Text Analysis in Scientific Communication

Closes:

Introduction 

Novelty is the base for knowledge advancement, and detecting novelty in scientific literature is a significant issue. Segregating novelty and rhetoric in literature is challenging. Information use measures aim to identify and indicate various concepts in literature, allowing us to explore the novel features and insights embedded in the text.

The existing methods to capture the semantic relationships in scientific literature are sparse representations. The changing landscape of information analysis warrants multi-fold approaches that reinforce the need for an in-depth understanding of newer scientific communication and the need to rethink existing analysis techniques constantly. This emphasis on continuous learning and adaptation is crucial in a constantly evolving field. Text and data analytics are essential tools for science communication. With the development of text-mining techniques and tools, it is possible to investigate the semantic and conceptual relations among the text networks to reflect interconnections.  

The advent of novel information and communication technologies and science assessment tools creates new opportunities to revisit existing problems and propose innovative solutions to battle the challenges mentioned earlier. Many novel approaches are investigated, such as text mining of scientific literature for science evaluation and communication and peer-review-based science metrics. Communication-based performance measures lead society to evolve newer information analysis. With its importance in addressing these challenges, the proposed special issue would underline the significance and relevance of such facets.

Text characterises narratives in the corpus from different contexts, and signal communication nature. Many assessment and performance measures are largely bibliographic data-oriented. Since bibliographic data generate numerical data, researchers tend to use it and confine the assessment measures to such numbers. However, recently, text-based measures have emerged. One example is the shift from co-word to co-phrase analysis.

Text networks reflect changes in communication patterns and communication-based performance analysis. Text can be used as a unit of measure and supplement bibliographic data units. The current literature-based measurement is characterised by bibliographic data and standards, which can be augmented by text data analysis. When such approaches are used, communication in science can be used as a perfect yardstick for performance measures.

List of Topic Areas:

  • Natural Language Analysis in Literature Mining
  • Scientific Text Modeling
  • Semantic Representation in Text Analysis
  • Similarity Measures
  • Text and Data Classification
  • Clustering text trajectories
  • Semantic text, data and knowledge analysis
  • Knowledge flow measurement
  • Emerging technology detection from content

Guest Editor:

Pit Pichappan, Senior Scientist, Digital Information Research Labs, Chennai. India. [email protected]

Grant Lewison, Senior Research Fellow, School of Life Sciences and Medicine, Kings College. London SE1 9RT. UK [email protected] 

Submissions Information:

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Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”. 

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key Deadlines:

Closing date: 1st of December 2024 
Closing date for abstract submission: 15th of November 2024
Email for abstract submissions: Pit Pichappan, Senior Scientist, Digital Information Research Labs, Chennai. India [email protected]