iiinova — where your digital self keeps evolving

Hannah Overbye-Thompson @overbye.bsky.social · 15/06/2026

And that's a wrap on graduation weekend; I am officially done with my PhD 🥳

Hannah Overbye-Thompson @overbye.bsky.social · 31/12/2025

New year, new chapter! I am incredibly excited to share that starting in fall 2026, I'll be joining Michigan State University as a tenure-track Assistant Professor in Advertising + PR. A big thank you to everyone who has been extra kind during my job market year & Go Green! 💚🤍

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Hannah Overbye-Thompson @overbye.bsky.social · 16/10/2025

I've noticed w/research it's often hard to find validated measures of constructs without hobbling together scales from multiple papers; now when possible I try to contribute by validating scales. Below is a scale that I hope is helpful that measures the perceptual attributes of DOI + reinvention 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 14/10/2025

Fabulous study by @felix-dietrich.de @aliciaernst.bsky.social @rkreling.bsky.social et al., examining how algorithmic curation affects music streaming UX. Key finding: More algorithmic recommendations = less enjoyment, BUT listening sessions w/algorithmic curation were perceived as more novel 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 09/10/2025

New study (2025) examines how AI autonomy affects user agency and attitudes. Key finding: AI autonomy triggers psychological reactance through threats to freedom, BUT personalization benefits cancel this out + users with higher agency feel more threatened by autonomous AI 🧪 doi.org/10.1080/0883...

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Hannah Overbye-Thompson @overbye.bsky.social · 23/09/2025

New study (2025) examines if people can detect bias in AI training data. Key finding: Training data cues were largely ineffective; users relied on AI performance instead to judge bias + consistent with prior work on AI bias, the majority of participants failed to notice any bias in training data 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 03/09/2025

New paper (2025) by @len-s.bsky.social proposes the PMSIS model: parents can use racially diverse entertainment media + "foreground co-viewing" + active mediation to improve children's intergroup socialization 🧪 doi.org/10.1093/annc... Great work Sovannie 👏👏👏 #commsky

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Hannah Overbye-Thompson @overbye.bsky.social · 29/08/2025

New study by @janadreston.bsky.social @anneo.bsky.social & @germanneubaum.bsky.social reveals how users understand algorithms. Key findings: 71% have a basic understanding of algorithms but only 33% can explain how they work; users see themselves as passive actors when interacting with algorithms 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 27/08/2025

Excited to share my new paper with @garciaerick.bsky.social Xinyi Zhang & @laurentwang.bsky.social. We ask: Do people see algorithmic bias as a risk—and do they feel capable of addressing it? Answer... It depends! More below 👇🧪 #commsky doi.org/10.1080/1044...

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Hannah Overbye-Thompson @overbye.bsky.social · 21/08/2025

New study by Aquino et al. provides a fabulous look at differing opinions about algorithmic bias held by healthcare professionals. 72 experts had 3 key disagreements: whether bias exists (most say yes, some no), who's responsible for fixing it & whether to include race/ethnicity data in AI systems 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 21/08/2025

New study by @drjt.bsky.social examines if attention control explains the 🔗 between inspection time tasks and intelligence. Key finding: attention control fully mediated the inspection time-intelligence relationship + people with better sustained attention showed less performance decline over time 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 04/08/2025

After a great summer teaching Social Networks at UCSB, I wanted to share 4 of my favorite network examples we explored in class 🧪🧵 1. Who Will Be the Next Pope? 2. The Hidden Network of Trees 🌳 3. The Beer Graph 🍺 4. The Oracle of Bacon 🥓🎬

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Hannah Overbye-Thompson @overbye.bsky.social · 01/08/2025

New study (Campbell & Hawkins, 2025) examines why people expect news to find them on social media (vs seeking it out). Key finding: when people habitually scroll social media w/out thinking + believe algorithms/lack of control drive SM usage, they're more likely to rely on incidental news exposure 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 27/07/2025

One of the challenges I've had when studying the effects of algorithmic bias is getting a high-level picture of how bias enters algorithms. Here I offer a simple breakdown of where/how bias can arise across different stages of development as well as some domains where we commonly see bias 2/5 🧵

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Hannah Overbye-Thompson @overbye.bsky.social · 27/07/2025

Excited to share my new work aimed at making algorithmic bias easier to study w/3 key contributions • Clear overview of how/where algorithmic bias arises • Workarounds as a concept for user adaptation • Framework for analyzing bias that is real, perceived, or absent 1/5 🧵🧪 doi.org/10.1007/s001...

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Hannah Overbye-Thompson @overbye.bsky.social · 22/07/2025

Paper by @oriplonsky.bsky.social et al., introduces BEAST-GB, merging behavioral decision theory with XGBoost to predict choices under risk/uncertainty. Key finding: behavioral features are critical even with abundant training data, suggesting theory-guided ML beats purely data-driven approaches 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 08/07/2025

New paper by Zhang et al. examines algorithmic management (AM) systems identifying 5 perspectives: surveillance/control, neutral tool, agentic boss, socio-technical process, and contradictory unity. AM creates tensions between control vs autonomy, transparency vs opacity & efficiency vs fairness 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 03/07/2025

New study (2025) introduces the Capability-Personalization Framework to explain when people prefer AI vs humans. Meta-analysis reveals AI appreciation occurs only when AI seems more capable AND personalization is unnecessary; otherwise people show AI aversion 🧪 doi.org/10.1037/bul0...

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Hannah Overbye-Thompson @overbye.bsky.social · 12/06/2025

@rachaelkee.bsky.social blowing us all away with her project at @hackingcommsci.bsky.social. Super impressive project with the goal of collecting high-throughput sleep and media data ✨😴📺 #ICA25

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Hannah Overbye-Thompson @overbye.bsky.social · 11/06/2025

Great presentation by @kyliewoodman.bsky.social on fMRI analysis at @hackingcommsci.bsky.social. I am always blown away by how clearly Kylie is able explain complex topics #ICA25

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Hannah Overbye-Thompson @overbye.bsky.social · 10/06/2025

Headed to #ICA25? I’d love to connect over research! DM me or say hi if you're around 👋 Let’s find time to chat 📚☕

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Hannah Overbye-Thompson @overbye.bsky.social · 27/05/2025

Absolutely brilliant paper by @wankepan.bsky.social that solves a major barrier to drift-diffusion modeling by introducing dockerHDDM: a user-friendly Docker environment with easy installation, Apple chip compatibility, and modern Bayesian tools. The paper is also an excellent introduction to DDMs 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 22/05/2025

New study (2025) shows computer access is the strongest predictor of beneficial internet use, surpassing smartphones & in-home internet. Households with a computer but no internet still fared better than those with internet but no computer; device quality explained additional variance 🧪 #commsky

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Hannah Overbye-Thompson @overbye.bsky.social · 07/05/2025

Study by @janpfa.bsky.social found people can tell true from false news across 40 countries, n=194,438; true news was rated as more accurate than false news. Political concordance didn't affect discernment; people are not gullible of news they agree with but are skeptical of news they disagree with🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 28/04/2025

New study (2025) shows political affiliation is the strongest predictor of immigration views in 2020 election. High-income states and areas with more foreign residents showed more pro-immigration views. Anti-immigration tweets cited threats while pro-immigration focused on humanitarian concerns 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 23/04/2025

New study finds that datafication in newsrooms creates "coopetition" where journalists cooperate & compete. While data illuminate work outcomes journalists employ flexible interpretation strategies - embracing data when beneficial (data opportunism) while questioning it when not (data skepticism) 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 15/04/2025

Excellent study by @vukasin.bsky.social (2025) finds US liberals trust scientists more than conservatives across all fields. Despite generally high trust levels overall, the gap is largest for climatologists. 5 interventions failed to increase trust, suggesting attitudes resistant to interventions 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 10/04/2025

New study (2025) finds LLM "AMIE" shows promise for assisting with medical diagnoses. Clinicians using this LLM achieved modestly better accuracy (51.7% vs 36.1%) in challenging case studies, while AMIE alone performed well (59.1% accuracy vs 33.6% for unassisted doctors) 🧪 doi.org/10.1038/s415...

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Hannah Overbye-Thompson @overbye.bsky.social · 03/04/2025

Excellent overview by Fabris et al. (2025) of how algorithms are used in hiring, and the different ways these algorithms can unintentionally lead to discrimination 🧪 doi.org/10.1145/3696...

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Hannah Overbye-Thompson @overbye.bsky.social · 01/04/2025

Finally got the plaque from NCA's masters education section, thanks @rnevshep.bsky.social

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Hannah Overbye-Thompson @overbye.bsky.social · 27/03/2025

Study by Geiß, @christinaviehmann.bsky.social & Kelly shows inflation of crisis coverage in newspapers 1785-2020, with crisis labeling increasing faster than crisis waves. Why? Government expansion, mediatization of politics & crisis frame sponsor activity drive increases, not critical conditions 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 13/03/2025

Just read an excellent study by Kordzadeh & Ghasemaghaei that provides an overview of algorithmic bias research & proposes a framework linking biased outputs to user perceptions & behaviors. Key takeaway: we need more research on how bias influences human-algorithm dynamics🧪 doi.org/10.1080/0960...

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Hannah Overbye-Thompson @overbye.bsky.social · 25/02/2025

Study shows algorithmic bias detection is challenging - only ~41% of participants recognized bias in a hiring algorithm, favoring German over Turkish applicants. Noticing the bias reduced attitudes towards both algorithms generally and the hiring algorithm specifically 🧪 doi.org/10.3389/fpsy...

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Hannah Overbye-Thompson @overbye.bsky.social · 22/02/2025

Study shows machine learning transparency affects user trust - when systems disclosed they were learning from users, they were viewed as helpful rather than privacy threats, regardless of performance quality. Why? Cues about AI learning triggered "helper heuristic," reducing frustration 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 19/02/2025

Just read the Social Biome by Andy Merolla and Jeff hall, and I have to say this is one of the most well-researched yet accessible book on interpersonal communication I have read. I highly recommend 🧪 yalebooks.yale.edu/book/9780300...

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Hannah Overbye-Thompson @overbye.bsky.social · 18/02/2025

Study by @soojongkim.bsky.social (2024) shows racial identity shapes perceptions of algorithmic bias, with POC showing decreased trust when AI discriminates against them, while White participants show opposite patterns. Why? Lived experiences influence how people interpret AI outcomes 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 14/02/2025

New study (Dec. 2024) shows racial stereotyping thrives in online humor on r/RoastMe, with non-White roasts using racial and national stereotypes as humor. Why? Anonymous platforms amplify social identity cues, revealing how minimal context primes stereotypical associations 🧪 doi.org/10.1080/0363...

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Hannah Overbye-Thompson @overbye.bsky.social · 11/02/2025

Why do users limit the extent to which their engagement w/news content can be seen by others? @oriten.bsky.social (2025) shows 4 key drivers: protection, pointlessness, personality & particularity. How? Through reducing volume of expression, using private spaces & preferring closed-ended reactions 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 05/02/2025

Research by @soojongkim.bsky.social (2025) shows Perception of AI discrimination varies among different demographics: those with higher digital literacy trust AI more despite bias, liberals are more skeptical, and higher income corresponded with viewing biased outcomes as more fair 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 01/02/2025

New paper (2024) explicates Machine Heuristic (MH) and creates validated scale for MH across mechanical and human tasks. The authors found six key dimensions shaping MH: machine as expert, efficient, rigid, superfluous, fair, and complex, with distinct patterns for mechanical vs human tasks 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 28/01/2025

Study by @mariekevhoof.bsky.social et al.(2024) compares 5 methods for detecting political searches in browser histories: Zero-shot AI (like GPT) achieves highest recall but needs human validation for accuracy. Best approach is AI-human collaboration: use AI to filter searches, then human review. 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 23/01/2025

New study by @dschiff.bsky.social finds AI ethics auditing focuses heavily on technical assessment (bias, privacy, explainability) but lacks stakeholder engagement & transparency. Despite regulatory pressure, auditors face challenges w/ unclear standards & limited resources🧪 doi.org/10.1177/2053...

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Hannah Overbye-Thompson @overbye.bsky.social · 21/01/2025

New meta-analysis (2024) finds human-AI collaboration is task-dependent: performance increases for creative & open-ended tasks (like content creation), but performance drops for decision-making tasks. Furthermore, human-AI collaboration seems to work best when humans already outperform AI 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 15/01/2025

Training AI on data with small biases amplifies bias; humans then learn these biases. This was found in perceptual (emotion & motion detection) and social tasks (employment profiling) where bias was operationalized as deviating from ground truth - 50/50 stimuli, motion %, demographic distributions 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 09/01/2025

New research by @paologerbaudo.bsky.social argues that TikTok has transformed social media from 'networked' to 'clustered' publics, where algorithmic interest-based grouping replaces friend-based connections, fundamentally changing how we experience and interact online 🧪 doi.org/10.1177/1461...

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Hannah Overbye-Thompson @overbye.bsky.social · 06/01/2025

New research by Lu & Yuan (2024) shows AI disclaimers on deepfake parodies reduce perceived comprehension difficulty, which in turn influences enjoyment, discounting, and counter-arguing, subsequently affecting policy support and sharing intentions 🧪 doi.org/10.1093/joc/...

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Hannah Overbye-Thompson @overbye.bsky.social · 05/01/2025

I'll be back to posting article summaries soon, but in the meantime meet the newest member of the Overbye-Thompson household 🐶

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Hannah Overbye-Thompson @overbye.bsky.social · 29/12/2024

For Christmas my brother got me a kit to build & program a computer with the classic 6502 microprocessor (why I don't know) I'm both touched and amused by his confidence in my abilities - now I just need to learn to read circuit schematics 😂 Great project for the week before the quarter starts 👩‍💻👷‍♀️

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Hannah Overbye-Thompson @overbye.bsky.social · 21/12/2024

New research by Zhang et al 2024 shows use of ChatGPT by students for brainstorming & feedback (academic support) is positively associated with academic self-efficacy & creativity but using it for completing assignments & answering exam questions (academic displacement) shows negative associations 🧪

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Hannah Overbye-Thompson @overbye.bsky.social · 12/12/2024

@mariekevhoof.bsky.social et al., 2024 find algorithmic personalization alone doesn't lead to different political information when using search engines; results depend far more on the search query than search history 🧪 doi.org/10.1093/jcmc... @damiantrilling.bsky.social @judith-moeller.bsky.social

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