Predicting Topics of Scientific Papers from Co-Authorship Graphs: a Case Study

In this paper, we present a case study of predicting topics of scientific papers using a co-authorship graph. Co-authorship graphs constitute a specific view on bibliographic data, where scientific publications are modelled as a graph’s nodes, and two nodes are linked by an undirected edge whenever the two corresponding papers share at least one author. We apply a simple collective classification algorithm based on relaxation labelling to the ILPnet2 bibliographic database. The approach is based on the assumption that papers in the same neighbourhood of the co-authorship graph tend to be on the same topics, and that the predicted topic for one node in the graph depends on the actual or predicted topics of the nodes linked to it. We evaluate the performance of this method on the ILPnet2 data in terms of ROC analysis, and explain the results in terms of the co-authorship graph and the position and properties of papers on a certain topic in the graph.

Understanding the Impact of Early Citers on Long-Term Scientific Impact

This paper explores an interesting new dimension to the challenging problem of predicting long-term scientific impact (LTSI) usually measured by the number of citations accumulated by a paper in the long-term. It is well known that early citations (within 1-2 years after publication) acquired by a paper positively affects its LTSI. However, there is no work that investigates if the set of authors who bring in these early citations to a paper also affect its LTSI. In this paper, we demonstrate for the first time, the impact of these authors whom we call early citers (EC) on the LTSI of a paper. Note that this study of the complex dynamics of EC introduces a brand new paradigm in citation behavior analysis. Using a massive computer science bibliographic dataset we identify two distinct categories of EC - we call those authors who have high overall publication/citation count in the dataset as influential and the rest of the authors as non-influential. We investigate three characteristic properties of EC and present an extensive analysis of how each category correlates with LTSI in terms of these properties. In contrast to popular perception, we find that influential EC negatively affects LTSI possibly owing to attention stealing. To motivate this, we present several representative examples from the dataset. A closer inspection of the collaboration network reveals that this stealing effect is more profound if an EC is nearer to the authors of the paper being investigated. As an intuitive use case, we show that incorporating EC properties in the state-of-the-art supervised citation prediction models leads to high performance margins. At the closing, we present an online portal to visualize EC statistics along with the prediction results for a given query paper

Forest Ecosystem Services: An Analysis of Worldwide Research

The relevance of forests to sustain human well-being and the serious threats they face have led to a notable increase of research works on forest ecosystem services during the last few years. This paper analyses the worldwide research dynamics on forest ecosystem services in the period from 1998 to 2017. A bibliometric analysis of 4284 articles was conducted. The results showed that the number of published research articles has especially increased during the last five years. In total, 68.63% of the articles were published in this period. This research line experiences a growing trend superior to the general publishing trend on forest research. In spite of this increase, its relative significance within the forest research is still limited. The most productive subject areas corresponded to Environmental Science, Agricultural and Biological Sciences and Social Sciences Economic topics are understudied. The scientific production is published in a wide range of journals. The three first publishing countries are United States, China and the United Kingdom. The most productive authors are attached to diverse research centres and their contributions are relatively recent. A high level of international cooperation has been observed between countries, institutions and authors. The findings of this study are useful for researchers since they give them an overview of the worldwide research trends on forest ecosystem services

County development and sustainability in China: a systematic scoping of the literature

Despite the importance of research and innovation in facilitating sustainable county development in China, little evidence is available concerning the output and characteristics of that research. This scoping review assesses key features or characteristics of the research output, the extent to which researchers engage with concepts of sustainability and the potential impact of the research. Publications were identified and classified using a process consistent with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA). The R programming packages igraph and wordcloud respectively were used to analyse and graphically depict the strength of authorship networks and keyword frequency. Findings revealed that this field of research is an evolving one with a widely-dispersed network of researchers increasingly using new keywords. The implications of the review findings for improving the value and impact of sustainable county development research are explored

The Open Research Web: A Preview of the Optimal and the Inevitable

The multiple online research impact metrics we are developing will allow the rich new database , the Research Web, to be navigated, analyzed, mined and evaluated in powerful new ways that were not even conceivable in the paper era – nor even in the online era, until the database and the tools became openly accessible for online use by all: by researchers, research institutions, research funders, teachers, students, and even by the general public that funds the research and for whose benefit it is being conducted: Which research is being used most? By whom? Which research is growing most quickly? In what direction? under whose influence? Which research is showing immediate short-term usefulness, which shows delayed, longer term usefulness, and which has sustained long-lasting impact? Which research and researchers are the most authoritative? Whose research is most using this authoritative research, and whose research is the authoritative research using? Which are the best pointers (“hubs”) to the authoritative research? Is there any way to predict what research will have later citation impact (based on its earlier download impact), so junior researchers can be given resources before their work has had a chance to make itself felt through citations? Can research trends and directions be predicted from the online database? Can text content be used to find and compare related research, for influence, overlap, direction? Can a layman, unfamiliar with the specialized content of a field, be guided to the most relevant and important work? These are just a sample of the new online-age questions that the Open Research Web will begin to answer

Designing Explanation Interfaces for Transparency and Beyond

In this work-in-progress paper, we presented a participatory process of designing explanation interfaces for a social recommender system with multiple explanatory goals. We went through four stages to identify the key components of the recommendation model, expert mental model, user mental model, and target mental model. We reported the results of an online survey of current system users (N=14) and a controlled user study with a group of target users (N=15). Based on the findings, we proposed five set of explanation interfaces for five recommendation models (N=25) and discussed the user preference of the interface prototypes

Uncovering Research Trends in Safety Culture in the Global Construction Industry: A Bibliometric Analysis (1995-2020)

Introduction: Safety culture has mainly been used across several safety management literatures to describe the level of safety within workplaces. This paper presents the research landscape and scientific developments on safety culture in the global construction industry. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was employed to identify, screen, and analyze the published documents indexed in the Elsevier Scopus database. Next, the research landscape and scientific developments on the topic were examined by bibliometric analysis (BA) through co-authorship, keywords co-occurrence, and citations. Results: Results showed that 738 documents were published and indexed on the topic between 1995 and 2020. The findings showed that articles are the preferred medium, whereas Engineering is the preferred subject theme for published documents on the topic. The journal of Safety Science (published by Elsevier) is the most influential source of publications on the topic. In contrast, Dongping Fang, based at Tsinghua University (China), is the most influential researcher due to the substantial research grants and financial support from the National Natural Science Foundation. Further analysis showed that the most prolific authors on the topic are based in China, Australia, and Indonesia, although the United States has published the most documents. BA also revealed large networks of researchers and co-occurring keywords and the organizations and countries that currently exist, collaborate, and cite each other works on the topic. Conclusion: The findings indicate that safety culture in the global construction industry has undergone significant scientific developments resulting in high research impact mainly due to its role in preserving the health and safety of workers

Competition and Selection Among Conventions

In many domains, a latent competition among different conventions determines which one will come to dominate. One sees such effects in the success of community jargon, of competing frames in political rhetoric, or of terminology in technical contexts. These effects have become widespread in the online domain, where the data offers the potential to study competition among conventions at a fine-grained level. In analyzing the dynamics of conventions over time, however, even with detailed on-line data, one encounters two significant challenges. First, as conventions evolve, the underlying substance of their meaning tends to change as well; and such substantive changes confound investigations of social effects. Second, the selection of a convention takes place through the complex interactions of individuals within a community, and contention between the users of competing conventions plays a key role in the convention's evolution. Any analysis must take place in the presence of these two issues. In this work we study a setting in which we can cleanly track the competition among conventions. Our analysis is based on the spread of low-level authoring conventions in the eprint arXiv over 24 years: by tracking the spread of macros and other author-defined conventions, we are able to study conventions that vary even as the underlying meaning remains constant. We find that the interaction among co-authors over time plays a crucial role in the selection of them; the distinction between more and less experienced members of the community, and the distinction between conventions with visible versus invisible effects, are both central to the underlying processes. Through our analysis we make predictions at the population level about the ultimate success of different synonymous conventions over time--and at the individual level about the outcome of "fights" between people over convention choices.Comment: To appear in Proceedings of WWW 2017, data at https://github.com/CornellNLP/Macro

A systematic literature review

Albuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.publishersversionpublishe