Ali Sina Önder on Twitter / X

Social media promotion improves job market outcomes

Social media has transformed how academics disseminate research, but its effect on academic job outcomes remains unclear. Previous research has shown correlations between social media exposure and metrics like citation counts, but these relationships may be confounded by unobserved factors such as researcher quality or access to professional networks. We examine whether social media promotion causally affects job market outcomes in economics through a field experiment on Twitter (now X). We first collect tweets about job market papers from 519 candidates and post them from a dedicated account. We then randomize half of the posts to be quote-tweeted by established economists in the candidates’ fields, and measure the effects on both online visibility and hiring outcomes. We find that posts in the treatment group receive 441% more views and 303% more likes than those in the control group. Candidates whose posts were assigned to be quote-tweeted receive one additional flyout invitation compared to the control group average of 5.4 flyouts. Furthermore, women in the treatment group receive 0.9 more job offers than women in the control group, who receive 3 offers on average. Exploring mechanisms, we find that academic reputation drives these results, with stronger effects for quote-tweets from highly cited scholars and for candidates from top institutions. Our findings suggest social media promotion causally increases research visibility and improves academic job market outcomes.

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.

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

How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications

Abstract To provide some context for the potential engagement behavior of Twitter users around science, this article investigates how Bitly short links to scientific publications embedded in scholarly Twitter mentions are clicked on Twitter. Based on the click metrics of over 1.1 million Bitly short links referring to Web of Science (WoS) publications, our results show that around 49.5% of them were not clicked by Twitter users. For those Bitly short links with clicks from Twitter, the majority of their Twitter clicks accumulated within a short period of time after they were first tweeted. Bitly short links to the publications in the field of Social Sciences and Humanities tend to attract more clicks from Twitter over other subject fields. This article also assesses the extent to which Twitter clicks are correlated with some other impact indicators. Twitter clicks are weakly correlated with scholarly impact indicators (WoS citations and Mendeley readers), but moderately correlated to other Twitter engagement indicators (total retweets and total likes). In light of these results, we highlight the importance of paying more attention to the click metrics of URLs in scholarly Twitter mentions, to improve our understanding about the more effective dissemination and reception of science information on Twitter.

Sources on social media: Information context collapse and volume of content as predictors of source blindness - George Pearson, 2021

Although social media has become a primary news platform, the effects of social media features on users’ information processing remains under-explored. This stu...

Citing Less Critically: LLMs Reshape the Rhetoric and Reach of Scientific Citation

Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentioning). As large language models (LLMs) increasingly assist scientific writing, whether they reproduce citations with the same rhetorical intent as humans remains unclear. We introduce a masked-citation task to compare human and LLM-generated citation behavior. For each citation context, an LLM generates a replacement citation sentence, producing a counterfactual corpus directly comparable to human citation. We analyze what, whom, and how models cite, using an LLM-as-a-judge to classify citation intent and a 20-million-edge coauthorship network to measure social distance between cited authors. Across six popular LLMs and 1,746 top NLP conference papers (63k+ contexts, 132k+ citations), three patterns emerge: (1) Compared with human citation, LLMs cite significantly less critically; (2) LLMs over-cite popular and older papers, a tendency amplified for contrasting citations where human writing more often draws on recent, niche work; (3) Whereas humans often cite within their close social network, especially for supporting citations, LLMs tend to draw on more socially distant authors. Together, these differences are double-edged: LLM citation reaches beyond a scholar's close collaborators while being less critical and amplifying visibility bias, reshaping the rhetoric and reach of scientific citation.

Do links hurt news publishers on Twitter? Our analysis suggests yes

Engagement for tweets from @nytimes (53 million followers) is dwarfed by engagement for tweets from @GlobeEyeNews (866,000 followers).

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.

Michael 英泉 Eisen on Twitter / X

ALSO WHY THE FUCK ARE WE STILL JUST CITING PAPERS INSTEAD OF SPECIFIC PIECES OF DATA OR CLAIMS? https://t.co/dngS9d8nOW— Michael 英泉 Eisen (@mbeisen) May 16, 2026

Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study

Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of ≈ 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet’s engagement level and a user’s followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter’s recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users.