Social media promotion improves job market outcomes | PNAS

Do Science <i>Kardashians</i> Get Citation Premium? Self‐Fulfilling Effects of Social Media on Scientific Impact

ABSTRACT We analyze whether the visibility of scientists on social media affects the number of academic citations. We use the global COVID‐19 pandemic as a quasinatural experiment that exogenously increased public attention and the demand for expertise. Using publications on COVID‐related topics by social media stars and their coauthors prior to the outbreak of the pandemic, we find that social media stars' pre‐COVID‐era papers received about – more citations annually per paper after 2019. Quantitatively comparable results are obtained when we use scientists' Kardashian index (K‐index) as a benchmark for stardom, however we find no significant effects when using the intensive margin of scientists' K‐indexes. We provide a brief discussion of policy implications in light of these findings.

Ali Sina Önder on Twitter / X

Does your social media visibility affect your citations? Yes because social media visibility enhances your "expert" status. Here is the brand new paper with @econ_lessmann and Max Rose: https://t.co/KJaxgOaAYQ @davidstadelmann @MishaTeplitskiy @csugimoto @voxeu @AntonioFatas pic.twitter.com/kioHRJ0gYH— Ali Sina Önder (@asonder79) May 3, 2026

Custodians of the Internet

A revealing and gripping investigation into how social media platforms police what we post online—and the large societal impact of these decisions Most use...

Shreya Shankar on Twitter / X

This problem has gotten significantly worse. As Twitter and other social media have become primary channels for sharing research, academics are now expected to make the same ideas legible and appealing to both the general public (to go "viral") and senior scholars (to get the… https://t.co/zPF3tKzbVa— Shreya Shankar (@sh_reya) July 25, 2026

Digital Social Norm Enforcement: Online Firestorms in Social Media

Actors of public interest today have to fear the adverse impact that stems from social media platforms. Any controversial behavior may promptly trigger temporal, but potentially devastating storms of emotional and aggressive outrage, so called online firestorms. Popular targets of online firestorms are companies, politicians, celebrities, media, academics and many more. This article introduces social norm theory to understand online aggression in a social-political online setting, challenging the popular assumption that online anonymity is one of the principle factors that promotes aggression. We underpin this social norm view by analyzing a major social media platform concerned with public affairs over a period of three years entailing 532,197 comments on 1,612 online petitions. Results show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals. This effect is reinforced if selective incentives are present and if aggressors are intrinsically motivated.

Digital Social Norm Enforcement: Online Firestorms in Social Media

Actors of public interest today have to fear the adverse impact that stems from social media platforms. Any controversial behavior may promptly trigger temporal, but potentially devastating storms of emotional and aggressive outrage, so called online firestorms. Popular targets of online firestorms are companies, politicians, celebrities, media, academics and many more. This article introduces social norm theory to understand online aggression in a social-political online setting, challenging the popular assumption that online anonymity is one of the principle factors that promotes aggression. We underpin this social norm view by analyzing a major social media platform concerned with public affairs over a period of three years entailing 532,197 comments on 1,612 online petitions. Results show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals. This effect is reinforced if selective incentives are present and if aggressors are intrinsically motivated.

Sharing without clicking on news in social media

Social media have enabled laypersons to disseminate, at scale, links to news and public affairs information. Many individuals share such links without first reading the linked information. Here we analysed over 35 million public Facebook posts with uniform resource locators shared between 2017 and 2020, and discovered that such ‘shares without clicks’ (SwoCs) constitute around 75% of forwarded links. Extreme and user-aligned political content received more SwoCs, with partisans engaging in it more than politically neutral users. In addition, analyses with 2,969 false uniform resource locators revealed higher shares and, hence, SwoCs by conservatives (76.94%) than liberals (14.25%), probably because, in our dataset, the vast majority (76–82%) of them originated from conservative news domains. Findings suggest that the virality of political content on social media (including misinformation) is driven by superficial processing of headlines and blurbs rather than systematic processing of core content, which has design implications for promoting deliberate discourse in the online public sphere.

The associations of social media attention, visibility, disinformation and retraction initiators with time to retraction: a Cox regression analysis

Purpose This study examines how retraction reasons, retraction initiators, journal visibility, access models and Twitter activity associate with the speed of retracting flawed scientific publications. Design/methodology/approach Using a Cox proportional hazards model, we analyzed 1,179 articles retracted in 2019–2021, including a subset of 98 papers tweeted before retraction. Findings The results reveal that higher journal impact factor and open-access status were associated with faster retractions. However, a significant negative interaction indicated that the effect of high-impact journals diminished for open-access publications. Journal-initiated retractions were slower overall, except in cases of misconduct such as co-authorship deception and plagiarism, where journals acted more quickly. Among retraction reasons, only deception in co-authoring was associated with significantly slower retractions, but this trend reversed when journals led the process. The association of social media attention with retraction speed was statistically robust, albeit modest in magnitude: each additional pre-retraction tweet was associated with a slight reduction in time to retraction. Bootstrap validation confirmed the stability of this finding. Originality/value Public scrutiny, institutional responsibility and publication visibility jointly shape the time to retraction. This study advances altmetrics discourse by positioning social media as a conditional, yet meaningful, participant in the retraction lifecycle. Beyond altmetrics, our findings highlight retractions as part of a broader network of relationships between public accountability, digital ethics and science communication, positioning them as moments of accountability shaped jointly by journals, ethical responsibilities and digital publics.

Following news on social media boosts knowledge, belief accuracy and trust

Many worry that news on social media leaves people uninformed or even misinformed. Here we conducted a preregistered two-wave online field experiment in France and Germany (N = 3,395) to estimate the effect of following the news on Instagram and WhatsApp. Participants were asked to follow two accounts for 2 weeks and activate the notifications. In the treatment condition, the accounts were those of news organizations, while in the control condition they covered cooking, cinema or art. The treatment enhanced current affairs knowledge, participants’ ability to discern true from false news stories and awareness of true news stories, as well as trust in the news. The treatment had no significant effects on feelings of being informed, political efficacy, affective polarization and interest in news or politics. These results suggest that, while some forms of social media use are harmful, others are beneficial and can be leveraged to foster a well-informed society.

Nobody knows how the whole system works

One of the surprising (at least to me) consequences of the fall of Twitter is the rise of LinkedIn as a social media site. I saw some interesting posts I wanted to call attention to: First, Simon W…

Shifts in U.S. Social Media Use, 2020–2024: Decline, Fragmentation, and Enduring Polarization

Using nationally representative data from the 2020 and 2024 American National Election Studies (ANES), this paper describes how U.S. social media use has shifted across platforms, demographics, and politics. Overall platform reach declined, driven by growth in the share of Americans — especially the youngest and oldest cohorts — who report using no social media. Visiting and posting activity on Twitter/X and Facebook have fallen by nearly 50% since 2020, with the decline on Twitter/X driven primarily by reduced participation among Democratic users. While Facebook, YouTube, and Twitter/X lost ground, TikTok and Reddit grew modestly, consistent with a more fragmented digital public sphere. Platform audiences aged and became slightly more educated and racially diverse. Politically, most platforms shifted toward Republican users while remaining, on balance, Democratic-leaning. Twitter/X experienced the largest change: among posters, the partisan balance swung over 70 percentage points from Democrats to Republicans. Across platforms, political posting remains closely tied to affective polarization, as the most partisan respondents are also the most active. As casual users disengage while polarized partisans remain vocal, online discourse becomes narrower and more ideologically extreme.

Reranking partisan animosity in algorithmic social media feeds alters affective polarization

Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field ...

👊💰 We need a revolution in social media business models

Deepti Doshi on the business incentives underlying social media

Commercial Persuasion in AI-Mediated Conversations

As Large Language Models (LLMs) become a primary interface between users and the web, companies face growing economic incentives to embed commercial influence into AI-mediated conversations. We present two preregistered experiments (N = 2,012) in which participants selected a book to receive from a large eBook catalog using either a traditional search engine or a conversational LLM agent powered by one of five frontier models. Unbeknownst to participants, a fifth of all products were randomly designated as sponsored and promoted in different ways. We find that LLM-driven persuasion nearly triples the rate at which users select sponsored products compared to traditional search placement (61.2% vs. 22.4%), while the vast majority of participants fail to detect any promotional steering. Explicit "Sponsored" labels do not significantly reduce persuasion, and instructing the model to conceal its intent makes its influence nearly invisible (detection accuracy < 10%). Altogether, our results indicate that conversational AI can covertly redirect consumer choices at scale, and that existing transparency mechanisms may be insufficient to protect users.