The Four Pillars of Excellence™ and the Digital Fight for Truth


The Four Pillars of Excellence™ and the Digital Fight for Truth

Playing the Algorithm Game: When Digital Advocacy Becomes Loud Enough to Matter

I used to use social media mainly to keep my friends and family updated about my life, including my business, Zammtopia, and how I was building it.

But the more I work on my business, and the more I try to honour the philosophy I live by — the Zammtopia philosophy built on the four pillars of excellence: critical thinking, personal agency, consistency, and intuition — the more I realise that by continuously posting, I am participating in and contributing to an already very loud digital landscape. A landscape that is becoming more toxic, especially now that society has become used to it — and continues feeding it.

I have always been vocal about building Zammtopia with integrity and ethics. I refuse to become a digital serf in the emerging societal norm of digital feudalism, where platforms own the land, set the rules, harvest attention, and leave the rest of us competing for visibility on ground we will never truly control.

So, for months, I avoided posting anything if I could help it. My posts were mostly relegated to Zammtopia’s social media. I even rendered my personal account inactive — not technically inactive, but inactive through not posting.

However, recently, at the same time that I was deliberately abstaining from posting, I found myself unable to see and unsee, to see and do nothing, when the state of affairs ran so directly against my principles and my belief in what humanity is supposed to be.

Yes, I have been doomscrolling about how broken our world is.

I have been doomscrolling and actively participating, playing the algorithm’s game to add loudness around human rights violations, the genocide in Gaza, the conflict in Lebanon, the endless war between Russia and Ukraine, the hegemon starting a war and underestimating the underdog — consequently having the world pay for its mistake with the Strait of Hormuz — and the testimonies of people reporting from the ground, often at serious personal risk, even death.

“The only thing necessary for the triumph of evil is for good men to do nothing.”

And the world has been doing nothing for the past 80 years. So for all these years, the world has established it is far from good.

When I say “the world”, I mean the powers that be: the people in government at the highest levels; the leaders of countries who had no business dividing a land that was never theirs and handing its fate over to others — expecting peace and harmony to prevail; the world organisations that were supposed to protect humanity; and the institutions that have been rendered useless because they have become embedded with parties representing the perpetrators themselves.

If I keep silent, I become part of the world’s majority: complicit.

I cannot, in good conscience, stay silent. Not after so many people of great conviction have given up their lives in pursuit of even the most basic universal right to exist — the right to equality in how life is treated and valued, regardless of skin colour, race, nationality, faith, or difference.

That is why I began reposting — a lot. I wanted to help certain stories travel beyond the people already paying attention. I wanted to use the tools available, even when those tools felt compromised, because urgent testimony still needs movement, visibility, and witness. I stay awake until very late, until I can no longer fight sleep — but up to that very point, I repost about Gaza, Lebanon, and the atrocities that are happening in front of us — the world.

I have been labouring for the digital feudal lords, playing their algorithms’ game to add to the loudness of these issues. I let the noise devour me because someone inside that digital landscape is telling the truth. I dive in to search for what needs to move. I lift the stories that deserve more than a passing glance. I use the platform as it exists, knowing that it follows its own logic, encouraging it and feeding it.

Digital advocacy moves through systems built to maximise attention, engagement, retention, and profit. I may post with moral urgency, but platforms read behaviour. They measure clicks, shares, comments, watch time, pauses, outrage, repetition, and return visits. I may share a post because I want others to care. A platform may spread the same post because it creates friction.

That is the tension in my mind and spirit: I carry a moral intention, while knowing the platform follows an optimisation model.

Digital advocacy often begins with a simple hope: if enough of us post, share, comment, repeat, and amplify a message, the algorithm will notice. When I add my voice to that noise, I do it because the louder the digital landscape becomes, the greater the chance that a cause might rise into public visibility. During humanitarian crises, war, genocide, and civil rights emergencies, that hope feels urgent. Institutions move slowly. News cycles shift quickly. People on the ground need visibility now.

But as I keep posting, I also see how the research on recommendation engines, engagement ranking, collaborative filtering, online outrage, and platform moderation reveals a more complicated picture. Once my advocacy enters the algorithmic system, the platform decides where it travels. Ranking models prioritise whatever keeps users watching, reacting, arguing, sharing, and coming back — not necessarily what needs to be seen.

So digital advocacy can work, and I have seen moments where it does — but I have also watched it become redirected, polarised, diluted, contained, or turned into a spectacle that serves the platform more than the cause.

Beyond Politics: When Humanity Is Mistaken for Partisanship

One of the more unsettling shifts in the digital age is how quickly humanitarian crises get pushed into the category of “politics”. Once something is given that label, people feel they can step back, as if the suffering belongs to ideology instead of conscience.

Genocide is not a political opinion. Starving civilians is not a political opinion. Bombing hospitals, schools, journalists, aid workers, and children is not a political opinion.

These belong in the realm of morality long before they touch the realm of politics.

Politics concerns how power is organised. Morality deals with how we treat human life. Political systems may create the conditions for conflict, but how we respond to human suffering is a different question entirely. It asks whether we still recognise the worth of another person, no matter their nationality, religion, ethnicity, or the side their government happens to be on.

When human suffering is treated as a political argument, the focus shifts. People stop looking at what is happening to human beings and start looking at which “side” someone is on. The conversation becomes about teams instead of lives.

That shift shows a breakdown in critical thinking, a loss of personal agency, an inconsistency in values, and a quieting of intuition.

The Four Pillars of Excellence ask for something steadier. They ask us to look past slogans, past algorithmic noise, past tribal identity, and past the comfort of selective empathy. They remind us that truth isn’t decided by popularity, and morality isn’t determined by political affiliation.

The question is not whether Gaza, Lebanon, Ukraine, Sudan, or any other humanitarian crisis is politically complex. They are.

The question is whether complexity excuses us from recognising our shared humanity.

It doesn’t.

Doomscrolling as Digital Labour

Doomscrolling is described as the compulsive consumption of distressing news, traumatic imagery, and crisis updates. That description captures part of it, but it misses what happens when I scroll with political and moral purpose.

Because in activist spaces — and in my own experience — doomscrolling becomes digital labour.

I scroll to find testimony before it disappears beneath the next wave of content. I look for evidence that cuts through denial. I repost updates from people on the ground because their words need to travel further than the immediate circle already paying attention. I comment, share, save, and return because the algorithm seems to notice activity before it notices importance.

Recommendation systems measure watch time, clicks, comments, shares, completion rates, dwell time, and repeated interaction. These signals tell the platform what should travel. The system reads attention, friction, intensity, and return behaviour. Because of that, content I share for awareness may move through systems that reward emotional charge more readily than civic understanding.

The logic makes sense. If platforms reward engagement, then engagement becomes a tool. If algorithms amplify what people watch, share, and discuss, then I feel called to become louder, more consistent, and more emotionally present.

But this labour comes with a cost.

I may begin by looking for information and end up carrying the emotional weight of lives lost, homes destroyed, children wounded, families displaced, and communities forced to narrate their own suffering just to be believed. The grief is concrete. It sits in my chest, sharpens my focus, and anchors me to the reality that these are human beings whose lives have been treated as negotiable by those with power.

Then the anger arrives. It points towards the perpetrators, but also towards the machinery around them: the politicians who excuse the violence, the governments that arm it, the institutions that hide behind procedure, the media voices that turn atrocity into debate, and the loyal functionaries who help keep the whole structure intact.

In my desire to understand what this emotional response actually is, my research showed that part of it is described as “retributive anger”. When I witness what I see as a profound moral violation, I feel driven to speak, condemn, share, and act. Posting becomes a way to bear witness. It becomes a way to refuse complicity. It becomes a way to say, "I see this, and I will keep looking."

After that comes the exasperation — the moment I realise how many people can see the same evidence and still allow the system to continue. It is the frustration of watching world leaders speak in rehearsed phrases while people are buried, displaced, starved, bombed, or erased, and the exhaustion of knowing that so much of what we call order depends on selective attention.

Everything I am feeling is heavy — but imagining this heaviness alongside the gravity carried by people trapped in these places, people who have lost everything and many of their loved ones, puts things into perspective. Their reality forces me to pull myself together and go through the same cycle again: feeding the algorithm with their stories every time, because the alternative is silence.

So I keep doomscrolling purposefully because looking away feels like another small surrender to the very system that depends on people becoming tired, numb, distracted, or afraid of saying the wrong thing.

Platforms then translate those emotional expressions into behavioural data. Likes, reposts, comments, and notifications become visible rewards. In my desire to understand why this cycle feels so self‑reinforcing, my research showed that positive social feedback increases future outrage posting. When I receive more engagement than expected, the system creates reinforcement. Over time, I learn that outrage travels.

The system also builds “look‑alike” audiences. When I engage with certain kinds of content, the algorithm groups me with users who behave similarly. My posts then circulate within that homogenised cluster — people who already share my emotional responses, my political leanings, or my patterns of engagement. This means the content I intend as public advocacy often travels inside a narrow corridor of people the platform considers “similar to me”, rather than reaching those who most need to see it.

At times it feels futile — all the advocacy, all the posting, all the effort — because the system is not built to prioritise moral urgency. But what is the alternative? Silence? Looking away? Pretending not to see?

This feedback loop rewards salience. It rewards emotional charge. It rewards the kind of content that makes people stop scrolling.

That is where advocacy becomes vulnerable to distortion. I may intend to raise awareness, while the platform amplifies the post because it provokes anger, conflict, or identity threat. My moral urgency becomes entangled with the platform’s appetite for engagement.

Underneath it all, the algorithm will never understand the moral weight of what I’m sharing — it will never understand grief, guilt, complicity, courage, or witness in the way I do. It will just do its own thing.

    Out-Group Rage and the Engagement Economy

    Another pathway for advocacy content runs through the out‑group rage loop, and I see this play out every time my posts reach hostile audiences. In my desire to understand why this happens, my research showed that engagement‑based ranking systems often surface content to people likely to react. One finding struck me: language referencing political opponents strongly predicts sharing, and each individual term referring to the political out‑group increases the probability of a post being shared by an average of 67%. That insight helped me understand why posts that name, condemn, or challenge an opposing group suddenly explode in reach — not because people agree, but because provocation travels.

    This means my advocacy content about war, genocide, human rights, occupation, or state violence often ends up in hostile audiences. I watch people mock it, dispute it, minimise it, deny it, or counterattack, and the algorithm reads that hostile friction as valuable engagement. I see reach; the platform sees activity; the cause gains visibility — yet persuasion may remain out of reach. That tension is familiar. I want understanding. The system wants reaction.

    Hostile environments deepen polarisation. Genuine persuasion grows in spaces that support civil reasoning, moderated disagreement, and balanced discussion. Engagement‑based feeds elevate emotionally charged, morally loaded, conflict‑heavy content. The conditions that create virality differ from the conditions that create understanding, and this is how digital loudness becomes a trap: a post may travel widely while hardening opposition. It may succeed as content while struggling as advocacy. And even with that awareness, I keep posting, because visibility still matters, even when persuasion does not land where I hoped it would.

    Viral Spillover: When Digital Noise Breaks Through

    Even with these limits, playing the algorithm game can work, and this is one of the reasons I persist. My research showed that digital advocacy becomes most effective when it reaches what the material calls “escape velocity”. This usually happens during acute crises — wars, humanitarian emergencies, civil rights uprisings, or moments of mass public shock. At those moments, the sheer volume of content becomes large enough to break through personalised filters and saturate public discovery spaces. I’ve seen this happen: an issue becomes unavoidable, even for people who were not looking for it.

    This is viral spillover. Content moves beyond activist networks, followers, and already‑aligned audiences. It reaches passive observers, politically unaligned users, and people who might otherwise stay insulated from the issue. Short‑form video platforms matter here. On TikTok, for example, recommendation systems test content with a small audience before expanding distribution. Watch time and completion rate carry strong ranking power. If an advocacy video opens with a compelling visual hook, uses a resonant sound, and keeps viewers watching, the platform may push it to wider audiences through the For You Page.

    In these moments, consistency and digital loudness matter. Repeated posting can make an issue feel socially unavoidable. Coordinated attention can bring a crisis into the awareness of people who might otherwise miss it. Influencers can act as opinion leaders, translating raw digital noise into action by increasing their followers’ belief that collective response matters.

    But viral spillover also brings responsibility. When traumatic content becomes inescapable, unaligned users may experience compassion fatigue, anxiety, helplessness, or emotional shutdown. Some may repost out of guilt while gaining very little understanding. Mass symbolic campaigns can crowd out practical information. My research showed examples of mass reposting campaigns where symbolic content overwhelmed movement hashtags, pushing down emergency resources, updates, and information from people on the ground. In those moments, visibility and usefulness move in different directions.

    And this is the tension I work inside: the system distorts, contains, and redirects advocacy, yet there are moments when it breaks open — moments when attention escapes its usual boundaries and reaches people who would never have seen the issue otherwise. Those moments are rare, but they are real, and they are part of why I continue.

    Platform Containment and Shadow Demotion

    Advocacy also has to contend with platform containment, and this is one of the most demoralising parts of playing the algorithm game. On top of everything I have already described — the emotional weight, the hostile audiences, the homogenised clusters, the feedback loops — the challenges posed by the platforms themselves can feel insurmountable.

    Social media companies use systems that quietly reduce the reach of content classified as political, sensitive, borderline, spam‑like, or potentially harmful to user experience. These systems often limit visibility while leaving the post technically available. Users experience this as shadow banning or shadow demotion: the post remains visible to me and perhaps to my existing followers, but it loses access to search, hashtags, recommendations, Explore pages, Reels, or suggested feeds. I may keep posting, believing I am feeding the algorithm, while the platform quietly narrows the post’s path.

    In my desire to understand why this happens, my research showed that platforms use political content filters, AI moderation tools, spam classifiers, image‑recognition systems, and “do not amplify” tags to restrict reach. This matters for advocacy because many activist strategies resemble patterns that platforms associate with manipulation or spam. Identical graphics, repeated captions, mass posting, copy‑pasted scripts, rapid engagement spikes, and coordinated calls to action can all trigger automated suppression.

    That creates a painful contradiction: the behaviours that make a campaign feel unified and powerful to humans may look automated or inauthentic to machines. For me, loudness alone carries limited power. Consistency needs form, variation, context, and strategy — otherwise the system simply contains it.

    When Becoming Digitally Louder Works

    Even with all these constraints, I have learned that becoming digitally louder can work — but only under particular conditions, and only when I adapt how I show up inside the system.

    It works during moments of acute public attention. When a crisis has already entered news cycles and public consciousness, consistent posting can help sustain visibility. The algorithm is more willing to reward content connected to a fast‑moving attention wave, and I can feel the difference: posts travel further, reach people outside my usual circles, and carry more weight.

    It works when the content captures attention through clarity and storytelling rather than outrage alone. Strong hooks, credible evidence, human testimony, and emotionally grounded framing increase completion rates and shares while preserving dignity. When I take the time to frame something clearly, people stay with it longer, and the platform notices.

    It works when I vary my language. Instead of repeating identical captions, I rewrite calls to action in my own words. This lowers the risk of spam detection and makes the message feel more human. It also gives different audiences different emotional and cultural entry points, which matters when the goal is understanding rather than noise.

    It works when I use text and images thoughtfully. If platforms use image‑processing models to detect politically charged terms embedded in graphics, I place key information in captions, source links, alt text, or accompanying context. The message stays legible, and the system is less likely to suppress it.

    It works when advocacy builds micro‑communities. Responding to comments, answering questions, sharing credible sources, and inviting civil discussion creates higher‑quality engagement. Persuasion grows more readily in spaces where people can reflect rather than defend themselves, and I have seen how a single thoughtful exchange can shift someone’s perspective more than a thousand viral posts.

    It works when trusted community figures amplify the message. The “two‑step flow of communication” still matters online. People often receive difficult information more openly when it comes from someone they already trust, and I have watched how a single share from a respected voice can open doors that my own posts could not.

    And it works when awareness connects to action. Awareness can become a loop — a cycle of posting, reacting, and reposting that never leaves the screen. But awareness paired with donations, petitions, mutual aid, political pressure, educational resources, community organising, and offline mobilisation has a stronger chance of becoming meaningful.

    The Zammtopia Lens: Semiotic Hygiene in the Age of Digital Noise

    From a Zammtopia perspective, this issue is also a problem of semiotics — and this is where my conviction becomes relevant. I’m bringing in Zammtopia’s Four Pillars of Excellence — critical thinking, personal agency, consistency, and intuition — because they are the principles I live by. They reinforce my belief that humanity should treat each other with more compassion, equality, and constructive intent. These pillars help me make sense of the surreal lunacy that has bespelled the world, guide my decision to amplify posts and messages that push against that tide, and remind me why I need to keep showing up.

    Directed Loudness

    If you are anything like me — someone who avoids social media and refuses to become a digital serf in a digital feudal world — this is the moment to recognise that the issues needing our support, our voice, and our digital advocacy are far greater than our discomfort. Staying silent, complicit, or detached does not protect us; it erodes our conscience. It allows the slow death of our humanity. It is a way of giving up on the world by burying our heads in the sand.

    Now is the time to become noisy, loud, and amplifying. Not for the sake of noise itself, but because people are fighting for their right to live, to exist, and to determine their own future. Our brothers and sisters need their stories carried beyond the boundaries that have trapped them. They need their reality to reach those who would never otherwise see it. So yes, get into the algorithm stream, but never lose sight of what you are doing and why you are doing it — always. This is the only way to use it in advocacy and survive it.

    References

    1. Nous Network. “Designed for Outrage: Inside the Algorithm That Fuels Hate.” https://www.nousnetwork.org/designed-for-outrage-inside-the-algorithm-that-fuels-hate/
    2. The University Times. “Social Media Activism: A Force for Change or Mere Performance.” https://universitytimes.ie/2025/03/social-media-activism-a-force-for-change-or-mere-performance/
    3. The Mancunion. “Social Media and the Illusion of Activism.” https://mancunion.com/2024/11/13/social-media-and-the-illusion-of-activism/
    4. National Library of Medicine. “Activism in the Digital Age: The Link Between Social Media...” https://pmc.ncbi.nlm.nih.gov/articles/PMC10293473/
    5. National Library of Medicine. “How Social Learning Amplifies Moral Outrage Expression in Online...” https://pmc.ncbi.nlm.nih.gov/articles/PMC8363141/
    6. Hootsuite. “How the TikTok Algorithm Works in 2026.” https://blog.hootsuite.com/tiktok-algorithm/
    7. Polytechnique Insights. “Are Online Recommendation Algorithms Polarising Users’ Views?” https://www.polytechnique-insights.com/en/columns/digital/are-recommendation-algorithms-a-source-of-polarization/
    8. PNAS Nexus. “Engagement, User Satisfaction, and the Amplification of Divisive...” https://academic.oup.com/pnasnexus/article/4/3/pgaf062/8052060
    9. Knight First Amendment Institute. “Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media.” https://knightcolumbia.org/content/engagement-user-satisfaction-and-the-amplification-of-divisive-content-on-social-media
    10. Illinois Institute of Technology. “The Interaction Between Political Typology and Filter Bubbles in...” http://www.cs.iit.edu/~ml/pdfs/liu-www21.pdf
    11. arXiv. “Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media.” https://arxiv.org/html/2305.16941v6
    12. Chosun. “Social Media Algorithms Fuel Political Hostility During Elections.” https://www.chosun.com/english/industry-en/2026/05/28/OETFSVKHSJCWRO3JANV2WPH6ZA/
    13. National Library of Medicine. “Designing Social Media Content Recommendation Algorithms for Societal Good.” https://pmc.ncbi.nlm.nih.gov/articles/PMC12220283/
    14. Grokipedia. “Shadow Banning.” https://grokipedia.com/page/Shadow_banning
    15. PNAS. “The Effects of Social Media Criticism Against Public Health Institutions on Trust, Emotions, and Social Media Engagement.” https://www.pnas.org/doi/10.1073/pnas.2422890122
    16. Christian Standard. “Does Social Media Activism Do More Harm Than Good?” https://christianstandard.com/2026/02/does-social-media-activism-do-more-harm-than-good/
    17. National Library of Medicine. “Out-Group Animosity Drives Engagement on Social Media.” https://pmc.ncbi.nlm.nih.gov/articles/PMC8256037/
    18. Digg. “PNAS Study Finds Users Post Negative Comments to Appear Distinctive, Eroding Social Media Positivity Over Time.” https://digg.com/tech/x32t1z8j
    19. Wikipedia. “Collaborative Filtering.” https://en.wikipedia.org/wiki/Collaborative_filtering
    20. Redis. “Collaborative Filtering: How to Build a Recommender System.” https://redis.io/blog/collaborative-filtering-how-to-build-a-recommender-system/
    21. Illinois Institute of Technology. “Bias in the Bubble: New Research Shows News Filter Algorithms Reinforce Political Biases.” https://www.iit.edu/news/bias-bubble-new-research-shows-news-filter-algorithms-reinforce-political-biases
    22. IBM. “What Is Collaborative Filtering?” https://www.ibm.com/think/topics/collaborative-filtering
    23. Andreas Bloch. “An Overview of Collaborative Filtering Algorithms for Implicit Feedback Data.” https://andbloch.github.io/An-Overview-of-Collaborative-Filtering-Algorithms/
    24. Journal of Information and Communication Convergence Engineering. “U-Net-Based Recommender Systems for Political Election System Using Collaborative Filtering Algorithms.” https://test-jicce.inforang.com/journal/view.html?doi=10.56977/jicce.2024.22.1.7
    25. HaystaqDNA. “Political Look-Alike Modeling.” https://haystaqdna.com/political-look-alike/
    26. Fung Institute. “Op-ed: Social Media Algorithms & Their Effects on American Politics.” https://funginstitute.berkeley.edu/news/op-ed-social-media-algorithms-their-effects-on-american-politics/
    27. The Jerusalem Post. “Meta Rejects Claims 2025 Policy Changes Fueled Rise in Antisemitic Content.” https://www.jpost.com/diaspora/antisemitism/article-901605
    28. Behavioural Insights Team. “Social Media Algorithms Amplify Right-Wing Content Against Young Users’ Preference, Study Finds.” https://www.bi.team/press-releases/social-media-algorithms-amplify-right-wing-content-against-young-users-preference-study-finds/
    29. Taylor & Francis. “Political Persuasion on Social Media: A Moderated Moderation Model of Political Discussion Disagreement and Civil Reasoning.” https://www.tandfonline.com/doi/full/10.1080/01972243.2018.1497743
    30. Valerie Giron. “How Algorithmic Discrimination Exacerbates U.S. Partisan Tensions on Social Media Platforms.” https://www.polisci.uci.edu/files/docs/theses/2024-25/2025_valerie_giron.pdf
    31. National Library of Medicine. “Ingroup Solidarity Drives Social Media Engagement After Political Crises.” https://pmc.ncbi.nlm.nih.gov/articles/PMC12415245/
    32. SocialPilot. “TikTok Algorithm: How FYP Works & Tips to Go Viral.” https://www.socialpilot.co/blog/tiktok-algorithm
    33. Kamrun Nahar. “TikTok’s Algorithm Explained Like You’re 5.” https://iknahar.medium.com/tiktoks-algorithm-explained-like-you-re-5-because-the-official-docs-explain-it-like-you-re-50-0c02479be44f
    34. Agorapulse. “What You Need to Know About the TikTok Algorithm to Go Viral in 2026.” https://www.agorapulse.com/blog/tiktok/tiktok-algorithm/
    35. Oxford Academic. “Social Media Influencers Can Increase Collective Political Beliefs and Actions.” https://academic.oup.com/hcr/article/52/3/147/8300321
    36. R Street Institute. “Regulating Algorithmic Content Distribution and Moderation by Online Platforms.” https://www.rstreet.org/research/regulating-algorithmic-content-distribution-and-moderation-by-online-platforms/
    37. Repro Uncensored. “How to Avoid Instagram Shadowbans in 2026: A Guide for Activists.” https://www.reprouncensored.org/research-overview/shadowban-guide
    38. TIME. “Instagram’s Political Content Limit: Everything to Know.” https://time.com/6960587/meta-instagram-political-content-limit-off-setting-default/
    39. Mashable. “Instagram and Threads Are Automatically Limiting Political Content.” https://mashable.com/article/instagram-threads-limit-political-content
    40. The Guardian. “Instagram Users to See Less of What Meta Deems ‘Political’ Content Unless They Opt In.” https://www.theguardian.com/technology/2024/mar/26/instagram-meta-political-content-opt-in-rules-threads
    41. CNET. “Meta Is Limiting the Political Content You See on Instagram. Here’s How to Prevent It.” https://www.cnet.com/tech/services-and-software/meta-is-limiting-the-political-content-you-see-on-instagram-heres-how-to-prevent-it
    42. GetStream.io. “Shadow Ban — What Is It and How Does It Work?” https://getstream.io/glossary/shadow-ban/
    43. Yale Law School. “Reduction / Borderline Content / Shadowbanning.” https://law.yale.edu/sites/default/files/area/center/isp/documents/reduction_ispessayseries_jul2022.pdf
    44. National Library of Medicine. “Shaping Opinions in Social Networks With Shadow Banning.” https://pmc.ncbi.nlm.nih.gov/articles/PMC10971755/
    45. National Library of Medicine. “Digital Silence: The Psychological Impact of Being Shadow Banned on Mental Health and Self-Perception.” https://pmc.ncbi.nlm.nih.gov/articles/PMC12537705/