I'm Paul Bouchaud
Feel free to email me
Find me on BlueSky
My pronouns are They/Them
My research focus on auditing online platforms' algorithms, investigating their impact on social dynamic and exploring alternative attention-allocators, such as bridging systems.
I am pursuing my doctoral studies at EHESS/CAMS, in residency at the Complex Systems Institute of Paris (CNRS/ISCPIF).
My research is supported by the Jean-Pierre Aguilar fellowship of the CFM Foundation for Research.
Recently, I got involved with AI Forensics, a European non-profit that investigates influential and opaque algorithms such as those of YouTube, Google, TikTok or Amazon.
In 2022, I have launched the Horus project, a crowdsourced audit of Twitter, YouTube, Google & Facebook.
In a first study, I have been able to show that Twitter's recommender amplifies toxic tweets (insults, threats, etc.) and distorts the political landscape perceived by the users.
More recently, leveraging the behavioral data collected from this initiative, I trained engagement predictive models, enabling me to explore audit methodologies and delve into the consequences of algorithms aimed at maximizing user engagement.
As part of my residency at the Complex Systems Institute of Paris, I am fortunate to work with extensive historical Twitter databases.
These databases, curated by Maziyar Panahi, have amassed, since 2016, more than 700 million political tweets and 500 million tweets related to climate change.
Prior to the release of the IPCC AR6 in March 2023, we conducted an in-depth analysis of the online discussion about climate, shedding light on the dynamics of the climate denialist community.
Before that, leveraging the extensive Politoscope database, I have been able to fully calibrate an agent-based model of Twitter accounts
to examine how recommender systems can toxify social networking sites.
Browsing Amazon’s Book Bubbles
We investigate Amazon’s book recommendation system, uncovering cohesive communities of semantically similar books. We identify a large community of recommended books endorsing climate
denialism, COVID-19 conspiracy theories and conservative views. This study underscores how even non-personalized recommender systems can have foreseeable negative effects on public health and civic discourse.
Algorithmic Amplification of Politics and Engagement Maximization on Social Media
We examine how engagement-maximizing recommender systems influence the visibility of Members of Parliament's tweets in timelines.
We showcase the need for audits accounting for user characteristics when assessing the distortions introduced by personalization algorithms and advocate addressing online platform regulations by evaluating the metrics platforms aim to optimize.
(Accepted at Complex Network 2023)
Skewed Perspectives: Examining the Influence of Engagement Maximization on Content Diversity in Social Media Feeds
By training engagement predictive models, we explore the impact of curation algorithms seeking to maximize engagement on the information landscape.
Crowdsourced Audit of Twitter’s Recommender Systems
Combining crowd-sourced data donation and large-scale server-side data collection, we provide quantitative experimental evidence of Twitter recommender distortion of users' subscriptions choices.
The new fronts of denialism and climate skepticism
Analyzing two years of Twitter exchanges, we observe that the denialist community presents inauthentic forms of expertise, relays more toxic tweets and embeds +71% inauthentic accounts with respect to the Pro-Climate community.
Pro-climate accounts fleeing from Elon Musk's Twitter, climate skeptic accounts represent 50% of the online discussion by March 2023.
France Inter, L'Obs, etc.)
Can Few Lines of Code Change Society?
Beyond fact-checking and moderation: How recommender systems toxify social networking sites
After having calibrated an agent-based model over a large scale longitudinal database of tweets from political activists, we compare the consequences of various recommendation algorithms on the social fabric and to quantify their interaction with some cognitive biases.
(Accepted in JASSS)