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Exploring new or emerging research domains or subdomains can become overwhelming due to the magnitude of available resources and the high speed at which articles are published. As such, a tool that curates the information and underlines central entities, both authors and articles from a given research context, is highly desirable. Starting from the articles of the International Conference of Learning Analytics & Knowledge (LAK) in its first decade, this paper proposes a novel method grounded in Cohesion Network Analysis (CNA) to analyze subcommunities of authors based on the semantic similarities between authors and papers, and estimate their global impact. Paper abstracts are represented as embeddings using a fine-tuned SciBERT language model, alongside a custom trained LSA model. The extrapolation between the local LAK community to a worldwide importance was also underlined by the comparison between the rankings obtained from our method and statistics from ResearchGate. The accuracies for binary classifications in terms of high/low impact predictions were around 70% for authors, and around 80% for articles. Our method can guide researchers by providing valuable information on the interactions between the members of a knowledge community and by highlighting central local authors who may potentially have a high global impact.

The use of computational systems for enhancing collaborative learning is widely spread. While interest in the application of Computer-Supported Collaborative Learning (CSCL) systems in cybersecurity exists, endeavors in this sense are difficult to compare due to different choices of methods and tools. In this sense, we advance a typology of CSCL systems in cybersecurity and introduce an automated system, Yggdrasil, which identifies emerging vulnerability threats from tweets containing references to explicit news articles. Such a system can be used to enhance collaborative learning processes of experts and non-experts in cybersecurity regarding the early detection of vulnerabilities, by automatically extracting information from online communities. For this purpose, we built a corpus of 650 annotated tweets that contain links to cybersecurity related articles, posted by key profiles in the cybersecurity landscape. Using insights from state-of-the-art approaches in early detection of vulnerabilities from tweets and sentiment analysis from Twitter, we conducted two experiments for the development of Yggdrasil. Our results argue that the use of the BERT language model is an adequate approach for identifying relevant posts or news articles, having an accuracy over 91% in various configurations. Furthermore, we also illustrate the use of transfer learning as a viable alternative for the early detection of cybernetic vulnerabilities.

Dialogism is a philosophical theory centered on the idea that life involves a dialogue among multiple voices in a continuous exchange and interaction. Considering human language, different ideas or points of view take the form of voices, which spread throughout any discourse and influence it. From a computational point of view, voices can be operationlized as semantic chains that contain related words. This study introduces and evaluates a novel method of identifying semantic chains using BERT, a state-of-the-art language model for computational linguistics. The resulting model generalizes to multiple relations including repetitions, semantically related concepts from WordNet (i.e., synonyms, hypernyms, hyponyms, and siblings), as well as pronominal resolutions. By combining the attention scores between words, word pairs are merged into connected components that denote emerging voices from the discourse. The introduced visualization argues for a more dense capturing of inner semantic links between words and even compound words in contrast to classical methods of building lexical chains.