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📚 Essential Bibliography

Fundamental academic resources for research with AntropoBlue

🔬 Digital Methodology & Online Ethnography

Theoretical Foundations

Hine, C. (2015). Ethnography for the Internet: Embedded, Embodied and Everyday. London: Bloomsbury Academic.
  • Key reference for digital ethnography
  • Methodologies for online research
  • Essential ethical considerations
Pink, S., Horst, H., Postill, J., Hjorth, L., Lewis, T., & Tacchi, J. (2016). Digital Ethnography: Principles and Practice. London: SAGE Publications.
  • Principles of digital ethnography
  • Mixed digital methods
  • Applied case studies
Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London: SAGE Publications.
  • Big Data in social sciences
  • Data infrastructures
  • Methodological implications

🔗 Social Network Analysis

Quantitative Methodology

Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press.
  • Foundational SNA text
  • Mathematical and statistical methods
  • Practical applications
Scott, J. (2017). Social Network Analysis (4th ed.). London: SAGE Publications.
  • Accessible introduction to SNA
  • Updated key concepts
  • Software and tools
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing Social Networks (2nd ed.). London: SAGE Publications.
  • Practical guide to SNA
  • Using specialized software
  • Interpreting results

💻 Computational Methods

Sentiment Analysis & NLP

Liu, B. (2020). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions (2nd ed.). Cambridge: Cambridge University Press.
  • Sentiment analysis techniques
  • Applied machine learning
  • Model evaluation
Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing (3rd ed.). Stanford: Stanford University Press.
  • Natural language processing
  • Modern neural models
  • Social media applications

⚖️ Digital Research Ethics

Association of Internet Researchers (AoIR). Ethical Guidelines.
Franzke, A. S., Bechmann, A., Zimmer, M., Ess, C., & the AoIR Ethics Committee. (2020). Internet Research: Ethical Guidelines 3.0. AoIR.
  • Updated ethical guidance
  • Contemporary considerations
  • Practical cases

📊 Open Science & FAIR Data

Wilkinson, M. D., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.
  • Original FAIR principles
  • Practical implementation
  • International standards
Nosek, B. A., et al. (2015). Promoting an open research culture. Science, 348(6242), 1422-1425.
  • Open research culture
  • Scientific transparency
  • Reproducibility

🔧 Tools & Software

Programming & Analysis

Wickham, H., & Grolemund, G. (2017). R for Data Science. Sebastopol: O'Reilly Media.
  • R for data science
  • Tidyverse ecosystem
  • Visualization with ggplot2
VanderPlas, J. (2016). Python Data Science Handbook. Sebastopol: O'Reilly Media.
  • Python for data analysis
  • Pandas, NumPy, Matplotlib
  • Machine learning with scikit-learn

Qualitative Analysis

Bazeley, P., & Jackson, K. (2013). Qualitative Data Analysis with NVivo (2nd ed.). London: SAGE Publications.
  • Professional use of NVivo
  • Digital qualitative methodology
  • Integration with quantitative data

How to Cite This Bibliography

@misc{antropoblue_bibliography2024, title={Essential Bibliography for AntropoBlue Research}, author={AntropoBlue}, year={2024}, institution={AntropoBlue}, url={https://antropoblue.com/en/bibliography.html} }

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