Hazem Ibrahim

Language models are becoming an ordinary way to ask a political question, and there is widespread concern that their answers lean left. Nearly all evidence for this concern is national: each audit scores a model against a single country's survey respondents, most often American ones, and the resulting score confounds where the model stands with where the sampled population stands. Here we remove this confound by scoring models on item parameters estimated from 1.14 million human survey respondents in 141 countries and then held fixed, placing models, respondents and countries on one metric anchored to people rather than to any national sample. We pose 1,153 harmonized survey items to 65 models from 27 developers, at temperature zero and with no persona, and score the answers on four latent dimensions: social liberalism, economic left-right, democratic commitment and anti-elite sentiment. The quantity this measures is expressed ideology, the positions a model states when asked, rather than any claim about what these systems hold. We find, first, that all 65 models sit to the liberal side of the world human median on social liberalism, with the median model at the 31st percentile, and that the lean runs weaker in the same direction on the other three dimensions. We find, second, that the models resemble one another far more than people do: across three orders of magnitude in parameter count, and including open-weight, proprietary, reasoning-tuned and deliberately uncensored variants, the standard deviation of model positions is 11 to 33 percent of the human standard deviation on every dimension, and differently post-trained versions of the same base model stay within that band. Two further results caution against reading any single number too closely: changing which survey program supplies the items moves a model's measured position by 0.28 to 0.62 human standard deviations, two to nearly four times what the same change does to a country and more than the entire model-human gap on every dimension, and with the number of answered items held equal on both sides, the difference between models and human respondents in how consistently they place themselves on one ideological side shrinks to a modest residual. The direction of the liberal lean is robust across instruments; its magnitude is a property of the model and the questionnaire together.

Societies worldwide are described as increasingly polarized, yet issue polarization-how divided a society is over political positions-has been measured almost entirely on a single left-right axis developed in Western democracies. Whether that axis describes political division elsewhere is largely untested, because no survey measures political attitudes on multiple dimensions across many countries. Here, we harmonize 1,299 items from nine cross-national survey programs and 23 national election studies to place 1.14 million people from 141 countries on four dimensions: social liberalism, economic left-right, democratic commitment, and anti-elite sentiment. A single left-right axis does not describe political division in most of the world. The median within-country correlation between a person's social and economic positions is-0.015. Societies differ instead in what they are divided about, and they do so by region. European countries divide most often over social liberalism, East Asian and Arab countries over democratic commitment, Latin American countries over economic left-right, and African countries over anti-elite sentiment. Of the 81 countries measured on all four dimensions, 39 are most divided on a dimension no left-right scale would capture. Most political disagreement lies within countries rather than between them, at 58 to 88 percent of the variance. Demographic characteristics are only weakly associated with political attitudes, in directions that vary across countries. Ideological constraint, long treated as a property of mass publics, holds in nineteen of the eighty-three we can test, most strongly in the United States at +0.70, and there it is concentrated among the politically attentive. Understanding political conflict therefore requires asking not how polarized a society is but what it is polarized about. Our released item bank and fitted parameters place respondents from any future survey on the same scale without re-estimation, once that survey's items are harmonized to the bank.

Social media platforms increasingly mediate political information exposure, yet the role of algorithmic curation in shaping political exposure remains contested ^1,2 . This question is difficult to resolve on platforms in which users retain substantial control over their feeds ^3,4 . The ‘For You’ feed of TikTok, which delivers content almost entirely through algorithmic recommendation, offers a setting in which user agency is sharply constrained. Here we show, through 323 audit experiments with controlled ‘sock puppet’ accounts seeded with Democratic or Republican content across three US states, that accounts seeded with partisan content exhibited systematic, asymmetric differences in partisan exposure. Across more than 280,000 recommendations collected over 27 weeks during the 2024 US presidential election campaign, Republican-seeded accounts received about 11.5% more co-partisan content than Democratic-seeded accounts, whereas Democratic-seeded accounts were exposed to about 7.5% more cross-partisan content—largely anti-Democratic material—even after adjusting for engagement metrics. These asymmetries are concentrated among high-reach Republican channels and in specific policy domains, including immigration, crime and foreign policy for Democrats, and abortion for Republicans. Our findings show partisan imbalances in political information exposure on a platform dominated by algorithmic recommendations, with implications for platform governance and democratic discourse.

Immigrant parents not only face assimilation challenges in a new country, but many also find it difficult to connect their children with their heritage culture. As language plays an important in conveying and practicing culture, the challenges associated with preserving heritage language in immigrant families have not received much attention in the literature. In this paper, we focus on the Bangladeshi community in Canada and interview 20 Bangladeshi immigrant parents to explore the various concerns they have regarding preserving their heritage language and discuss two different approaches to learning through the presentation of language acquisition applications. Based on our study, we report the cultural tensions, economic constraints, and infrastructural challenges the immigrant families face while teaching heritage languages to their children. We also provide a set of design implications to better facilitate heritage language maintenance and associate our findings with some broader concerns in the HCI literature around migration, memory, identity, and learning.

The technological and online experiences of billions worldwide are dominated by a handful of companies known as “Big Tech.” Despite this being a cause for concern in governmental, economic, and ethical spheres, the literature lacks a study exploring the impact of public scandals on, and the global sentiment toward, Big Tech. Here, we quantify the power of Big Tech by analyzing their acquisitions, market capitalization, and number of monthly active users. Moreover, we utilize the synthetic control method to estimate the effect of public scandals on the stock price of two Big Tech companies, and find that they had no lasting effect. We also analyze the number of tweets mentioning these scandals, and find that they quickly fade from the spotlight. To explore public sentiment, we survey 5300 participants across 25 countries, and find that those from countries with lower digital literacy and more authoritarian regimes are more trusting of Big Tech. Furthermore, we find that one in three feels they lack control over the data collected about them, and one in four feels that Big Tech knows what they are thinking, knows more about them than their best friend, and may even be secretly listening to their conversations. Additionally, one in four feels addicted to Big Tech products, have no choice but to use them, and wishes there were more companies to choose from. These findings highlight the adverse effect of the oligopolistic nature of Big Tech on consumer choice and help inform policy-makers aiming to curb their dominance.

With over two billion monthly active users, YouTube currently shapes the landscape of online political video consumption, with 25% of adults in the United States regularly consuming political content via the platform. Considering that nearly three-quarters of the videos watched on YouTube are delivered via its recommendation algorithm, the propensity of this algorithm to create echo chambers and deliver extremist content has been an active area of research. However, it is unclear whether the algorithm may exhibit political leanings toward either the Left or Right. To fill this gap, we constructed archetypal users across six personas in the US political context, ranging from Far Left to Far Right. Utilizing these users, we performed a controlled experiment in which they consumed over eight months worth of videos and were recommended over 120,000 unique videos. We find that while the algorithm pulls users away from political extremes, this pull is asymmetric, with users being pulled away from Far Right content stronger than from Far Left. Furthermore, we show that the recommendations made by the algorithm skew left even when the user does not have a watch history. Our results raise questions on whether the recommendation algorithms of social media platforms in general, and YouTube, in particular, should exhibit political biases, and the wide-reaching societal and political implications that such biases could entail.