# Algorithmic radicalization

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Radicalization via social media algorithms

**Algorithmic radicalization** is the concept that [recommender algorithms](/source/Recommender_algorithm) on popular social media sites, such as [YouTube](/source/YouTube) and [Facebook](/source/Facebook), drive users toward progressively more extreme content over time, leading to the development of [radicalized](/source/Radicalization) extremist political views. Algorithms meticulously record user interactions, encompassing likes, dislikes and the duration of time watching content, with the objective of generating an endless stream of media designed to sustain [user engagement](/source/User_engagement). The phenomenon of [echo chamber](/source/Echo_chamber_(media)) channels has been demonstrated to exacerbate the [polarization](/source/Polarization_(politics)) of consumers, primarily through the reinforcement of media preferences and the validation of one's existing beliefs.[1][2][3][4]

Algorithmic radicalization remains a controversial phenomenon as it is often not in the best interest of social media companies to remove echo chamber channels.[5][6] To what extent recommender algorithms are actually responsible for radicalization remains disputed. Studies have found contradictory results regarding the promotion of extremist content by algorithms.

## Social media echo chambers and filter bubbles

Social media platforms learn the interests and likes of the user to modify their experiences in their feed to keep them engaged and scrolling, known as a [filter bubble](/source/Filter_bubble).[7] An echo chamber is formed when users come across beliefs that magnify or reinforce their thoughts and form a group of like-minded users in a closed system.[1] Echo chambers spread information without any opposing beliefs and can possibly lead to [confirmation bias](/source/Confirmation_bias). According to [group polarization](/source/Group_polarization) theory, an echo chamber can potentially lead users and groups towards more extreme radicalized positions.[8] According to the National Library of Medicine, "Users online tend to prefer information adhering to their worldviews, ignore dissenting information, and form polarized groups around shared narratives. Furthermore, when polarization is high, misinformation quickly proliferates."[8]

## By site

### Facebook

[Facebook](/source/Facebook)'s algorithm focuses on recommending content that makes the user want to interact. They rank content by prioritizing popular posts by friends, viral content, and sometimes divisive content. Each feed is personalized to the user's specific interests which can sometimes lead users towards an echo chamber of troublesome content.[9] Users can find their list of interests the algorithm uses by going to the "Your ad Preferences" page. According to a Pew Research study, 74% of Facebook users did not know that list existed until they were directed towards that page in the study.[10] It is also relatively common for Facebook to assign political labels to their users. In recent years,[*[when?](https://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style/Dates_and_numbers#Chronological_items)*] Facebook has started using artificial intelligence to change the content users see in their feed and what is recommended to them. A document known as [The Facebook Files](/source/The_Facebook_Files) has revealed that their AI system prioritizes [user engagement](/source/User_engagement) over everything else. The Facebook Files has also demonstrated that controlling the AI systems has proven difficult to handle.[11]

In an August 2019 internal memo leaked in 2021, Facebook has admitted that "the mechanics of our platforms are not neutral",[12][13] concluding that in order to reach maximum profits, optimization for engagement is necessary. In order to increase engagement, algorithms have found that hate, misinformation, and politics are instrumental for app activity.[14] As referenced in the memo, "The more incendiary the material, the more it keeps users engaged, the more it is boosted by the algorithm."[12] According to a 2018 study, "false rumors spread faster and wider than true information... They found falsehoods are 70% more likely to be retweeted on Twitter than the truth, and reach their first 1,500 people six times faster. This effect is more pronounced with political news than other categories."[15]

### YouTube

[YouTube](/source/YouTube) has been around since 2005 and has more than 2.5 billion monthly users. YouTube discovery content systems focus on the user's personal activity (watched, favorites, likes) to direct them to recommended content. YouTube's algorithm is accountable for roughly 70% of users' recommended videos and what drives people to watch certain content.[16] According to a 2022 study by the [Mozilla Foundation](/source/Mozilla_Foundation), users have little power to keep unsolicited videos out of their suggested recommended content. This includes videos about hate speech, livestreams, etc.[17][16]

YouTube has been identified as an influential platform for spreading radicalized content. [Al-Qaeda](/source/Al-Qaeda) and similar [extremist groups](/source/Extremist_group) have been linked to using YouTube for recruitment videos and engaging with international media outlets. In a research study published by the *American Behavioral Scientist Journal*, they researched "whether it is possible to identify a set of attributes that may help explain part of the YouTube algorithm's decision-making process".[18] The results of the study showed that YouTube's algorithm recommendations for extremism content factor into the presence of radical keywords in a video's title. In February 2023, in the case of [Gonzalez v. Google](/source/Gonzalez_v._Google_LLC), the question at hand is whether or not Google, the parent company of YouTube, is protected from lawsuits claiming that the site's algorithms aided terrorists in recommending [ISIS](/source/ISIS) videos to users. [Section 230](/source/Section_230) is known to generally protect online platforms from civil liability for the content posted by its users.[19]

Multiple studies have found little to no evidence to suggest that YouTube's algorithms direct attention towards far-right content to those not already engaged with it.[20][21][22]

### TikTok

[TikTok](/source/TikTok) is a platform that recommends videos to a user's 'For You Page' (FYP), making every users' page different. With the nature of the algorithm behind the app, TikTok's FYP has been linked to showing more explicit and radical videos over time based on users' previous interactions on the app.[23] Since TikTok's inception, the app has been scrutinized for misinformation and hate speech as those forms of media usually generate more interactions to the algorithm.[24]

Various extremist groups, including [jihadist](/source/Jihadist) organizations, have utilized TikTok to disseminate propaganda, recruit followers, and incite violence. The platform's algorithm, which recommends content based on user engagement, can expose users to extremist content that aligns with their interests or interactions.[25][*[failed verification](https://en.wikipedia.org/wiki/Wikipedia:Verifiability)*]

As of 2022, TikTok's head of US Security has put out a statement that "81,518,334 videos were removed globally between April – June for violating our Community Guidelines or Terms of Service" to cut back on hate speech, harassment, and misinformation.[26]

Studies have noted instances where individuals were radicalized through content encountered on TikTok. For example, in early 2023, Austrian authorities thwarted a plot against an LGBTQ+ [pride parade](/source/Pride_parade) that involved two teenagers and a 20-year-old who were inspired by jihadist content on TikTok. The youngest suspect, 14 years old, had been exposed to videos created by Islamist influencers glorifying jihad. These videos led him to further engagement with similar content, eventually resulting in his involvement in planning an attack.[25]

Another case involved the arrest of several teenagers in [Vienna, Austria](/source/Vienna%2C_Austria), in 2024, who were planning to carry out a terrorist attack at a [Taylor Swift](/source/Taylor_Swift) concert. The investigation revealed that some of the suspects had been radicalized online, with TikTok being one of the platforms used to disseminate extremist content that influenced their beliefs and actions.[25]

## Self-radicalization

See also: [Radicalization](/source/Radicalization)

An infographic from the United States Department of Homeland Security's "If You See Something, Say Something" campaign. The campaign is a national initiative to raise awareness to homegrown terrorism and terrorism-related crime.

The U.S. Department of Justice defines 'Lone-wolf' (self) terrorism as "someone who acts alone in a terrorist attack without the help or encouragement of a government or a terrorist organization".[27] Through social media outlets on the internet, 'Lone-wolf' terrorism has been on the rise, being linked to algorithmic radicalization.[28] Through echo-chambers on the internet, viewpoints typically seen as radical were accepted and quickly adopted by other extremists.[29] These viewpoints are encouraged by forums, group chats, and social media to reinforce their beliefs.[30]

## References in media

### *The Social Dilemma*

Main article: [The Social Dilemma](/source/The_Social_Dilemma)

This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed. (November 2023) (Learn how and when to remove this message)

*The Social Dilemma* is a 2020 docudrama about how algorithms behind social media enables addiction, while possessing abilities to manipulate people's views, emotions, and behavior to spread conspiracy theories and disinformation. The film repeatedly uses buzz words such as 'echo chambers' and '[fake news](/source/Fake_news)' to prove [psychological manipulation](/source/Psychological_manipulation) on social media, therefore leading to [political manipulation](/source/Political_manipulation_of_social_media). In the film, Ben falls deeper into a [social media addiction](/source/Social_media_addiction) as the algorithm found that his social media page has a 62.3% chance of long-term engagement. This leads into more videos on the recommended feed for Ben and he eventually becomes more immersed into propaganda and conspiracy theories, becoming more polarized with each video.

## Proposed solutions

Part of a series on Counterterrorism and Countering violent extremism Methods and approaches Deradicalization Government orgs Counter Terrorism Centre Counterterrorism Mission Center National Counter Terrorism Centre Countering Violent Extremism Task Force National Counterterrorism Center US House Subcommittee Bureau of Counterterrorism CTFD US Senate Subcommittee Counter Terrorism Policing United Nations Office of Counter-Terrorism UN CTITF Individual Offices Coordinator for Counterterrorism Independent Reviewer Systems National Terrorism Advisory System War on terror Terrorist Finance Tracking Program Schools and institutes Combating Terrorism Center CTEC Chicago Project on Security and Threats INSCT START Handa Centre Think tanks International Centre for Counter-Terrorism Terrorism Research Center International Institute for Counter-Terrorism Global Internet Forum to Counter Terrorism Jamestown Foundation Uppsala Conflict Data Program Counter Extremism Project The Investigative Project on Terrorism Intelligence and Terrorism Information Center Global efforts Counterterrorism in China Counterterrorism in Australia Counter-terrorism in Singapore Counter-terrorism in Malaysia Counterterrorism in Colombia Counterterrorism in Argentina Counterterrorism in Canada Counterterrorism in Azerbaijan Legislation Anti-terrorism legislation Incitement Incitement to terrorism Terrorism insurance Executive Order 13224 Anti-Terrorism Act of 2020 v t e

The examples and perspective in this section deal primarily with the US and do not represent a worldwide view of the subject. You may improve this section, discuss the issue on the talk page, or create a new section, as appropriate. (March 2026) (Learn how and when to remove this message)

### United States: Weakening Section 230 protections

Main article: [Section 230](/source/Section_230)

In the [Communications Decency Act](/source/Communications_Decency_Act), Section 230 states that "No provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider".[31] Section 230 protects the media from liabilities or being sued of third-party content, such as illegal activity from a user.[31] However, critics argue that this approach reduces a company's incentive to remove harmful content or misinformation, and this loophole has allowed social media companies to maximize profits through pushing radical content without legal risks.[32] This claim has itself been criticized by proponents of Section 230, as prior to its passing, courts had ruled in *[Stratton Oakmont, Inc. v. Prodigy Services Co.](/source/Stratton_Oakmont%2C_Inc._v._Prodigy_Services_Co.)* that moderation in any capacity introduces a liability to content providers as "publishers" of the content they chose to leave up.[33]

Lawmakers have drafted legislation that would weaken or remove Section 230 protections over algorithmic content. [House Democrats](/source/House_Democratic_Caucus) [Anna Eshoo](/source/Anna_Eshoo), [Frank Pallone Jr.](/source/Frank_Pallone_Jr.), [Mike Doyle](/source/Michael_F._Doyle), and [Jan Schakowsky](/source/Jan_Schakowsky) introduced the "Justice Against Malicious Algorithms Act" in October 2021 as [H.R. 5596](https://www.congress.gov/bill/117th-congress/house-bill/5596). The bill died in committee,[34] but it would have removed Section 230 protections for service providers related to personalized [recommendation algorithms](/source/Recommender_system) that present content to users if those algorithms knowingly or recklessly deliver content that contributes to physical or severe emotional injury.[35]

## See also

- [Algorithmic curation](/source/Algorithmic_curation)

- [Alt-right pipeline](/source/Alt-right_pipeline)

- [Ambient awareness](/source/Ambient_awareness)

- [Complex contagion](/source/Complex_contagion)

- [Computational propaganda](/source/Computational_propaganda)

- [Dead Internet theory](/source/Dead_Internet_theory)

- [Disinformation attack](/source/Disinformation_attack)

- [Doomscrolling](/source/Doomscrolling)

- [Echo chamber](/source/Echo_chamber_(media))

- [Enshittification](/source/Enshittification)

- [Extremism](/source/Extremism)

- [False consensus effect](/source/False_consensus_effect)

- [Far-right usage of social media](/source/Far-right_usage_of_social_media)

- [Filter bubble](/source/Filter_bubble)

- [Influence-for-hire](/source/Influence-for-hire)

- [Misinformation effect](/source/Misinformation_effect)

- [Online youth radicalization](/source/Online_youth_radicalization)

- [Radical trust](/source/Radical_trust)

- [Selective exposure theory](/source/Selective_exposure_theory)

- [Social bot](/source/Social_bot)

- [Social data revolution](/source/Social_data_revolution)

- [Social influence bias](/source/Social_influence_bias)

- [Social media bias](/source/Media_bias#Social_media_bias)

- [Vicarious trauma after viewing media](/source/Vicarious_trauma_after_viewing_media)

- [Virtual collective consciousness](/source/Virtual_collective_consciousness)

- [Schizoposting](/source/Schizoposting)

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1. **[^](#cite_ref-34)** ["H.R. 5596 (117th): Justice Against Malicious Algorithms Act of 2021"](https://www.govtrack.us/congress/bills/117/hr5596). [GovTrack](/source/GovTrack). Retrieved April 11, 2024.

1. **[^](#cite_ref-35)** Robertson, Adi (October 14, 2021). ["Lawmakers want to strip legal protections from the Facebook News Feed"](https://www.theverge.com/2021/10/14/22726290/malicious-algorithms-section-230-bill-eshoo-pallone-doyle-schakowsky-facebook-whistleblower). *[The Verge](/source/The_Verge)*. [Archived](https://web.archive.org/web/20211014170451/https://www.theverge.com/2021/10/14/22726290/malicious-algorithms-section-230-bill-eshoo-pallone-doyle-schakowsky-facebook-whistleblower) from the original on October 14, 2021. Retrieved October 14, 2021.

v t e Social networks and social media Types Personal Professional Sexual Value Clique Adolescent Networks Corporate social media Distributed social network (list) Enterprise social networking Enterprise social software Mobile social network Personal knowledge networking Services List of social networking services List of virtual communities with more than 1 million users Concepts and theories Ambient awareness Assortative mixing Attention inequality Interpersonal bridge Organizational network analysis Small-world experiment Social aspects of television Social capital Social data revolution Social exchange theory Social identity theory Social media and psychology Social media intelligence Social media mining Social media optimization Social network analysis Social web Structural endogamy Virtual collective consciousness Models and processes Account verification Aggregation Change detection Blockmodeling Collaboration graph Collaborative consumption Giant Global Graph Lateral communication Reputation system Social bot Social graph Social media analytics Social network analysis software Social networking potential Social television Structural cohesion Economics Affinity fraud Attention economy Collaborative finance Creator economy Influencer marketing Narrowcasting Sharing economy Social commerce Social sorting Viral marketing Phenomena Algorithmic radicalization Algorithmic amplification Community recognition Complex contagion Computer addiction Consequential strangers Friend of a friend Friending and following Friendship paradox Influence-for-hire Internet addiction Information overload Overchoice Six degrees of separation Social media addiction Social media and suicide Social invisibility Social network game Suicide and the Internet Tribe Viral phenomenon Related topics Friendship recession Peer pressure User profile Online identity Persona Social profiling Researchers Viral messages Virtual community

v t e Media and human factors Cognitive psychology Externality Evolutionary psychology Behavioral modernity Cognition Mismatch Media psychology Media studies Social psychology Media practices Betteridge's law of headlines Gatekeeping Infotainment Human-interest story Junk food news Least objectionable program Soft media Journalistic scandal Media bias Media manipulation Pink-slime journalism Political endorsement Propaganda Public relations Missing white woman syndrome News values Sensationalism Hot take Spiking Tabloid television Trial by media Yellow journalism Attention Attention economy Attention inequality Attention management Attention span Chumbox Clickbait Cognitive miser Low information voter Digital zombie Phubbing Doomscrolling Human multitasking Media multitasking Mobile phones and driving safety Smartphones and pedestrian safety Texting while driving Influence-for-hire Infodemic Information explosion Information overload Information pollution Information–action ratio Rage farming Screen time Binge-watching Television consumption Sticky content Cognitive bias/ Conformity Availability cascade Availability heuristic Bandwagon effect Confirmation bias Crowd psychology Mobbing Moral panic Mean world syndrome Negativity bias Peer pressure Social-desirability bias Social influence bias Spiral of silence Digital divide/ Political polarization Algorithmic radicalization Youth Algorithmic amplification Echo chamber Fake news website Post-truth politics United States Filter bubble Knowledge divide Knowledge gap hypothesis Political polarization in the United States Social media use in politics United States 2016 U.S. presidential election 2020 U.S. presidential election Related topics Computer rage Criticism of Facebook 2021 Facebook company files leak Facebook–Cambridge Analytica data scandal Criticism of Netflix Cultural impact of TikTok Digital media use and mental health Effects of violence in mass media Fascination with death Griefer Mass shooting contagion Psychological effects of Internet use Sealioning Social aspects of television Social bot Social impact of YouTube Technophilia Neophile Technophobia Violence and video games

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Adapted from the Wikipedia article [Algorithmic radicalization](https://en.wikipedia.org/wiki/Algorithmic_radicalization) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Algorithmic_radicalization?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.