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Algorithmic radicalization

From Wikipedia, the free encyclopedia

Algorithmic radicalization is the concept that recommender algorithms on popular social media sites such as YouTube and Facebook drive users toward progressively more extreme content over time, leading to them developing radicalized extremist political views. Algorithms record user interactions, from likes/dislikes to amount of time spent on posts, to generate endless media aimed to keep users engaged. Through echo chamber channels, the consumer is driven to be more polarized through preferences in media and self-confirmation.[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 controversial; studies have found contradictory results as to whether algorithms have promoted extremist content.

Social media echo chambers and filter bubbles

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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.[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.[8] Echo chambers spread information without any opposing beliefs and can possibly lead to confirmation bias. According to group polarization theory, an echo chamber can potentially lead users and groups towards more extreme radicalized positions.[9] 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."[10]

By site

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Facebook

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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.[11] 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.[12] It is also relatively common for Facebook to assign political labels to their users. In recent years,[when?] 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 has revealed that their AI system prioritizes user engagement over everything else. The Facebook Files has also demonstrated that controlling the AI systems has proven difficult to handle.[13]

In an August 2019 internal memo leaked in 2021, Facebook has admitted that "the mechanics of our platforms are not neutral",[14][15] 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.[16] As referenced in the memo, "The more incendiary the material, the more it keeps users engaged, the more it is boosted by the algorithm."[14] 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."[17]

YouTube

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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.[18] According to a 2022 study by the Mozilla Foundation, users have little power to keep unsolicited videos out of their suggested recommended content. This includes videos about hate speech, livestreams, etc.[19][18]

YouTube has been identified as an influential platform for spreading radicalized content. Al-Qaeda and similar extremist groups 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".[20] 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, 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 videos to users. Section 230 is known to generally protect online platforms from civil liability for the content posted by its users.[21]

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.[22][23][24]

TikTok

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TikTok is an app 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.[25] 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.[26]

Various extremist groups, including 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.[27]

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.[28]

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 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.[27]

Another case involved the arrest of several teenagers in Vienna, Austria, in 2024, who were planning to carry out a terrorist attack at a 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.[27]

Self-radicalization

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See also: 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".[29] Through social media outlets on the internet, 'Lone-wolf' terrorism has been on the rise, being linked to algorithmic radicalization.[30] Through echo-chambers on the internet, viewpoints typically seen as radical were accepted and quickly adopted by other extremists.[31] These viewpoints are encouraged by forums, group chats, and social media to reinforce their beliefs.[32]

References in media

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The Social Dilemma

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Main article: The Social Dilemma

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' to prove psychological manipulation on social media, therefore leading to political manipulation. In the film, Ben falls deeper into a 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

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Weakening Section 230 protections

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Main article: Section 230

In the 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".[33] Section 230 protects the media from liabilities or being sued of third-party content, such as illegal activity from a user.[33] 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.[34] 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. that moderation in any capacity introduces a liability to content providers as "publishers" of the content they chose to leave up.[35]

Lawmakers have drafted legislation that would weaken or remove Section 230 protections over algorithmic content. House Democrats Anna Eshoo, Frank Pallone Jr., Mike Doyle, and Jan Schakowsky introduced the "Justice Against Malicious Algorithms Act" in October 2021 as H.R. 5596. The bill died in committee,[36] but it would have removed Section 230 protections for service providers related to personalized recommendation algorithms that present content to users if those algorithms knowingly or recklessly deliver content that contributes to physical or severe emotional injury.[37]

See also

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References

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  1. ^ "What is a Social Media Echo Chamber? | Stan Richards School of Advertising". advertising.utexas.edu. November 18, 2020. Retrieved November 2, 2022.
  2. ^ "The Websites Sustaining Britain's Far-Right Influencers". bellingcat. February 24, 2021. Retrieved March 10, 2021.
  3. ^ Camargo, Chico Q. (January 21, 2020). "YouTube's algorithms might radicalise people – but the real problem is we've no idea how they work". The Conversation. Retrieved March 10, 2021.
  4. ^ E&T editorial staff (May 27, 2020). "Facebook did not act on own evidence of algorithm-driven extremism". eandt.theiet.org. Retrieved March 10, 2021.
  5. ^ "How Can Social Media Firms Tackle Hate Speech?". Knowledge at Wharton. Retrieved November 22, 2022.
  6. ^ "Internet Association - We Are The Voice Of The Internet Economy. | Internet Association". December 17, 2021. Archived from the original on December 17, 2021. Retrieved November 22, 2022.
  7. ^ Kaluža, Jernej (July 3, 2022). "Habitual Generation of Filter Bubbles: Why is Algorithmic Personalisation Problematic for the Democratic Public Sphere?". Javnost - the Public, Journal of the European Institute for Communication and Culture. 29 (3): 267–283. doi:10.1080/13183222.2021.2003052. ISSN 1318-3222.
  8. ^ "What is a Social Media Echo Chamber? | Stan Richards School of Advertising". advertising.utexas.edu. November 18, 2020. Retrieved April 12, 2023.
  9. ^ Cinelli, Matteo; De Francisci Morales, Gianmarco; Galeazzi, Alessandro; Quattrociocchi, Walter; Starnini, Michele (March 2, 2021). "The echo chamber effect on social media". Proceedings of the National Academy of Sciences of the United States of America. 118 (9): –2023301118. Bibcode:2021PNAS..11823301C. doi:10.1073/pnas.2023301118. ISSN 0027-8424. PMC 7936330. PMID 33622786.
  10. ^ Cinelli, Matteo; De Francisci Morales, Gianmarco; Starnini, Michele; Galeazzi, Alessandro; Quattrociocchi, Walter (January 14, 2021). "The echo chamber effect on social media". Proceedings of the National Academy of Sciences of the United States of America. 118 (9): e2023301118. Bibcode:2021PNAS..11823301C. doi:10.1073/pnas.2023301118. ISSN 0027-8424. PMC 7936330. PMID 33622786.
  11. ^ Oremus, Will; Alcantara, Chris; Merrill, Jeremy; Galocha, Artur (October 26, 2021). "How Facebook shapes your feed". The Washington Post. Retrieved April 12, 2023.
  12. ^ Atske, Sara (January 16, 2019). "Facebook Algorithms and Personal Data". Pew Research Center: Internet, Science & Tech. Retrieved April 12, 2023.
  13. ^ Korinek, Anton (December 8, 2021). "Why we need a new agency to regulate advanced artificial intelligence: Lessons on AI control from the Facebook Files". Brookings. Retrieved April 12, 2023.
  14. ^ a b "Disinformation, Radicalization, and Algorithmic Amplification: What Steps Can Congress Take?". Just Security. February 7, 2022. Retrieved November 2, 2022.
  15. ^ Isaac, Mike (October 25, 2021). "Facebook Wrestles With the Features It Used to Define Social Networking". The New York Times. ISSN 0362-4331. Retrieved November 2, 2022.
  16. ^ Little, Olivia (March 26, 2021). "TikTok is prompting users to follow far-right extremist accounts". Media Matters for America. Retrieved November 2, 2022.
  17. ^ "Study: False news spreads faster than the truth". MIT Sloan. Retrieved November 2, 2022.
  18. ^ a b "Hated that video? YouTube's algorithm might push you another just like it". MIT Technology Review. Retrieved April 11, 2023.
  19. ^ "YouTube User Control Study - Mozilla Foundation". Mozilla Foundation. September 2022. Retrieved November 12, 2024.
  20. ^ Murthy, Dhiraj (May 1, 2021). "Evaluating Platform Accountability: Terrorist Content on YouTube". American Behavioral Scientist. 65 (6): 800–824. doi:10.1177/0002764221989774. S2CID 233449061 – via JSTOR.
  21. ^ Root, Damon (April 2023). "Scotus Considers Section 230's Scope". Reason. 54 (11): 8. ISSN 0048-6906.
  22. ^ Ledwich, Mark; Zaitsev, Anna (March 2, 2020). "Algorithmic extremism: Examining YouTube's rabbit hole of radicalization". First Monday. 25 (3). arXiv:1912.11211. doi:10.5210/fm.v25i3.10419. Retrieved November 8, 2024.
  23. ^ Hosseinmardi, Homa; Ghasemian, Amir; Clauset, Aaron; Mobius, Markus; Rothschild, David M.; Watts, Duncan J. (August 10, 2021). "Examining the consumption of radical content on YouTube". Proceedings of the National Academy of Sciences. 118 (32): e2101967118. doi:10.1073/pnas.2101967118. PMC 8364190. PMID 34341121.
  24. ^ Chen, Annie Y.; Nyhan, Brendan; Reifler, Jason; Robertson, Ronald E.; Wilson, Christo (September 2023). "Subscriptions and external links help drive resentful users to alternative and extremist YouTube channels". Science Advances. 9 (35): eadd8080. doi:10.1126/sciadv.add8080. PMC 10468121. PMID 37647396.
  25. ^ "TikTok's algorithm leads users from transphobic videos to far-right rabbit holes". Media Matters for America. October 5, 2021. Retrieved November 22, 2022.
  26. ^ Little, Olivia (April 2, 2021). "Seemingly harmless conspiracy theory accounts on TikTok are pushing far-right propaganda and TikTok is prompting users to follow them". Media Matters for America. Retrieved November 22, 2022.
  27. ^ a b c "TikTok Jihad: Terrorists Leverage Modern Tools to Recruit and Radicalize". The Soufan Center. August 9, 2024. Retrieved August 10, 2024.
  28. ^ "Our continued fight against hate and harassment". Newsroom | TikTok. August 16, 2019. Retrieved November 22, 2022.
  29. ^ "Lone Wolf Terrorism in America | Office of Justice Programs". www.ojp.gov. Retrieved November 2, 2022.
  30. ^ Alfano, Mark; Carter, J. Adam; Cheong, Marc (2018). "Technological Seduction and Self-Radicalization". Journal of the American Philosophical Association. 4 (3): 298–322. doi:10.1017/apa.2018.27. ISSN 2053-4477. S2CID 150119516.
  31. ^ Dubois, Elizabeth; Blank, Grant (May 4, 2018). "The echo chamber is overstated: the moderating effect of political interest and diverse media". Information, Communication & Society. 21 (5): 729–745. doi:10.1080/1369118X.2018.1428656. ISSN 1369-118X. S2CID 149369522.
  32. ^ Sunstein, Cass R. (May 13, 2009). Going to Extremes: How Like Minds Unite and Divide. Oxford University Press. ISBN 978-0-19-979314-3.
  33. ^ a b "47 U.S. Code § 230 - Protection for private blocking and screening of offensive material". LII / Legal Information Institute. Retrieved November 2, 2022.
  34. ^ Smith, Michael D.; Alstyne, Marshall Van (August 12, 2021). "It's Time to Update Section 230". Harvard Business Review. ISSN 0017-8012. Retrieved November 2, 2022.
  35. ^ Masnick, Mike (June 23, 2020). "Hello! You've Been Referred Here Because You're Wrong About Section 230 Of The Communications Decency Act". Retrieved April 11, 2024.
  36. ^ "H.R. 5596 (117th): Justice Against Malicious Algorithms Act of 2021". GovTrack. Retrieved April 11, 2024.
  37. ^ Robertson, Adi (October 14, 2021). "Lawmakers want to strip legal protections from the Facebook News Feed". The Verge. Archived from the original on October 14, 2021. Retrieved October 14, 2021.
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