15,105 research outputs found
Text authorship identified using the dynamics of word co-occurrence networks
The identification of authorship in disputed documents still requires human
expertise, which is now unfeasible for many tasks owing to the large volumes of
text and authors in practical applications. In this study, we introduce a
methodology based on the dynamics of word co-occurrence networks representing
written texts to classify a corpus of 80 texts by 8 authors. The texts were
divided into sections with equal number of linguistic tokens, from which time
series were created for 12 topological metrics. The series were proven to be
stationary (p-value>0.05), which permits to use distribution moments as
learning attributes. With an optimized supervised learning procedure using a
Radial Basis Function Network, 68 out of 80 texts were correctly classified,
i.e. a remarkable 85% author matching success rate. Therefore, fluctuations in
purely dynamic network metrics were found to characterize authorship, thus
opening the way for the description of texts in terms of small evolving
networks. Moreover, the approach introduced allows for comparison of texts with
diverse characteristics in a simple, fast fashion
Mapping the Evolution of "Clusters": A Meta-analysis
This paper presents a meta-analysis of the “cluster literature” contained in scientific journals from 1969 to 2007. Thanks to an original database we study the evolution of a stream of literature which focuses on a research object which is both a theoretical puzzle and an empirical widespread evidence. We identify different growth stages, from take-off to development and maturity. We test the existence of a life-cycle within the authorships and we discover the existence of a substitutability relation between different collaborative behaviours. We study the relationships between a “spatial” and an “industrial” approach within the textual corpus of cluster literature and we show the existence of a “predatory” interaction. We detect the relevance of clustering behaviours in the location of authors working on clusters and in measuring the influence of geographical distance in co-authorship. We measure the extent of a convergence process of the vocabulary of scientists working on clusters.Cluster, Life-Cycle, Cluster Literature, Textual Analysis, Agglomeration, Co-Authorship
Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)
Opinion mining and sentiment analysis has become ubiquitous in our society, with
applications in online searching, computer vision, image understanding, artificial intelligence and
marketing communications (MarCom). Within this context, opinion mining and sentiment analysis
in marketing communications (OMSAMC) has a strong role in the development of the field by
allowing us to understand whether people are satisfied or dissatisfied with our service or product
in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To
the best of our knowledge, there is no science mapping analysis covering the research about opinion
mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science
mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work
during the last two decades in this interdisciplinary area and to show trends that could be the basis
for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer
and InCites based on results from Web of Science (WoS). The results of this analysis show the
evolution of the field, by highlighting the most notable authors, institutions, keywords,
publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La
reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la
Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐
FEDERJA‐148)” and The APC was funded by the same research gran
Drawing Elena Ferrante's Profile. Workshop Proceedings, Padova, 7 September 2017
Elena Ferrante is an internationally acclaimed Italian novelist whose real identity has been kept secret by E/O publishing house for more than 25 years. Owing to her popularity, major Italian and foreign newspapers have long tried to discover her real identity. However, only a few attempts have been made to foster a scientific debate on her work.
In 2016, Arjuna Tuzzi and Michele Cortelazzo led an Italian research team that conducted a preliminary study and collected a well-founded, large corpus of Italian novels comprising 150 works published in the last 30 years by 40 different authors. Moreover, they shared their data with a select group of international experts on authorship attribution, profiling, and analysis of textual data: Maciej Eder and Jan Rybicki (Poland), Patrick Juola (United States), Vittorio Loreto and his research team, Margherita Lalli and Francesca Tria (Italy), George Mikros (Greece), Pierre Ratinaud (France), and Jacques Savoy (Switzerland).
The chapters of this volume report the results of this endeavour that were first presented during the international workshop Drawing Elena Ferrante's Profile in Padua on 7 September 2017 as part of the 3rd IQLA-GIAT Summer School in Quantitative Analysis of Textual Data. The fascinating research findings suggest that Elena Ferrante\u2019s work definitely deserves \u201cmany hands\u201d as well as an extensive effort to understand her distinct writing style and the reasons for her worldwide success
The role of handbooks in knowledge creation and diffusion: A case of science and technology studies
Genre is considered to be an important element in scholarly communication and
in the practice of scientific disciplines. However, scientometric studies have
typically focused on a single genre, the journal article. The goal of this
study is to understand the role that handbooks play in knowledge creation and
diffusion and their relationship with the genre of journal articles,
particularly in highly interdisciplinary and emergent social science and
humanities disciplines. To shed light on these questions we focused on
handbooks and journal articles published over the last four decades belonging
to the research area of Science and Technology Studies (STS), broadly defined.
To get a detailed picture we used the full-text of five handbooks (500,000
words) and a well-defined set of 11,700 STS articles. We confirmed the
methodological split of STS into qualitative and quantitative (scientometric)
approaches. Even when the two traditions explore similar topics (e.g., science
and gender) they approach them from different starting points. The change in
cognitive foci in both handbooks and articles partially reflects the changing
trends in STS research, often driven by technology. Using text similarity
measures we found that, in the case of STS, handbooks play no special role in
either focusing the research efforts or marking their decline. In general, they
do not represent the summaries of research directions that have emerged since
the previous edition of the handbook.Comment: Accepted for publication in Journal of Informetric
Predicting the dynamics of scientific activities: A diffusion-based network analytic methodology
Copyright © 2018 by Association for Information Science and Technology With the rapid explosion of information and the dramatic development of bibliometric techniques in the past decades, it becomes a challenge to comprehensively, extensively, and efficiently understand science maps. Aim-ing to explore in-depth insights from science maps and predict the dynamics of scientific activities, this paper, based on the co-occurrence statistics of terms derived from scientific documents, proposes a diffusion-based network analytic methodology to conduct the prediction study from two aspects: the research interest of scien-tific researchers and the evolutionary directions of scientific topics. A case study on academic articles down-loaded from three leading journals in the field of bibliometrics demonstrates the feasibility of the methodology. The future directions of bibliometrics are identified, such as the application of information technologies to tradi-tional bibliometric data, the interactions between bibliometrics and science, technology, and innovation policy issues, and individual-level bibliometrics. The results also provide recommendations as potential research inter-ests for a set of experts. The proposed method could be a toolkit to conduct forecasting studies for a given technological area or a given discipline, and a recommender system to assist academic researchers in identify-ing potential research interests and extended areas
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