Text as big data: Develop codes of practice for rigorous computational text analysis in energy social science
暂无分享,去创建一个
[1] B. Sovacool. What Are We Doing Here? Analyzing Fifteen Years of Energy Scholarship and Proposing a Social Science Research Agenda , 2014 .
[2] M. Lahsen,et al. Business storytelling about energy and climate change: The case of Brazil’s ethanol industry , 2017 .
[3] Patrik Svensson,et al. The Landscape of Digital Humanities , 2010, Digit. Humanit. Q..
[4] Chris Reed,et al. Argument Mining: A Survey , 2020, Computational Linguistics.
[5] A. Giarolla,et al. Topic modeling method for analyzing social actor discourses on climate change, energy and food security , 2018, Energy Research & Social Science.
[6] Justin Grimmer,et al. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts , 2013, Political Analysis.
[7] Roger D Peng,et al. Reproducible research and Biostatistics. , 2009, Biostatistics.
[8] Biljana Macura,et al. The role of reporting standards in producing robust literature reviews , 2018, Nature Climate Change.
[9] A. Pentland,et al. Computational Social Science , 2009, Science.
[10] Jing Liao,et al. Did a change in Nature journals’ editorial policy for life sciences research improve reporting? , 2019, BMJ Open Science.
[11] K. Isoaho,et al. A critical review of discursive approaches in energy transitions , 2019, Energy Policy.
[12] R. Peng. Reproducible Research in Computational Science , 2011, Science.
[13] Christopher M. Danforth,et al. Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter , 2011, PloS one.
[14] Ruslan Salakhutdinov,et al. Evaluation methods for topic models , 2009, ICML '09.
[15] Christopher M. Danforth,et al. Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll , 2015, PloS one.
[16] Ralf Krestel,et al. Domain-specific word embeddings for patent classification , 2019, Data Technol. Appl..
[17] Benjamin Hofner,et al. Reproducible research in statistics: A review and guidelines for the Biometrical Journal , 2016, Biometrical journal. Biometrische Zeitschrift.
[18] R. Kitchin,et al. Big Data, new epistemologies and paradigm shifts , 2014, Big Data Soc..
[19] Michael Röder,et al. Exploring the Space of Topic Coherence Measures , 2015, WSDM.
[20] Arho Toikka,et al. A Big Data View of the European Energy Union: Shifting from ‘a Floating Signifier’ to an Active Driver of Decarbonisation? , 2019, Politics and Governance.
[21] Daria Gritsenko,et al. Vodka on ice? Unveiling Russian media perceptions of the Arctic , 2016 .
[22] Nick Obradovich,et al. Rapidly declining remarkability of temperature anomalies may obscure public perception of climate change , 2019, Proceedings of the National Academy of Sciences.
[23] C. Madu,et al. Modeling landscape sustainability in the oil producing Niger delta area of Nigeria , 2019, Energy Policy.
[24] Claudio Cioffi-Revilla,et al. Computational social science , 2010 .
[25] Giovanni Baiocchi,et al. Reproducible research in computational economics: guidelines, integrated approaches, and open source software , 2007 .
[26] L. L. Benites-Lazaro,et al. CSR as a legitimatizing tool in carbon market: Evidence from Latin America’s Clean Development Mechanism , 2017 .
[27] Thomas Jacobs,et al. Topic models meet discourse analysis: a quantitative tool for a qualitative approach , 2019, International Journal of Social Research Methodology.
[28] David M. W. Powers,et al. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.
[29] Rada Mihalcea,et al. Text Mining: A Guidebook for the Social Sciences , 2016 .
[30] Claudio Cioffi-Revilla. Computational Social Science , 2010 .
[31] Yolanda Gil,et al. Enhancing reproducibility for computational methods , 2016, Science.
[32] J. Fowler,et al. Rapid assessment of disaster damage using social media activity , 2016, Science Advances.
[33] Derek Greene,et al. An analysis of the coherence of descriptors in topic modeling , 2015, Expert Syst. Appl..
[34] Chong Wang,et al. Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.
[35] Matt Taddy,et al. Text As Data , 2017, Journal of Economic Literature.
[36] Eric M Prager,et al. Improving transparency and scientific rigor in academic publishing , 2018, Brain and behavior.
[37] M. Hajer,et al. A decade of discourse analysis of environmental politics: Achievements, challenges, perspectives , 2005 .
[38] Fabrizio Sebastiani,et al. Machine learning in automated text categorization , 2001, CSUR.
[39] Benjamin K. Sovacool,et al. Promoting novelty, rigor, and style in energy social science: Towards codes of practice for appropriate methods and research design , 2018, Energy Research & Social Science.
[40] William F. Lamb,et al. Fast growing research on negative emissions , 2017 .
[41] Silke Adam,et al. Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology , 2018 .
[42] Eetu Mäkelä,et al. Topic Modeling and Text Analysis for Qualitative Policy Research , 2019, Policy Studies Journal.
[43] L. Sanderink. Shattered frames in global energy governance: Exploring fragmented interpretations among renewable energy institutions , 2020 .
[44] Nello Cristianini,et al. Content analysis of 150 years of British periodicals , 2017, Proceedings of the National Academy of Sciences.
[45] Petter Törnberg,et al. Muslims in social media discourse: Combining topic modeling and critical discourse analysis , 2016 .
[46] John Unsworth,et al. A Companion to Digital Humanities , 2008 .
[47] A. Saltelli,et al. Ethics of quantification: illumination, obfuscation and performative legitimation , 2020, Palgrave Communications.
[48] Michael Gleicher,et al. Task-Driven Comparison of Topic Models , 2016, IEEE Transactions on Visualization and Computer Graphics.
[49] Ilkka Tuomi. Data is more than knowledge: implications of the reversed knowledge hierarchy for knowledge management and organizational memory , 1999 .
[50] R. Hirschheim. INFORMATION SYSTEMS EPISTEMOLOGY: AN HISTORICAL PERSPECTIVE , 2000 .
[51] G. Miller. Sociology. Social scientists wade into the tweet stream. , 2011, Science.
[52] H. Klüver,et al. Measuring Interest Group Influence Using Quantitative Text Analysis , 2009 .
[53] Christopher Gandrud,et al. Reproducible Research with R and RStudio , 2013 .
[54] Loren Collingwood,et al. Tradeoffs in Accuracy and Efficiency in Supervised Learning Methods , 2012 .
[55] Sebastian Benthall,et al. Philosophy of Computational Social Science , 2016 .
[56] E. Grubert,et al. Villainous or valiant? Depictions of oil and coal in American fiction and nonfiction narratives , 2017 .
[57] Nader Shaikh,et al. A checklist is associated with increased quality of reporting preclinical biomedical research: A systematic review , 2017, PloS one.
[58] Matthew L. Jockers,et al. Text‐Mining the Humanities , 2015 .
[59] John P. A. Ioannidis,et al. What does research reproducibility mean? , 2016, Science Translational Medicine.
[60] Andrew McCallum,et al. Optimizing Semantic Coherence in Topic Models , 2011, EMNLP.
[61] Ronald N. Giere,et al. ESP and Psychokinesis: A Philosophical Examination , 1980 .
[62] Margaret E. Roberts,et al. A Model of Text for Experimentation in the Social Sciences , 2016 .
[63] David Mimno,et al. Evaluating the Stability of Embedding-based Word Similarities , 2018, TACL.
[64] Manuel W. Bickel. Reflecting trends in the academic landscape of sustainable energy using probabilistic topic modeling , 2019 .
[65] Scott A. Golder,et al. Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures , 2011 .
[66] Abraham S. D. Tidwell,et al. Energy ideals, visions, narratives, and rhetoric: Examining sociotechnical imaginaries theory and methodology in energy research , 2018 .
[67] Anisa Rowhani-Farid,et al. Badges for sharing data and code at Biostatistics: an observational study , 2018, F1000Research.
[68] Hans Ekkehard Plesser,et al. Reproducibility vs. Replicability: A Brief History of a Confused Terminology , 2018, Front. Neuroinform..