content planning across all four stages of the consumer journey (awareness, evaluation, decision-making, and loyalty). By mapping distinct UGC formats—such as video reviews, micro-comments, or hashtag-driven campaigns—onto each stage of the journey, the study provides a nuanced, stage-specific content architecture that goes beyond generic prescriptions.
Moreover, the study extends the conversation on content visibility by highlighting the risks of algorithmic bias and proposing inclusive, fair content dissemination strategies. This not only fills a notable gap in the literature on digital equity but also aligns UGC management with broader ethical considerations, particularly relevant in the era of AI-driven curation and personalization.
To operationalize these findings, brands should:
Develop UGC-specific content guidelines that optimize for both authenticity and algorithmic compatibility.
Promote natural sharing behaviors through voluntary incentive structures like user challenges or recognition-based rewards, without compromising content sincerity.
Design UGC dashboards that continuously monitor performance metrics (e.g., interaction rates, CTRs, conversions) and adapt strategies accordingly.
Train digital marketing teams in algorithm-awareness literacy, especially around spam trigger mechanisms and content diversity optimization.
Ensure that content moderation and visibility practices account for algorithmic bias, thereby fostering digital environments that are inclusive and trust-based.
In conclusion, this research redefines UGC not merely as a byproduct of digital participation but as a strategic asset with systemic implications for digital marketing efficacy and equity. By synthesizing insights from algorithm studies, consumer behavior, and content strategy, the study offers a framework that is both theoretically rich and practically applicable. In an age where brand narratives are increasingly co-authored by users, the success of content strategies hinges less on what brands broadcast and more on what users create, share, and amplify.