arXiv subjects

Er-Te Zheng

Publications and source records attributed to Er-Te Zheng.

8 recordsLinked to original sources

arXiv2026

Should children follow their parents' research paths? Intergenerational research continuity and divergence in academic families

How academic advantages are transmitted within families is usually studied as occupational inheritance, but it is not clear whether scholarly research orientations persist across generations and if it is an advantage when it does. To address this, we link Wikidata kinship records with OpenAlex bibliometric profiles to study 3,229 documented parent-child scholar pairs and 488,659 publications. Field-level research similarity was evident but not universal: whilst the median similarity was 0.546, 25.3% of parent-child pairs had no Field overlap (i.e., similarity 0). These pairs were substantially more similar than publication-period-matched comparison pairs (median 0.098). Direct academic interaction was uncommon: 10.4% of parent-child pairs had co-authored, 9.8% of children had cited their parents, and 6.9% of parents had cited their children. Nevertheless, each 0.1 increase in Field similarity was associated with 38-39% higher adjusted odds of co-authorship and cross-citation. There was also intergenerational continuity in academic achievement and recognition. Parents' publication volume and field-normalized citation impact were positively associated with those of their children. Children of national academy members had approximately twice the odds of becoming national academy members themselves (Odds Ratio = 2.04), while children of prizewinning parents had 46% higher odds of winning prizes (Odds Ratio = 1.46). However, children of national academy members showed lower research similarity to their parents. Greater research differentiation was associated with higher field-normalized citation impact among children, but not with publication output or higher odds of academic recognition. Academic families therefore appear to transmit resources and advantages with the sole exception that diverging from parental fields seems to confer a citation advantage.

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arXiv2026

Science discussions of retracted articles on Bluesky: public scrutiny or misinformation spreading?

Post-publication peer review (PPPR) has emerged as an important supplement to traditional peer review, with social media playing a growing role in publicising potential problems in published research. However, it remains unclear whether social media discussions of retracted articles primarily reflect good practices, such as exposing flaws and acknowledging retraction status, or bad practices, such as overlooking retractions and continuing to disseminate scientific misinformation. In this study, we collected Bluesky posts referencing scholarly articles from Altmetric and retrieved metadata for the referenced articles using OpenAlex. The final dataset included 284 retracted articles with 79 pre-retraction posts and 857 post-retraction posts, 59 retraction notices with 186 posts, and 609,461 non-retracted articles with 1,344,756 posts. We manually coded Bluesky posts discussing retracted articles to identify instances of good and bad practice. The results show that posts demonstrating good practice (89.9%) substantially outnumbered those demonstrating bad practice (10.1%). Posts reflecting good practice also had more user engagement. In the pre-retraction phase, good practice posts constituted a slight minority (43.0%), whereas in the post-retraction phase they were dominant (94.2%). Most negative posts in the pre-retraction phase (90.0%) had good practice while only 17.3% positive posts in the post-retraction phase showed bad practice. Thus, sentiment analysis can be helpful to filter posts that could flag potential flaws before retraction, but it may struggle to accurately identify the spread of misinformation after retraction. More broadly, this study highlights the potential of Bluesky to support responsible scientific communication, public scrutiny, and research integrity.

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arXiv2026

Organisational accounts engaged in scholarly communication on Twitter: Patterns of presence, activity and engagement

Organisational accounts are an integral part of the Twitter (now X) ecosystem. This study identified 9,842 research- and policy-related organisational accounts that had tweeted about scholarly publications by linking three global organisational databases (GRID, ROR, and Overton) with two altmetric databases containing Twitter data (Altmetric and the former Crossref Event Data). The resulting openly available dataset was used to examine organisational activity in scholarly communication across three dimensions: social media capital, tweeting activity, and engagement level. The results show that, compared to all Twitter users engaged in scholarly communication, organisational accounts hold a notable advantage in terms of follower bases and the proportion of scholarly tweets. Their scholarly tweets achieve high visibility through likes and retweets but perform weakly in generating more conversational forms of engagement, such as quotes and replies. Distinct patterns emerge across organisational categories: research facilities, in particular, demonstrate the strongest focus on scholarly tweeting, whereas government accounts are comparatively more successful in eliciting engagement across all metrics, including the more interactive ones. This study contributes both an open dataset of organisational accounts and a methodological framework for their identification, while also highlighting the important roles that organisations play in shaping scholarly discourse on social media.

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arXiv2025

How is science discussed on Bluesky?

Amid the migration of academics from X, the social media platform Bluesky has emerged as a potential alternative. To assess its viability and relevance for science communication, this study presents the first large-scale analysis of scholarly article dissemination on Bluesky, exploring its potential as a new source of social media metrics. We collected and analysed over 2.6 million Bluesky posts referencing 532,302 scholarly articles from January 2023 to July 2025, integrating metadata from the OpenAlex database. Temporal trends, disciplinary coverage, language use, textual characteristics, and user engagement were examined. A sharp increase in scholarly activity on Bluesky was observed from November 2024 to January 2025, coinciding with broader academic shifts away from X. As on X, Bluesky posts primarily concern the health, social, and environmental sciences and are predominantly written in English. Nevertheless, Bluesky posts demonstrate substantially higher levels of interaction (likes, reposts, replies, and quotes) and greater textual originality than previously reported for X, suggesting both stronger interactive and more interpretive engagement. These findings highlight Bluesky's emerging role as a credible platform for science communication and a promising source for altmetrics. The platform may facilitate not only early visibility of research outputs but also more meaningful scholarly dialogue in the evolving social media landscape.

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arXiv2025

Do male leading authors retract more articles than female leading authors?

Scientific retractions reflect issues within the scientific record, arising from human error or misconduct. Although gender differences in retraction rates have been previously observed in various contexts, no comprehensive study has explored this issue across all fields of science. This study examines gender disparities in scientific misconduct or errors, specifically focusing on differences in retraction rates between male and female first authors in relation to their research productivity. Using a dataset comprising 11,622 retracted articles and 19,475,437 non-retracted articles from the Web of Science and Retraction Watch, we investigate gender differences in retraction rates from the perspectives of retraction reasons, subject fields, and countries. Our findings indicate that male first authors have higher retraction rates, particularly for scientific misconduct such as plagiarism, authorship disputes, ethical issues, duplication, and fabrication/falsification. No significant gender differences were found in retractions attributed to mistakes. Furthermore, male first authors experience significantly higher retraction rates in biomedical and health sciences, as well as in life and earth sciences, whereas female first authors have higher retraction rates in mathematics and computer science. Similar patterns are observed for corresponding authors. Understanding these gendered patterns of retraction may contribute to strategies aimed at reducing their prevalence.

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arXiv2025

Social media uptake of scientific journals: A comparison between X and WeChat

This study examines the social media uptake of scientific journals on two different platforms - X and WeChat - by comparing the adoption of X among journals indexed in the Science Citation Index-Expanded (SCIE) with the adoption of WeChat among journals indexed in the Chinese Science Citation Database (CSCD). The findings reveal substantial differences in platform adoption and user engagement, shaped by local contexts. While only 22.7% of SCIE journals maintain an X account, 84.4% of CSCD journals have a WeChat official account. Journals in Life Sciences & Biomedicine lead in uptake on both platforms, whereas those in Technology and Physical Sciences show high WeChat uptake but comparatively lower presence on X. User engagement on both platforms is dominated by low-effort interactions rather than more conversational behaviors. Correlation analyses indicate weak-to-moderate relationships between bibliometric indicators and social media metrics, confirming that online engagement reflects a distinct dimension of journal impact, whether on an international or a local platform. These findings underscore the need for broader social media metric frameworks that incorporate locally dominant platforms, thereby offering a more comprehensive understanding of science communication practices across diverse social media and contexts.

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arXiv2025

Can news and social media attention reduce the influence of problematic research?

News and social media are widely used to disseminate science, but do they also help raise awareness of problems in research? This study investigates whether high levels of news and social media attention might accelerate the retraction process and increase the visibility of retracted articles. To explore this, we analyzed 15,642 news mentions, 6,588 blog mentions, and 404,082 X mentions related to 15,461 retracted articles. Articles receiving high levels of news and X mentions were retracted more quickly than non-mentioned articles in the same broad field and with comparable publication years, author impact, and journal impact. However, this effect was not statistically signicant for articles with high levels of blog mentions. Notably, articles frequently mentioned in the news experienced a significant increase in annual citation rates after their retraction, possibly because media exposure enhances the visibility of retracted articles, making them more likely to be cited. These findings suggest that increased public scrutiny can improve the efficiency of scientific self-correction, although mitigating the influence of retracted articles remains a gradual process.

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arXiv2024

Can social media provide early warning of retraction? Evidence from critical tweets identified by human annotation and large language models

Timely detection of problematic research is essential for safeguarding scientific integrity. To explore whether social media commentary can serve as an early indicator of potentially problematic articles, this study analysed 3,815 tweets referencing 604 retracted articles and 3,373 tweets referencing 668 comparable non-retracted articles. Tweets critical of the articles were identified through both human annotation and large language models (LLMs). Human annotation revealed that 8.3% of retracted articles were associated with at least one critical tweet prior to retraction, compared to only 1.5% of non-retracted articles, highlighting the potential of tweets as early warning signals of retraction. However, critical tweets identified by LLMs (GPT-4o mini, Gemini 2.0 Flash-Lite, and Claude 3.5 Haiku) only partially aligned with human annotation, suggesting that fully automated monitoring of post-publication discourse should be applied with caution. A human-AI collaborative approach may offer a more reliable and scalable alternative, with human expertise helping to filter out tweets critical of issues unrelated to the research integrity of the articles. Overall, this study provides insights into how social media signals, combined with generative AI technologies, may support efforts to strengthen research integrity.

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