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Manoel
4,017 posts
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Manoel
@manoelribeiro
Computational Social Science, Platforms, GenAI, Moderation
manoelhortaribeiro.github.io
Joined January 2009
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  • Pinned
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    Manoel
    @manoelribeiro
    May 19
    In a new blog post, I argue that the anti-ai movement ought to distinguish between claims about the technology and the "project of AI," as defined by Vetsi et al. in their new paper. 🔗: doomscrollingbabel.manoel.xyz/p/the-anti-ai-…
    3.8K
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    Manoel
    @manoelribeiro
    Jun 14, 2023
    One of our key sources of human data is no longer fully “human"! We estimate that 33-46% of crowd workers on MTurk used large language models (LLMs) in a text production task - which may increase as ChatGPT and the like become more popular and powerful. arxiv.org/abs/2306.07899
    858K
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    Manoel
    @manoelribeiro
    Aug 3, 2022
    Life is good: Grammarly now works on overleaf
  • user avatar
    Manoel
    @manoelribeiro
    May 22, 2024
    I'm thrilled to announce that I'll join @PrincetonCS/@PrincetonCITP as an assistant professor in Spring 2025 — can't wait to join this amazing group of researchers. I will continue to work on online platforms / the societal impact of AI. If you’d like to collaborate, reach out!
    66K
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    Manoel
    @manoelribeiro
    Feb 13, 2024
    Is the YouTube algorithm radicalizing us? Our new PNAS paper says: probably not! But why should you trust this paper rather than other papers or reports saying otherwise? Because we came up with a way to disentangle the causal effect of the algorithm. pnas.org/doi/10.1073/pn…
    122K
  • user avatar
    Manoel
    @manoelribeiro
    Oct 16, 2024
    I am recruiting 1-2 PhD students for Fall 2025 at Princeton to work on Comp Social Science/Societal Impact of GenAI/GenAI for SocSci I wrote a bit about my research flavor & interests here: manoelhortaribeiro.github.io/advising Deadline: December 15th (cs.princeton.edu/grad#prospecti…) Please boost!
    77K
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    Manoel
    @manoelribeiro
    Aug 23, 2019
    In our new pre-print, we find significant evidence of user radicalization on #YouTube. Tracking >79M comments across >300k videos, we show that those who comment on the so-called Intellectual Dark Web/Alt-lite consistently migrate to Alt-right channels. arxiv.org/abs/1908.08313
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    Manoel
    @manoelribeiro
    Mar 22, 2024
    🚨LLMs outdebate humans when given access to opponents' personal Information!🚨 In a pre-reg study (N=820), participants who debated ChatGPT had 81.7% (p<0.01) higher odds of agreeing with their opponents after the debate (compared to a human baseline) 📜arxiv.org/abs/2403.14380
    Image of a research paper.
    193K
  • user avatar
    Manoel
    @manoelribeiro
    Oct 13, 2023
    I’m hitting the job market looking mainly for tenure-track asst. professor positions (2024)! 🚨👨‍🔬 My core research informs how we should curate content on online platforms — and I’m also interested in how large language models will impact on the Web in the upcoming years.
    83K
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    Manoel
    @manoelribeiro
    Jun 6, 2024
    I’m thrilled that our paper, with @cervisiarius and led by @russogiusep: “Stranger Danger! Cross-community interactions with fringe users increase the growth of fringe communities on Reddit” won the best paper award at @icwsm! 🎉🎉🎉 ojs.aaai.org/index.php/ICWS…
    10K
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    Manoel
    @manoelribeiro
    Oct 25, 2023
    How can we ensure our data is generated by humans? Our new preprint shows that >30% of crowd workers used LLMs on a text summarization task on Prolific >This is similar to previous figures on less vetted MTurk >Mitigation strategies can reduce use >50% 📜arxiv.org/abs/2310.15683
    We show that the use of large language models (LLMs) is prevalent among crowd workers, and that targeted mitigation strategies can significantly reduce, but not eliminate, LLM use. On a text summarization task where workers were not directed in any way regarding their LLM use, the estimated prevalence of LLM use was around 30%, but was reduced by about half by asking workers to not use LLMs and by raising the cost of using them, e.g., by disabling copy-pasting. Secondary analyses give further insight into LLM use and its prevention: LLM use yields high-quality but homogeneous responses, which may harm research concerned with human (rather than model) behavior and degrade future models trained with crowdsourced data. At the same time, preventing LLM use may be at odds with obtaining high-quality responses; e.g., when requesting workers not to use LLMs, summaries contained fewer keywords carrying essential information. Our estimates will likely change as LLMs increase in popularity or ca
    72K
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    Manoel
    @manoelribeiro
    May 6, 2024
    Received ChatGPT-like reviews? They may have boosted your paper's odds of being accepted! In a quasi-experimental study of a top AI conference, ICLR, we measured the effect of AI-assisted peer reviews on scores and acceptance rates. (Led by @russogiusep) arxiv.org/abs/2405.02150
    76K
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    Manoel
    @manoelribeiro
    Apr 7, 2022
    The most useful paper anyone working with Twitter data could read. 30m that could save you a lifetime of headaches! Great analyses/discussion on the new (improved) API including: - location bias - time bias - data decay - content moderation bias 📰arxiv.org/abs/2204.02290
  • user avatar
    Manoel
    @manoelribeiro
    Apr 19, 2018
    Replying to @KLdivergence @wesmckinn and @hadleywickham
    Marvel: 'Infinity War is the most ambitious crossover event in history' Me: