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Kobi Hackenburg

@kobihackenburg.bsky.social

388 followers 84 following 31 posts

data science + political communication @oiioxford @uniofoxford

Posts Replies Media

Kobi Hackenburg @kobihackenburg.bsky.social · 21/07/2025

Today (w/ @ox.ac.uk @stanford @MIT @LSE) we’re sharing the results of the largest AI persuasion experiments to date: 76k participants, 19  LLMs, 707 political issues. We examine “levers” of AI persuasion: model scale, post-training, prompting, personalization, & more! 🧵:

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Kobi Hackenburg @kobihackenburg.bsky.social · 07/03/2025

📈Out today in @PNASNews!📈 In a large pre-registered experiment (n=25,982), we find evidence that scaling the size of LLMs yields sharply diminishing persuasive returns for static political messages. 🧵:

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Reposted by Kobi Hackenburg

Paul Röttger @paul-rottger.bsky.social · 13/02/2025

Are LLMs biased when they write about political issues? We just released IssueBench – the largest, most realistic benchmark of its kind – to answer this question more robustly than ever before. Long 🧵with spicy results 👇

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Reposted by Kobi Hackenburg

David Rand @dgrand.bsky.social · 19/11/2024

Everyone going to SJDM this weekend, come to our special session on using #LLMs in #JDM research on Monday at 9:45am (location = Empire Complex)! w/ @kobihackenburg.bsky.social Hope Schroeder and myself - presentations from us but also hopefully lots of discussion/Q&A with all of you!

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Reposted by Kobi Hackenburg

Ross Dahlke @rossdahlke.bsky.social · 01/02/2024

Labeling misinformation as misleading and from fellow in-group members (e.g., dem/ rep) makes people less likely to share it, suggesting social identity is effective in mitigating misinfo, finds @clarapretus.bsky.social @kobihackenburg.bsky.social @mtsakiris.bsky.social @jayvanbavel.bsky.social

The Misleading count: an identity-based intervention to counter partisan misinformation sharing

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Figure 1. Employed interventions. Examples of the employed interventions including (a) the Misleading count condition (30% of the Like count) used in Experiment 1, (b) the official Twitter Misleading tag used in Experiment 2, and (c) the accuracy nudge used in Experiment 3.

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Figure 2. Effect of the intervention across experiments. (a,c,e) Likelihood of sharing social media posts on polarizing issues (Experiments 1–3) and non-polarizing issues (Experiment 1) in response to the Misleading count (Experiments1–3), the official Twitter tag (Experiment 2) and the accuracy nudge (Experiment 3) compared with control by group and condition (tagged by in-group’ and ‘tagged by anyone). In Experiment 1, the Misleading count was always 30% of the Like count. In Experiments 2 and 3, the Misleading count was presented in two conditions: high count (80% of the Like count) and low count (20% of the Like count). The absolute Misleading count was two orders of magnitude higher in Experiment 1 as compared with Experiments 2 and 3 (e.g. 10 000 versus 100). Error bars represent 95% confidence intervals. (b,d,f) Coefficient estimates of the contrast between each intervention compared wiith control for social media posts relevant to polarizing (Experiments 1–3)

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Figure 3. Perceived accuracy, attitude strength, and likelihood of sharing social media posts across experiments. (a,d,g) Perceived accuracy, attitude strength (certainty, extremity, and importance), familiarity, and salience of the used social media posts by group (Exp. 1–3) and type of issue (polarizing and non-polarizing) (Exp. 1) as tested in pilot studies (Exp 1: N = 370; Exp 2: N = 80; Exp 3: N = 234). (b,e,g) Likelihood of sharing social media posts on polarizing issues (Exp. 1–3) and non-polarizing issues (Exp. 1) as a function of political affiliation. (c,f,i) Extreme political orientation was associated with an increased likelihood of sharing social media posts in the control condition (no interventions) across samples and political groups (liberals in blue and conservatives in red). Notably, U.S. samples in Exp. 1 and 3 were more polarized in terms of political orientation compared to the UK sample in Exp. 2.

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