arXiv subjects

Kinga Makovi

Publications and source records attributed to Kinga Makovi.

5 recordsLinked to original sources

arXiv2026

A field experiment of social influence and behavioral contagion with bots on Reddit

Recent advances in AI have heightened scholars' and policy makers' concern with social influence and behavioral contagion in online communities. We conduct a field experiment on Reddit to investigate the extent to which online users are susceptible to positive behavioral stimuli from other users and artificial agents. We let apparent human and bot accounts give symbolic awards to users with one of four rationales: praising the recipient's logical argument, emotional sensitivity, or moral integrity, or explaining that the award resulted from a random draw in a lottery. We evaluate how the different rationales for the award affect the recipients' subsequent behavior on the platform in terms of volume, impact, and content, as well as the further behavioral contagion to other users. We find that awards do not increase user activity and downstream impact, and awards from bots with the lottery rationale can in fact reduce them. Nevertheless, awards encourage direct communication between users. These findings highlight the possible resilience of online users to simple behavioral manipulation from platform algorithms and artificial agents, but not necessarily to more sophisticated schemes that simulate human conversation. Transparently labeling automated agents remains essential for ethical and effective platform governance.

cs.SI↗

arXiv2023

Characterizing the effect of retractions on publishing careers

Retracting academic papers is a fundamental tool of quality control, but it may have far-reaching consequences for retracted authors and their careers. Previous studies have highlighted the adverse effects of retractions on citation counts and coauthors' citations; however, the broader impacts beyond these have not been fully explored. We address this gap leveraging Retraction Watch, the most extensive data set on retractions and link it to Microsoft Academic Graph and Altmetric. Retracted authors, particularly those with less experience, often leave scientific publishing in the aftermath of retraction, especially if their retractions attract widespread attention. However, retracted authors who remain active in publishing maintain and establish more collaborations compared to their similar non-retracted counterparts. Nevertheless, retracted authors generally retain less senior and less productive coauthors, but gain more impactful coauthors post-retraction. Our findings suggest that retractions may impose a disproportionate impact on early-career authors.

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arXiv2023

Perception, performance, and detectability of conversational artificial intelligence across 32 university courses

The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work -- a possibility that has sparked discussions on the integrity of student evaluations in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses. Further, students' perspectives regarding the use of such tools, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of ChatGPT against students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use. We find that ChatGPT's performance is comparable, if not superior, to that of students in many courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection. Finally, we find an emerging consensus among students to use the tool, and among educators to treat this as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of AI into educational frameworks.

cs.CY↗

arXiv2019

The Impact of Informal Mentorship in Academic Collaborations

Inspired by the numerous benefits of mentorship in academia, we study "informal mentorship" in scientific collaborations, whereby a junior scientist is supported by multiple senior collaborators, without them necessarily having any formal supervisory roles. To this end, we analyze 2.5 million unique pairs of mentor-protégés spanning 9 disciplines and over a century of research, and we show that mentorship quality has a causal effect on the scientific impact of the papers written by the protégé post mentorship. This effect increases with the number of mentors, and persists over time, across disciplines and university ranks. The effect also increases with the academic age of the mentors until they reach 30 years of experience, after which it starts to decrease. Furthermore, we study how the gender of both the mentors and their protégé affect not only the impact of the protégé post mentorship, but also the citation gain of the mentors during the mentorship experience with their protégé. We find that increasing the proportion of female mentors decreases the impact of the protégé, while also compromising the gain of female mentors. While current policies that have been encouraging junior females to be mentored by senior females have been instrumental in retaining women in science, our findings suggest that the impact of women who remain in academia may increase by encouraging opposite-gender mentorships instead.

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arXiv2013

Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action.

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