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) , Souza et al. (2022) , Matos et al. (2023) , Maria et al. (2023) , Biazatti et al. (2024) , Felizardo et al. (2025) , and Miranda et al. (2025) .

birddog

The goal of birddog is sniffing out emergence and trajectories in scientific and patent literature.

Installation

Install the stable version from CRAN:

install.packages("birddog")
library(birddog)

Or the development version from GitHub:

# install.packages("remotes")
remotes::install_github("roneyfraga/birddog")
library(birddog)

Features

Data import

read_openalex() – OpenAlex API or CSV exports
read_wos() – Web of Science BibTeX, RIS, plain-text, tab-delimited

Citation network and community detection

sniff_network() – direct citation or bibliographic coupling networks
sniff_components() – identify connected components
sniff_groups() – community detection (fast greedy, Louvain, Leiden, walktrap, edge betweenness)

Group analysis

sniff_groups_attributes() – group-level summary statistics and horizon plots
sniff_groups_keywords() – keyword frequency per group
sniff_groups_terms() – NLP-based phrase extraction
sniff_groups_hubs() – hub classification (Zi-Pi, Guimera and Amaral 2005)
sniff_groups_cumulative_citations() – per-document citation growth

Indexes

sniff_citations_cycle_time() – measures the pace of change (Kayal 1999)
sniff_entropy() – normalized Shannon entropy for keyword diversity (Shannon 1948; Pielou 1966)

Trajectories

sniff_groups_cumulative() – cumulative clusterization over time
sniff_groups_trajectories() – Jaccard similarity DAG across years
plot_group_trajectories_2d() / plot_group_trajectories_3d() – node-based trajectory plots
detect_main_trajectories() – top-N disjoint paths via dynamic programming
filter_trajectories() – filter and rank detected trajectories
plot_group_trajectories_lines_2d() / plot_group_trajectories_lines_3d() – variable-width line plots

Main path analysis

sniff_key_route() – key-route search (Liu and Lu 2012) with SPC weights

Topic modeling

sniff_groups_stm_prepare() / sniff_groups_stm_run() – structural topic modeling within groups

Vignettes

The vignettes are available online here:

Methodological workflow

Main publications

Miranda et al. (2025) The Landscape of Green and Biohydrogen Technology: A Data-Driven Exploration Using Non-Supervised Methods
Felizardo et al. (2025) Transforming Wastes into Resources: Innovations in Cotton Biorefineries for a Sustainable Future
Biazatti et al. (2024) Soybean biorefinery and technological forecasts based on a bibliometric analysis and network mapping
Maria et al. (2023) Evolution of Green Finance: A Bibliometric Analysis through Complex Networks and Machine Learning
Matos et al. (2023) Building and evaluating prospective scenarios for corn-based biorefineries
Souza et al. (2022) Is entrepreneurship an emerging area of research? A computational response
Souza et al. (2022) Bioenergy research in Brazil: A bibliometric evaluation of the BIOEN Research Program