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Algorithmic Amplification And Radicalisation

Posted 24 Mar 2025

Updated 27 Mar 2025

4 min read

Why in the news?

Recently, experts have raised concerns regarding social media algorithms having the potential to amplify and spread extremism.

Understanding Social Media Algorithmic Amplification

  • Social media algorithms: These are computerized rules that examine user behaviour and rank content based on interactive metrics such as likes, comments, shares, timelines etc. 
    • It uses machine learning models to make customized recommendations.
    • It works as amplifiers because posts with higher engagement, shares, likes etc., alongwith hashtags, quickly tend to gain popularity and emerge as viral trends.
  • Algorithmic Radicalisation: It is the idea that algorithms on social media platforms drive users towards progressively more extremist propaganda and polarizing narratives
    • It then influences their ideological stances, exacerbating societal divisions, promoting disinformation, bolstering influence of extremist groups etc. 
    • It reflects social media algorithms, which are intended to boost user interaction, inadvertently construct echo chambers and filter bubbles, confirming users' pre-existing beliefs, leading to confirmation bias, group polarization etc.
    • It shows how social media platforms coax users into ideological rabbit holes and form their opinions through a discriminating content curation model.
Description: A diagram of a diagram of a social media network

Description automatically generated with medium confidence

Challenges in curbing Algorithmic Radicalization

  • Complex mechanisms involved: The opacity of algorithms used in social media present challenges in addressing extremist contents.
    • Social media algorithms work as 'black boxes', in which even some developers fully don't understand the underlying processes for recommending certain content.
    • E.g., complexity of TikTok's "For You" page's operational mechanics, limits the mitigation of its algorithmic bias.
  • Modulated content: Extremist groups change their radical contents to euphemisms or symbols to evade detection systems.
    • E.g., IS and al-Qaeda uses coded language and satire to avoid detection.
  • Moderation vs. free speech: Maintaining the right balance between effective content moderation and free speech is a complex issue.
    • Extremist groups exploit this delicate balance by ensuring that their contents remain within the permissible limits of free speech, while still spreading divisive ideologies.
  • Failure in accounting local context: Extremist contents are generated from the socio-political undercurrents in a specific country, and algorithms deployed globally often fail to account for these local socio-cultural contexts, exacerbating the problem.
  • Lack of international regulation and cooperation: Countries primarily view radical activities from their national interest rather than from the perspective of global humanity.

Steps taken to curb Algorithmic Radicalisation 

Global steps 

  • European Union's (EU's) Digital Services Act 2023 requires social media apps to disclose how their algorithms work and allows independent researchers to assess their impact on users.
  • Artificial Intelligence (AI)-driven moderation: E.g., YouTube's machine-learning model, 2023, reduced flagged extremist videos by 30%.
  • Christchurch Call: A community of over 130 governments, online service providers, and civil society organisations acting together to eliminate terrorist and violent extremist content online.

Indian steps 

  • Ministry of Electronics and Information Technology's several initiatives have flagged over 9,845 URLs hosting harmful content.
  • IT Rules 2021: It enables tracing the first originator of content on social media, digital news, OTT platforms etc., and removing flagged content within 36 hours.

 

Way forward

  • Algorithmic Audits: Regular algorithm audits should be mandatory to ensure transparency and fairness, similar to European Union's (EU's) Digital Services Act 2023.
  • Accountability measures: Policymakers should clearly define the rules for algorithmic accountability, including penalties for platforms that fail to address the amplification of harmful content.
    • E.g., Germany's Netz law imposes fines on social media platforms for not removing illegal content within 24 hours.
  • Custom-made content moderation: Customized moderation policies (or algorithmic frameworks), tailored to localized contexts, can enhance the effectiveness of interventions to curb radicalisation spread by social media platforms. 
    • E.g., regulators in France partnered with social media companies to enhance their algorithms' ability to detect and moderate extremist content, considering various dialects spoken within the country.
  • Public awareness: Government must conduct public awareness drives to help users identify propaganda and avoid engaging with extremist content.
    • E.g., UK's Online Safety Bill contains provisions for public education initiatives to improve online media literacy.
  • Tags :
  • radicalisation
  • Radicalisation
  • social media algorithms
  • online radicalisation
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