The Algorithmic Megaphone How Social Media Fuels Populism

Bonisiwe Shabane
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the algorithmic megaphone how social media fuels populism

In the age of infinite scroll, algorithms don’t just recommend content, they reshape politics.Across the globe, right-wing populist movements have found their loudest amplifier not in parliaments or rallies, but in the invisible coding... From Brexit to Trumpism to rising populist parties in Europe, simplistic, emotionally charged narratives are turbocharged by algorithmic design. But what happens when the very systems built to keep us engaged end up undermining moderation, nuance, and democracy itself? Social media platforms optimize for engagement, not truth. And research shows that anger, fear, and outrage spread faster than reasoned debate. “Algorithms are not neutral.

They reward content that sparks reaction, often outrage, creating a megaphone for the loudest voices.”~ Dr. Zeynep Tufekci, Technology & Society Scholar This design flaw gives populist movements an edge. Messages that are short, emotional, and polarizing outperform balanced or complex perspectives. New research shows the impact that social media algorithms can have on partisan political feelings, using a new tool that hijacks the way platforms rank content. How much does someone’s social media algorithm really affect how they feel about a political party, whether it’s one they identify with or one they feel negatively about?

Until now, the answer has escaped researchers because they’ve had to rely on the cooperation of social media platforms. New, intercollegiate research published Nov. 27 in Science, co-led by Northeastern University researcher Chenyan Jia, sidesteps this issue by installing an extension on consenting participants’ browsers that automatically reranks the posts those users see, in real time and still... Jia and her team discovered that after one week, users’ feelings toward the opposing party shifted by about two points — an effect normally seen over three years — revealing algorithms’ strong influence on... Over the past decade, social media has become deeply entwined with American political discourse. Platforms like Facebook, Twitter (now X), YouTube, and TikTok use opaque algorithms to determine what content users see, with profound effects on civic engagement and opinion formation.

In the early days of social networking, many were optimistic – “by connecting people and giving them a voice, social media had become a global force for plurality, democracy and progress,” as The Economist... | Impact). However, as social media’s influence grew, concerns mounted that these platforms were “hijacking democracy” by amplifying extreme voices, disinformation, and populist rhetoric ( Social Media Effects: Hijacking Democracy and Civility in Civic Engagement –... Today, scholars and experts are examining how algorithm-driven feeds may contribute to political polarization and the rise of populism in the United States. This report analyzes: Engagement-Driven Algorithms: Most social media platforms use recommendation algorithms designed to maximize user engagement (likes, shares, view time).

These algorithms learn from each user’s behavior and prioritize content likely to keep them hooked. While this personalization can make feeds more relevant, it also tends to favor provocative or emotionally charged material that generates stronger reactions. Researchers note that “social media technology employs popularity-based algorithms that tailor content to maximize user engagement,” and that maximizing engagement “increases polarization, especially within networks of like-minded users” (How tech platforms fuel U.S. political polarization and what government can do about it). In other words, the more a post incites outrage or passion, the more the algorithms will spread it, potentially skewing the political discourse toward extremes. Facebook: On Facebook, the News Feed algorithm ranks posts based on metrics like comments, shares, and reactions.

Internal studies at Facebook found that this system “exploit[s] the human brain’s attraction to divisiveness” (Facebook Knew Its Algorithms Divided Users, Execs Killed Fixes: Report – Business Insider). In fact, a leaked 2016 Facebook report concluded that “64% of all extremist group joins are due to our recommendation tools”, notably the “Groups You Should Join” and “Discover” algorithms that suggested communities to... This means the platform’s own automated suggestions were steering a majority of users who joined extremist or hyper-partisan groups, dramatically widening those groups’ reach. Facebook’s algorithm changes have also been linked to heightened partisan content. For example, in 2018 Facebook adjusted its feed to emphasize “meaningful social interactions,” but this inadvertently boosted posts that sparked argument and anger—leading to more divisive political content appearing in people’s feeds (Facebook CEO... Although top Facebook executives have publicly downplayed the platform’s role (“some people say… social networks are polarizing us, but that’s not at all clear from the evidence,” CEO Mark Zuckerberg argued (How tech platforms...

political polarization and what government can do about it)), the company’s own documents and actions suggest otherwise. Facebook has occasionally tweaked its algorithms to suppress incendiary posts – such as during the tense period right after the 2020 U.S. election – acknowledging that its automated ranking can fuel extremism (How tech platforms fuel U.S. political polarization and what government can do about it). However, these interventions tend to be temporary, since permanently tamping down divisive content would reduce user engagement (How tech platforms fuel U.S. political polarization and what government can do about it), and thus advertising revenue.

Twitter (X): Twitter initially showed users an unfiltered chronological timeline, but it introduced an algorithmic “Home” timeline and trending topic algorithms that highlight popular tweets. These systems can accelerate the viral spread of polarizing hashtags or sensational political takes. Twitter’s own internal research in 2021 revealed a concerning bias: the algorithm was found to amplify tweets from right-wing politicians and news sources more than those from left-wing sources (Twitter admits bias in algorithm... In other words, Twitter admitted its recommendation system disproportionately boosted certain political content on the right. This kind of amplification can skew the platform’s discourse, making extreme or populist right-wing narratives more visible. (Notably, Twitter’s trending topics have often been dominated by partisan campaigns or outrage-fueled discussions, illustrating how the algorithm magnifies whatever draws engagement, for better or worse.)

YouTube: YouTube’s recommendation algorithm is engineered to maximize watch time by suggesting videos a viewer is likely to click next. In practice, critics have long accused YouTube of leading users down a “rabbit hole” of increasingly extreme content to keep them watching. For instance, a user who starts with an innocuous political video might be recommended slightly more provocative videos, and over time these can escalate to fringe conspiracy theories or hyper-partisan channels. A Northwestern University analysis noted that these algorithms can “amplify inherent human biases” and interfere with normal social learning by over-rewarding sensational content (Social-Media Algorithms Have Hijacked “Social Learning”). While recent studies offer a mixed picture (some find that already-polarized viewers drive the consumption of extremist videos more than casual viewers being radicalized by the algorithm (Study Finds Extremist YouTube Content Mainly Viewed... In 2019, the platform adjusted its algorithm to reduce recommendations of content that “comes close to” violating policies (e.g.

conspiracy theories or disinformation) – a response to evidence that its automated suggestions were promoting such content. Nevertheless, anecdotes of users being “radicalized” via YouTube abound, and the site has hosted influential populist firebrands who built large followings through algorithmic promotion. A study shows that the order in which platforms like X display content to their users affects their animosity towards other ideological groups A team of U.S. researchers has shown that the order in which political messages are displayed on social media platforms does affect polarization — one of the most debated issues since the rise of social media and the... The phenomenon is equally strong regardless of the user’s political orientation, the academics note in an article published on Thursday in Science.

Social media is an important source of political information. For hundreds of millions of people worldwide, it is even the main channel for political engagement: they receive political content, share it, and express their opinions through these platforms. Given the relevance of social media in this sphere, understanding how the algorithms that operate on these platforms work is crucial — but opacity is the norm in the industry. That makes it extremely difficult to estimate the extent to which the selection of highlighted content shapes users’ political views. How did the researchers overcome algorithmic opacity to alter the order of posts that social media users see? Tiziano Piccardi from Stanford University and his colleagues developed a browser extension that intercepts and reorders the feed (the chronological timeline of posts) of certain social networks in real time.

The tool uses a large language model (LLM) to assign a score to each piece of content, measuring the extent to which it contained “antidemocratic attitudes and partisan animosity” (AAPA). Once scored, the posts were reordered one way or another — without any collaboration from the platform or reliance on its algorithm. The experiment involved 1,256 participants, who had all been duly informed. The study focused on X, as it is the social network most used in the U.S. for expressing political opinions, and it was conducted during the weeks leading up to the 2024 presidential election to ensure a high circulation of political messages. (From left) Sens.

John Curtis, R-Utah, and Mark Kelly, D-Ariz., discuss their joint effort to hold big tech accountable for harms caused by social media algorithms with NPR's Steve Inskeep on Nov. 18. Zayrha Rodriguez/NPR hide caption Social media companies and their respective algorithms have repeatedly been accused of fueling political polarization by promoting divisive content on their platforms. Now, two U.S. Senators have introduced legislation aimed at holding tech companies accountable for those business practices.

Sens. John Curtis, R-Utah, and Mark Kelly, D-Ariz., joined Morning Edition host Steve Inskeep to talk about the impact of social media algorithms on U.S. politics and beyond and their plan to address it. Listen to the interview by clicking play on the blue box above. Algorithms have the potential to amplify and spread extremism, thus, a multipronged approach combined with technology, innovations, and regulatory checks is pertinent. Algorithms are quickly becoming a keystone of content distribution and user engagement on social media.

While these systems are designed to enhance the user’s experience and engagement, they often unintentionally amplify extremist propaganda and polarising narratives. This amplification can exacerbate societal divisions, promote disinformation, and bolster the influence of extremist groups. This is called “algorithmic radicalisation”, which shows how social media platforms coax users into ideological rabbit holes and form their opinions through a discriminating content curation model. This article explores the mechanisms behind algorithmic amplification, its impact on the amplification of extremist narratives, and the challenges in countering extremism. Social media algorithms are computerised rules that examine user behaviour and rank content based on interactive metrics such as likes, comments, shares, and timelines. They also use machine learning models to make customised recommendations.

This process also works as an amplifier because posts with higher engagement or shares quickly tend to gain popularity, and viral trends sometimes emerge in this process. Algorithms may also create echo chambers if they only show similar viewpoints repeatedly to keep users engaged. Algorithms and hashtags amplify content by targeting specific audiences and promoting posts aligned with the user’s interests and behaviours. ���u4�|��"� ���|!����;d?i[)d�D-u�|,� /���.��T� d �V��Cw��}����D�㳀{��'���^�mx>'I$� "#hg:�n�f�[��8���^���f����q�9��*%rJ6���!x^&F�D���8�����!z����_]��� n���9��XIO2���Ԓyd)���#�ɻ�ZD��dz���s/�}����w K��T�D*"#�E~���%0�a!��؀#�G�#|N�i"=1�#Nn�g>���?�˨�,a��Q�,<'�"�W�w���o��ui�,�e�5��H��9�pq�� W���I8�k���Qb@�%�.��53�L"����"��<������9�}6R���n4��������x[M[�q�+������q]��\%W��ss�5\�{���;�]�~�G�u��O�}���Ϗ�g������Q�[—*��f�UX��j�����+�+�;��4Uh�`;��G����+��=4�O�+�;hϣa7�����d�����Uoڛ��y��5��^������ �I�{�6U<�,F�h�������� d>=�2@3Q~7J^�2y?���N5�(|�눝�ҧ��h/�}� �p� �tr;l���K�h�7�g�/��,�3'�1���<�}?7�~������d?�l2���pUtnQuQ�țt"�H�H P��VҁpB<,"��z�9�̄#�>����/p���PR�+�vX��0W���'�## �g����x� Ы�B��W�n�E�@�q��\�v1=�z|֢��т&����;Т*�a� �z���P)?�� p����?X*��7×�6�ő�`^%?µ}�Џ��]i#���k��_�vq�w����>�h�?�aP(������ =�:��38ʳ��n?dGn�Mr?n��$ ����D��{�I��j?�q���� j�P����LD=�D-P[3��,��#�H<�@ӱ�=�+��p��J]�~T��2�^���E�8[>��(E?n�d�s��V%�J�����dvs4;��DG�͜���o�K7�Ug�z.)1�#�GZ'����ntcs�޹��?�λ���R��U�� �f�R�>���s�H�_�X�z�U����m�b�XP�$"O,��|FK��ov�t� %ٝ]~7dK�ݤ������ v��&rq交t›�!�c77dzL��& ����!^��{4ig�ʸ��Y �x�7�GRU�����9>�d�e�7�iܾs��&�_ٷ�K]����� b�R,��2K�#���|���o�F^��������Z�����g�Ǭ�ur������5/2�V�<�pM@�ь7 �Z#�a�� 6�4,ꍦ��9���-��P��v�) Q��3I%�QKN^��l��H���8r�¹����#C�����쉶K���y1�HW�_0t��D�Չ�Z��F�`���La�|�(�����b6+̙�Qa��u::�lr���yk��L����8/Xr:���N�'�Dڶ''����.�;r���r�|�wךƑ�_j;q6�ψ{���R HV$3^���t�~��M��U��4�z�u����q�l��&OVO֏��b��X�<��V���j^�Z�^#��8A����?6']�n�6���dj hE-ծr[�����@a�����ӈ3X9���&���8��I� ƉV�l�8��r:��D�n,�ϛ�V �|tӬ�����>zl��7�g�ϻ��%<�����"�H$r`�ڝ�ȃ���h�ىK�G'q/���`k@�FK�d~]I�i��y��@9�@... ���G���~Ч@�T�aH�� B#���� f��`A���������6��#t"�\�@�'BŸ� n,���e?�8�?� R?B��0d!�@a6�F�9A� �c��<�"̇�] ����8��A!�#��a7(@Xq�ݡ+���b膰��B��Y��a/聰 J����?���>�a_+�� � a?荰�S�"7�A��""� � VA?���2u�@�P��B��"��@��a�08� ��p A8�"���������a�V�#N��b�p�J��a4��[���)0a �Gx)�SaB�oP N��NG�̀j�3a2�Yp ��0%�%��p.� ������P��.�i�t�� f \�������,��`�:����g��!\ �^��SX ��E���^��X�p-,Ax,M��u�C����հ"� \+^���<�1\�n��7���+S�fX��X�5[~7�Uo�u��z������j���5צN��9�6 � 6"�6��;��;�z�w��ԇp7܀�؂�^n��w�6��#���>� |~��a�}�}xnM���m��>��p�'�N�O��wr�4܃p܋��C�� h�����M�@�]� �� �8|B� #�� <���ƒ�x�x�@���[�)��������Ro�ah@�"�N�/q�24"��I� �@�#�᫰�k� £Ќ�ϰ�1�����_��Ro�_�7�߂C߆��_�� �#|^@x#|����E��K?��S��$�����1��... ���fK'��RM��"�(�:�(΀���9n$�1a�5�Ŵ�k�29�.�����$`4,A_P���' ��)������eM�U��2��o��M1�pM1K,V2�`"��@�@�8�8w���}7�[������Ϥ7{��t�0_ZaZb�7?b�kا�5�\��L�P��=պغ�*X ������U���;��X�O�;����X�\?e���r��!赣{�����i��4�0��9&��\�n��[� k$s�*����H����,����/���n]v6޺�=.���5��m)ҭ���a�R�;�-�~ ����y�D��e_^����.�ɛ�x���7� �g��,�&��|Lv�n4���Շ�4�U��� +�s̟�>w�D�ZT�,FՊi�5@���k���q��ZB���5M�i�lLr4>i1Q�t��4.����5 ø�d��(\n�9$�O�)Yd$% r����r0͊�l|Z���#{���;IZ���m���BmE��4�YK������L�2N e�t���ux�[+�yM�mE{��O�X��cW���������v"lӟZ�˲�v�/�x�0��0ԢA�3'�l\����Nj���,a���0˨w�wCq�N�e��_籽+��8�{��x{��F���ئx�����V�V9��s\�jv�G�j]K\�+`ݦ�P�����"����vmւҀxWQ:n����h��ύ#s�¬��� �f�ސWPڀn�/�W{rc�,O�g�l��\%j���)(m�T���Ri pq/�Q��N���h;�.L<~ni<�Ŵ�� � fV�-��A1[{(�͠��E,��9���__� �(�z���o��������ҸaƖ�t�������[W�[�Э�kw��{���# x��s�Nb�"gѮmU[[k�q[�w����CW'��3�:=ZH*:J��~'���I�dY��Yҳ��-ޡZcZ�����o���Y9|��w��Aǎ wmY��t�Ŷ{���ӷ�2%b@r��J[AX��5%G�1�1�X����Y�Ƙ�0Zn���8�1Q�d�k�I��i�-���7���-w��2��c�� ��r�^z��q�[ ��\��Nw)ߧ���\��P%0�����[��k&�!H�IvS�!Qҫ4e -��s�+����: ̱�x���vk����$18I l����,@ʯ���ё.��Zګ��u�E��v�\)�袎�tTO�1�a?� ��N"��d���n.�b{`���C7i{(K�MΑ�!�;Eo�>��/�w�y5�����rbKK�Eg�s-:����(������:�KoҢ'�Ωml��e��ÚV,}c����R��=mS���w�ڶgQ&7��Ŏ4P$�&.5�b_0ʰͰ��`h1�4�1�`�� �3U� )�2��%�T0脫�$����\ ����AlO���HA ���JӶ2� v�M�x�V�k6�]���>�ȄHa8G���e|�OEf�tf�f�Ҹ��F�ljjj�v���N1�� Ɨ� (����>�0�s���7�{���g:��#�g��d�"�Z���]:)�������D��R��ܡP!�0�I�q��)�iA��v�~垬N�ñ�����roS�R큋 .`�x>.2�+����~Y��q�5Ml�'#����(y���ӱX}{P�⯉Fsi�xZ��aX�;#ѿ�Ϲg.�iRp}ޜ�f�jOdHg=��ﵝ��v �E�ȪY�>C_�M���#�fH�o�,o|��s�4?���킟1��~4�O��wp���rgEp�#�W����}O�N���M�f_�G��:r��Iv�kˣ�.���և�n���V����w�u�g�/.�6a���>�6�������:�ť+�aV�����'��z��Ს':�Ya��,������p#��F�����ȅ���[0 s�m���LԨd��s ��a$�g�aB�c��s�C�xvx<-�#�����&Mˬu�[��F�k����Ė����H7_��8P...

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