Policy labs in Europe: political innovation, structure and content analysis on Twitter

Recent years have seen a veritable boom in the creation of policy labs. These institution-based innvation laboratories aim to open up the processes of public policy design to the social stakeholders involved. In 2016, the European Union Policy Lab commissioned a report that identified 64 such laboratories in Europe. In the present study, we use network analysis to reveal the structure of the relationships between the 42 of these labs that have a presence on Twitter. We then conduct a content analysis of their tweets to identify the topics of interest. Our results describe a fragmented, country-based network and the principal concepts and key issues addressed by these institutions

Where is the science in Wikipedia? Identification and characterization of scientifically supported contents

This study illustrates the challenges of developing a broad Wikipedia thematic landscape. Particularly the limitations of Wikipedia categories in providing an overview of the thematic areas covered in Wikipedia are shown. The use of WikiProjects is presented as a viable although limited alternative, providing interesting classificatory possibilities. The classification proposed here can be useful for further research on Wikipedia as well as for other researchers who want to identify Wikipedia dynamics in a more aggregated and visual way

A Brief Introduction to Big Data for Humanists

The term‘big data’is still somewhat confusing for researchers, as most as-sociate it with its most basic objectives such as data collection and processing ofoperations and do not have a clear overview of its scope and implications (Favar-etto et al. 2020). Moreover, there is a certain sense of uneasiness towards big dataas it is a cultural phenomenon in a state of constant change and evolution andthe use of this concept as a buzzword further aggravates its conceptual vague-ness. Therefore, the aim of this chapter is to offer a synthetic vision of what isunderstood as big data to serve as a starting point for researchers in the field ofhumanitie

ChatGPT for Bibliometrics: Potential applications and limitations

This is a preprint version of a chapter to be published in Library Catalogues as Data: Research, Practice, and Usage (Facet Publishing), co-edited by Prof. Melissa Terras and Dr. Sarah Ames.Versión 1. https://hdl.handle.net/10481/91334 Versión 2. https://hdl.handle.net/10481/92547This paper explores the transformative role of ChatGPT in enhancing bibliometric research methodologies across various stages of academic study. It discusses the application of ChatGPT in bibliometric studies across five core research stages: preparation and consultation, data processing, data analysis, results interpretation, and scientific writing. Highlighting ChatGPT's versatility, the paper showcases its utility in streamlining data handling, enhancing analytical depth, and facilitating scholarly communication. With capabilities ranging from querying external APIs to customising responses for specific research needs, ChatGPT may aid the efficiency and efficacy of bibliometric research. Ethical considerations are also discussed, advocating for the integration of ChatGPT to uphold high ethical standards and improve research integrity

The Elon Musk Paradox: Quantifying the Presence and Impact of Twitter Bots on Altmetrics with Focus in Social Sciences

With the rise of Twitter bots in social and political spheres, their implications in scientific communication and altmetrics have become a concern. However, there are no large-scale studies that identify the population of bots and their impact on altmetrics. This quantitative study aims to analyse the presence and impact of Twitter bots in the dissemination of Social Science papers on Twitter and to explore the specific case of Information Science & Library Science (ISLS) as a case study. The overall presence of bots discussing Social Science papers has been found to account for 3.61% of users and 3.85% of tweets. However, this presence and impact is uneven across disciplines, highlighting Criminology & Penology with 12.4% of the mentions made by bots. In the specific case of ISLS, it has been determined by Kendall's correlation that mentions of bots have no impact on altmetrics.Full paper available at: https://dapp.orvium.io/deposits/644235015db3c5af25159230/vie

The Botization of Science? Large-scale study of the presence and impact of Twitter bots in science dissemination

Twitter bots are a controversial element of the platform, and their negative impact is well known. In the field of scientific communication, they have been perceived in a more positive light, and the accounts that serve as feeds alerting about scientific publications are quite common. However, despite being aware of the presence of bots in the dissemination of science, no large-scale estimations have been made nor has it been evaluated if they can truly interfere with altmetrics. Analyzing a dataset of 3,744,231 papers published between 2017 and 2021 and their associated 51,230,936 Twitter mentions, our goal was to determine the volume of publications mentioned by bots and whether they skew altmetrics indicators. Using the BotometerLite API, we categorized Twitter accounts based on their likelihood of being bots. The results showed that 11,073 accounts (0.23% of total users) exhibited automated behavior, contributing to 4.72% of all mentions. A significant bias was observed in the activity of bots. Their presence was particularly pronounced in disciplines such as Mathematics, Physics, and Space Sciences, with some specialties even exceeding 70% of the tweets. However, these are extreme cases, and the impact of this activity on altmetrics varies by speciality, with minimal influence in Arts & Humanities and Social Sciences. This research emphasizes the importance of distinguishing between specialties and disciplines when using Twitter as an altmetric

Mapping the backbone of the Humanities through the eyes of Wikipedia

The present study aims to establish a valid method by which to apply the theory of co-citations to Wikipedia article references and, subsequently, to map these relationships between scientific papers. This theory, originally applied to scientific literature, will be transferred to the digital environment of collective knowledge generation. To this end, a dataset containing Wikipedia references collected from Altmetric and Scopus’ Journal Metrics journals has been used. The articles have been categorized according to the disciplines and specialties established in the All Science Journal Classification (ASJC). They have also been grouped by journal of publication. A set of articles in the Humanities, comprising 25 555 Wikipedia articles with 41 655 references to 32 245 resources, has been selected. Finally, a descriptive statistical study has been conducted and co-citations have been mapped using networks and indicators of degree and betweenness centralit

Tweet my paper: Who handles dissemination on Twitter?

The communication of research results is a task that is not equally distributed among authors. This paper explores how researchers distribute dissemination tasks on Twitter, the main channel for scientific communication. The main goal is to determinate which authorship position is most associated with self-dissemination of papers on Twitter, and whether this pattern is homogeneous across research areas. For Twitter mentions to papers, a large-scale dataset was created by merging Web of Science and Altmetric.com data, while for the identification of scholars on Twitter, an open dataset was used. Our main finding shows that 27% of Twitter users who mention papers are scholars and that only 13% of their mentions were for self-promotion purposes. Likewise, the corresponding author is the main responsible for this dissemination, a role that is mainly carried out by the first author.Full paper available at: https://dapp.orvium.io/deposits/64de4a4995b02025f51d327d/vie

Identifying and characterizing social media communities: a socio‑semantic network approach to altmetrics

Funding for open access charge: Universidad de Granada/CBUA. This work has funded by the Spanish Ministry of Science and Innovation grant number PID2019-109127RB-I00/SRA/10.13039/501100011033. Wenceslao Arroyo-Machado has an FPU Grant (FPU18/05835) from the Spanish Ministry of Universities. Daniel Torres-Salinas is supported by the Reincorporation Programme for Young Researchers from the University of Granada. Nicolas Robinson-Garcia is funded by a Ramon y Cajal grant from the Spanish Ministry of Science and Innovation (REF: RYC2019-027886-I).Altmetric indicators allow exploring and profiling individuals who discuss and share scientific literature in social media. But it is still a challenge to identify and characterize communities based on the research topics in which they are interested as social and geographic proximity also influence interactions. This paper proposes a new method which profiles social media users based on their interest on research topics using altmetric data. Social media users are clustered based on the topics related to the research publications they share in social media. This allows removing linkages which respond to social or personal proximity and identifying disconnected users who may have similar research interests. We test this method for users tweeting publications from the fields of Information Science & Library Science, and Microbiology. We conclude by discussing the potential application of this method and how it can assist information professionals, policy managers and academics to understand and identify the main actors discussing research literature in social media.Spanish Government PID2019-109127RB-I00/SRA/10.13039/501100011033Spanish Ministry of Universities FPU18/05835Ramon y Cajal grant from the Spanish Ministry of Science and Innovation REF: RYC2019-027886-IUniversity of GranadaUniversidad de Granada/CBU

Mapping social media attention in Microbiology: Identifying main topics and actors

This paper aims to map and identify topics of interest within the field of Microbiology and identify the main sources driving such attention. We combine data from Web of Science and Altmetric.com, a platform which retrieves mentions to scientific literature from social media and other non-academic communication outlets. We focus on the dissemination of microbial publications in Twitter, news media and policy briefs. A two-mode network of social accounts shows distinctive areas of activity. We identify a cluster of papers mentioned solely by regional news media. A central area of the network is formed by papers discussed by the three outlets. A large portion of the network is driven by Twitter activity. When analyzing top actors contributing to such network, we observe that more than half of the Twitter accounts are bots, mentioning 32% of the documents in our dataset. Within news media outlets, there is a predominance of popular science outlets. With regard to policy briefs, both international and national bodies are represented. Finally, our topic analysis shows that the thematic focus of papers mentioned varies by outlet. While news media cover the wider range of topics, policy briefs are focused on translational medicine, and bacterial outbreaks