Visualising Scientific Topic Evolution

2021 ◽  
Author(s):  
Panagiotis Deligiannis ◽  
Thanasis Vergoulis ◽  
Serafeim Chatzopoulos ◽  
Christos Tryfonopoulos
Keyword(s):  
Topic Evolution ◽  
Scientific Topic

scholarly journals Query‐centric scientific topic evolution extraction

2015 ◽  
Vol 52 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Scott Jensen ◽  
Yingying Yu ◽  
Hans B. Liu ◽  
Xiaozhong Liu
Keyword(s):  
Topic Evolution ◽  
Scientific Topic

scholarly journals Complex-Network Approach for Visualizing and Quantifying the Evolution of a Scientific Topic

2018 ◽  
pp. 106-120
Author(s):  
Olesya Mryglod ◽  
Bertrand Berche ◽  
Yurij Holovatch ◽  
Ralph Kenna
Keyword(s):  
Complex Network ◽  
Nuclear Power ◽  
Network Science ◽  
Subject Area ◽  
Descriptive Statistics ◽  
Complex Network Theory ◽  
Topic Evolution ◽  
History Of ◽  
Scientific Topic ◽  
Scientific Fields

Tracing the evolution of specific topics is a subject area that belongs to the general problem of mapping the structure of scientific knowledge. Often bibliometric databases are used to study the history of scientific topic evolution from its appearance to its extinction or merger with other topics. In this chapter, the authors present an analysis of the academic response to the disaster that occurred in 1986 in Chornobyl (Chernobyl), Ukraine, considered as one of the most devastating nuclear power plant accidents in history. Using a bibliographic database, the distributions of Chornobyl-related papers in different scientific fields are analysed, as are their growth rates and properties of co-authorship networks. Elements of descriptive statistics and tools of complex-network theory are used to highlight interdisciplinary as well as international effects. In particular, tools of complex-network science enable information visualization complemented by further quantitative analysis.

Modeling and Analyzing Topic Evolution

2012 ◽  
Vol 38 (10) ◽  
pp. 1690 ◽  
Author(s):  
Yan-Li HU ◽  
Liang BAI ◽  
Wei-Ming ZHANG
Keyword(s):  
Topic Evolution

Corruption Measurements: Caught Between Conceptualizing the Phenomenon and Promoting New Governance Agenda?

2021 ◽  
pp. 097226292098394
Author(s):  
Kannan Perumal
Keyword(s):  
Data Aggregation ◽  
Policy Advocacy ◽  
New Governance ◽  
Methodological Issues ◽  
Topic Evolution ◽  
Theoretical Perspectives ◽  
Control Framework ◽  
The Subject ◽  
Theoretical Issues ◽  
Insight Into

The work ‘Corruption Measurements: Caught Between Conceptualizing the Phenomenon and Promoting New Governance Agenda?’ is a qualitative study based on reviewing the literature available on the subject. It starts with the introduction that explains the evolution of the idea of measuring corruption, its relevance to governance and associated theoretical issues. The topic, ‘Evolution of Corruption Measurements’ gives an overview about different corruption indices. While the topic ‘Challenges to Corruption Measurements’ briefly introduces the challenges faced by corruption measurements, the topics ‘Conceptualizing Corruption’ and ‘Methodological Issues’ give insight into the contentions faced by corruption measurements from different theoretical perspectives. Also, explained in these sections are how the corruption measurements have conceptualized corruption over the period of three decades; and how do they keep evolving their methods in order to become more relevant in policy advocacy. Issues associated with data aggregation also are explained in-depth in this work. This work demonstrates that though continuous methodological evolution and empirical research have helped corruption measurements to improve their acceptance level, the gap that exist between corruption control framework and practice will remain a challenge to address in future if corruption measurements do not genuinely account the contextual realities.

scholarly journals Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 415
Author(s):  
Jinli Wang ◽  
Yong Fan ◽  
Hui Zhang ◽  
Libo Feng
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Graph Model ◽  
Representation Learning ◽  
Research Direction ◽  
Calculation Model ◽  
Topic Evolution ◽  
Blockchain Technology ◽  
The Status ◽  
Research Hotspots

Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.

Understanding microplastics, and visual art as a method of communication: scientific poster versus painting

2021 ◽  
Author(s):  
Sophie Peters
Keyword(s):  
Human Health ◽  
Visual Art ◽  
Health Problems ◽  
Toxic Chemicals ◽  
Secondary Research ◽  
Effective Form ◽  
Scientific Poster ◽  
Scientific Topic

<p>This multidisciplinary dissertation investigates in detail, visual art as a method of communication, in particular about a scientific topic: microplastics and human health. Primary and secondary research conducted suggest that microplastics have potential to cause health problems in humans due to the leaching of toxic chemicals and that over 8% of an educated western sample had never heard of microplastics before. Over 30% of participants reported that a painting was a more effective form of communication about microplastics and human health than a scientific poster on the same topic, opening areas for further study into the value and process of communication through visual art.</p>

Scientific Topic Highlights Plenary Session

Neurology ◽  
1998 ◽  
Vol 50 (Issue 3, Supplement 2) ◽  
pp. 59-59 ◽  
Keyword(s):  
Plenary Session ◽  
Scientific Topic

scholarly journals A Study of Rumor Detection based on Social Network Topic Models Relationship

2020 ◽  
Author(s):  
Diogo Nolasco ◽  
Jonice Oliveira
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Community ◽  
Topic Model ◽  
Fake News ◽  
Detection Problem ◽  
Model Method ◽  
Show Evidence ◽  
Rumor Detection ◽  
Scientific Topic

The rumor detection problem on social networks has attracted considerable attention in recent years with the rise of concerns about fake news and disinformation. Most previous works focused on detecting rumors by individual messages, classifying whether a post or blog entry is considered a rumor or not. This paper proposes a method for rumor detection on topic-level that identifies whether a social topic related to a scientific topic is a rumor. We propose the use of a topic model method on social and scientific domains and correlate the topics found to detect the most prone to be rumors. Results applied in the Zika epidemic scenario show evidence that the least correlated topics contain a mix of rumors and local community discussions.

scholarly journals The impact of Influencer marketing on consumer buying behavior in social networks

2019 ◽  
Author(s):  
Miglė Černikovaitė
Keyword(s):  
Social Networks ◽  
Research Area ◽  
Buying Behavior ◽  
Shared Information ◽  
The Impact ◽  
Conceptual Research ◽  
Practical Implications ◽  
Scientific Topic ◽  
Customer Satisfaction And Loyalty ◽  
Consumer Buying Behavior

Purpose – the purpose of the article is to analyze the impact effect of Influencers marketing on consumer buying behavior by determining which partnership opportunities are most relevant. Research methodology – the theoretical analysis of scientific literature and quantitative statistical analysis of empirical research results. Findings – the research in Lithuania has shown that before making a decision to purchase a product or a service, most respondents are actively seeking information in social networks by reading other costumers feedback. Moreover, the survey reveals that recommendations, comments, shared information about certain brands by Influencers are the most important factors in changing buying behavior. Research limitations – the main limitations of research may be the geographical research area – Lithuania and social networks (Facebook). Practical implications – understanding of Influencers impact on consumer buying behavior. Originality/Value – this scientific topic is rather new. Scientists, like Matsumura, Yamamoto, & Tomozawa (2008), investigated Influencers and Consumer Insights impact in the Blogosphere; Thakur, Srivastava (2015) presented a Conceptual research model of Influencers impact of Customer Satisfaction and Loyalty and etc. However, there is a lack of research investigating the impact of Influencer marketing on consumer buying behavior. This research aims to fill this gap in the Lithuanian case

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