Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study

The emergence of social media platforms has brought about a significant transformation in global patterns of information dissemination and consumption, as a large number of internet users now rely on these channels as their primary sources of information acquisition [1‐4]. The rapid growth in social media membership, and consequently of digital traces circulating in these platforms, have been accompanied by a progressive rise in the importance of artificial intelligence (AI) based recommender systems, content pre-selection, ranking and suggestion systems used to customise users’ online experiences [5, 6].

The integration of AI-based recommender systems into social media platforms has led to a fundamental shift in the way users consume and interact with online information [7], significantly increasing the level of automated content curation while limiting users’ freedom of independent content discovery [8]. This paradigm shift towards the machine-learning based hyper-personalisation of social media content raises concerns regarding potential impacts on the quality and diversity of information available to users, with clear implications for the integrity of knowledge acquisition processes. Several recent studies have analysed these risks, concluding that engagement-based recommender systems – which form the majority of recommendation engines currently deployed within social media platforms [9, 10] – may be prone to bias [11, 12], user-manipulating behaviour [13], the creation of echo chambers [5, 14], and to the amplification of false or misleading content [15, 16].

Despite their status as critical infrastructure of social media platforms – and arguably of information circulation at a societal level – the internal architectures and practical functioning of recommender systems remain only superficially understood [17], and while several platforms have previously released white papers with information on their functioning [18‐20], limited evidence exists on the characteristics that guide their deployment. This is also the case for Twitter, (Now X Corp.) which recently made parts of its recommender system public, providing a window into the functioning of a social media content suggestion system [21]. However, while this release does provide new information on the system’s architecture, perhaps the most central part of the system – a ‘heavy ranker’- deep neural network used to make recommendation predictions [22] cannot be replicated with currently available information, limiting the possibility of testing the behaviour of this recommender system. This lack of evidence is a clear obstacle towards evaluating the magnitude of any form of algorithmic bias in content suggestion, and in particular, towards understanding whether, in their drive to maximise user engagement with a platform, recommender systems are acting as significant drivers of the diffusion of online disinformation and misinformation. This is crucial, as understanding how false and low-credibility content propagates within social media platforms is key to improving the safety of societal information commons.

To address the existing evidence gap, this study introduces a measurement approach that leverages existing digital traces to empirically observe – analysing recommendation outcomes through impression counts – the state of the promotion of low-credibility content on Twitter in a two week period in January 2023. The motivation for choosing Twitter as the object of this study is threefold: First, Twitter has a large user base with global reach, with a monthly user base of approximately 450 million users and over 300 million daily tweets [23], making it one the largest and most influential social media platforms globally. Second, Twitter has been often criticised for platforming and amplifying extremist content, disinformation and misinformation [24, 25], and studying its recommender systems may provide additional insights into how false information spreads on the platform. Third, Twitter was, at the time of data collection, the only platform which provided data on impressions (or views) through its API. While Twitter’s recommender system has undergone significant changes since January 2023, this study aims to offer a snapshot of the recommender system’s behaviour at a crucial juncture in the platform’s evolution, before the recent system-wide overhaul. Although the findings may not directly translate to the current recommender system, they may provide unique insights into a pivotal transitional moment, revealing baseline tendencies in how the system influenced the propagation of low-credibility content.

To map the behaviour of Twitter’s recommender system, this study uses an original dataset of ≈ 2.7 million tweets discussing COVID-19 and climate change published on the platform in a 14-day period in January 2023, and extracts tweets containing information from low-credibility URL domains and high-credibility URL domains [26], testing whether low-credibility information gets amplified visibility on the platform. Data on impressions – defined in the API documentation as the count of how many times a Tweet has been seen [27] – was initially made available through the Twitter API in January 2023 and, as a passive metric of how many users have been exposed to a tweet, provides a powerful window into the content that the Twitter recommender system tailors to users. Through this process, it is possible to estimate whether tweets sharing information from low-credibility domains generate exceptional impressions, which may point towards a recommendation bias towards low-credibility content, as well as a general lack of functioning integrity signals [6]. By analysing the visibility of low-credibility content – which can be used as a baseline for false or misleading information – this research seeks to provide insights into the dynamics that drive user exposure to false or misleading information, with potential implications for social media platforms and the broader digital information ecosystem.

The recent addition of impressions data on the Twitter API offers a unique opportunity to investigate the role of Twitter’s recommender system in promoting the circulation of disinformation and misinformation. While this opportunity has now been limited by restrictions to the Twitter API, this study presents an initial attempt to use an inferential approach based on impressions data to assess any differences between low-credibility and high-credibility tweets on Twitter. The main analysis of this work revealed that tweets containing URLs from low-credibility domains achieve higher visibility on the platform, with an average difference after bootstrapping of +19.2% for COVID-19 data, and +95.8% for climate change data. However, results also show that within bootstrapped samples, the majority of this effect comes from low-credibility tweets’ overperformance for high-engagement and high-followers users, which account for the majority of Twitter’s impressions.

This work also set out to uncover notable features that may explain any impressions-based amplification, and found that toxicity scores obtained from Jigsaw’s Perspective API showed a clear pattern where high-toxicity tweets exhibited heightened amplification when containing low-credibility content, confirming the existing understanding that toxic content may be more easily amplified by engagement-based recommender systems. These results support findings from prior works, particularly those showing clear connections between negative emotional content and viral misinformation spread [48]. By confirming the role of engagement-based recommender systems in amplified toxic content, these findings highlight the need for alternative approaches to social media recommendation, such as bridging-based recommender systems, which focus on connecting users with diverse perspectives and high-quality information rather than maximising engagement alone [49]. While developing recommender systems that balance engagement, equality and diversity is indeed challenging, and further research is needed on this topic, this works lends support to the notion that alternatives to engagement-based recommendations are worth pursuing to improve online discourse and reduce the amplification of misinforming, toxic content.

Furthermore, one of the most notable results from the additional stratification analysis is the substantial amplification observed for politically biased low-credibility tweets, especially those with right-leaning partisanship. This finding highlights growing concerns about political polarisation and the rise of filter bubbles on social platforms. It suggests that in its pursuit of maximising engagement, the version of Twitter’s recommender system under analysis may have inadvertently amplified tweets that express partisan viewpoints, regardless of their truthfulness. While this finding alone does not show the formation of echo chambers – as cross-partisan exposure may still occur – this is concerning, given the vulnerability of high-profile socio-political discussions like climate change or COVID-19 to manipulation by misinformation campaigns. The fact that right-leaning tweets see substantially more amplification of false claims on climate change points to the ability of partisan disinformation to successfully exploit algorithmic loopholes on Twitter, highlighting concerns about social media deepening societal divides and potentially favoring hyper-partisan information. Lastly, results showed that low-credibility tweets from legacy verified accounts enjoy significant amplification on Twitter. While legacy verified accounts are only a minority of the platform’s population, it is important to note that Twitter recently changed its verification policy, and the blue tick can now be purchased as part of a monthly subscription plan. While data on paid-for verified users is not available via the Twitter API, this finding is concerning, as it suggests that it is possible that the blue tick, which warrants amplified visibility within the recommender system, may be weaponised by malicious actors to spread false and misleading information, particularly during highly emotionally loaded emergencies such as that of COVID-19. This point is crucial, and deserves further attention in future research.

Finally, this research offers initial empirical evidence on the promotion of low-credibility content on Twitter, revealing key factors that may contribute to a significant impressions overperformance of tweets containing false or misleading information. Given ongoing concerns about Twitter’s impacts on information quality and integrity under its new management, this historical assessment of recommendation patterns can also offer an important benchmark for future analysis. Moreover, the study’s methodological approach of leveraging impression counts to empirically analyse recommendation outcomes remains broadly relevant. Evaluating the visibility afforded to different types of content on social platforms is key to understanding how algorithmic curation shapes online information landscapes. By offering a framework to probe the role of recommender systems in propagating low-credibility information, this research retains value for inspiring further investigation – both of Twitter’s evolving dynamics and other influential networks. Capturing past system behaviours can provide a foundation to monitor emerging risks and work towards safer, more accountable social information ecosystems.

However, it is also important that the results presented in this work have limitations, which should form the basis for future research into recommender-based amplification of low-credibility content. Firstly, while impressions data may currently be the best available metric to test the behaviour of recommender systems, this metric is still imperfect, as it may also be influenced by exogenous factors that can be difficult to control, such as the level of public interest on a specific topic like climate change or COVID-19. Furthermore, despite current mitigating measures based on data stratification, it is important to acknowledge the cyclical nature of the relationship between engagement, followers count, and impressions, where each metric may, in practice, compound the other. This interplay adds additional complexity to the interpretation of the findings, where the conclusion that Twitter’s algorithm favours low-credibility content, should be considered with an understanding of the potentially reciprocal relationship between these key variables. Finally, this works analyses Twitter’s recommender system as a static system – both temporally and in terms of behaviour – using a frequentist statistical approach. In practice, any recommender system operates on top of expressed and implied human preferences, and future analyses may attempt the implementation of different approaches to account for this, such as Bayesian analysis.

Recommender systems are arguably the most prevalent application of AI and machine learning globally, affecting billions of internet users daily. However, despite their ubiquitous nature, there is an evident absence of regulation on their large-scale deployment. The results of this study highlight a need, as a minimum, for increased transparency in the field of social media recommender systems, as only through system-wide access it will be possible to produce clearer causal explanations for the effects observed in this work. However, as the recent release of parts of the Twitter algorithm eloquently shows, in principle transparency is not enough. Rather, it is crucial that transparency is accompanied the possibility of full replication of a recommender system,which would allow for the definition of protocols for comprehending, auditing, and stress-testing these systems to prevent the unintentional promotion of disinformation, to curb the perpetuation of harmful biases, and to safeguard the security of knowledge dissemination and acquisition processes.

Addressing this challenge is admittedly difficult, as recommender systems are proprietary, high-value assets at the heart of social media platforms. Nevertheless, it is vital to stress the significance of transparency and oversight for systems that mediate access to information and shape public discourse on a global scale. This study contributes to emphasising these imperatives, while also offering initial insights into how Twitter’s recommender system may have specifically amplified misleading and false information. Future research building on these findings should further investigate the dynamics of disinformation circulation on social media and explore potential interventions to curb the unintentional spread of misinformation by recommender systems. Ultimately, continued research in this domain is essential to ensure the responsible design and implementation of AI systems.