birddog - Sniffing Emergence and Trajectories in Academic Papers and
Patents
Provides a unified set of methods to detect scientific
emergence and technological trajectories in academic papers and
patents. The package combines citation network analysis with
community detection and attribute extraction, also applying
natural language processing (NLP) and structural topic modeling
(STM) to uncover the contents of research communities. It
implements metrics and visualizations of community
trajectories, including novelty indicators, citation cycle
time, and main path analysis, allowing researchers to map and
interpret the dynamics of emerging knowledge fields.
Applications of the method include: Souza et al. (2022)
<doi:10.1002/bbb.2441>, Souza et al. (2022)
<doi:10.14211/ibjesb.e1742>, Matos et al. (2023)
<doi:10.1007/s43938-023-00036-3>, Maria et al. (2023)
<doi:10.3390/su15020967>, Biazatti et al. (2024)
<doi:10.1016/j.envdev.2024.101074>, Felizardo et al. (2025)
<doi:10.1007/s12649-025-03136-z>, and Miranda et al. (2025)
<doi:10.1016/j.ijhydene.2025.01.089>.
