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Illustrated by Prof. Chengshan (Frank) LIU
5Vs: Big volume, velocity, variety, veracity, and value.
Honestly, this term has gone out of fashion.
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In our field ”data-driven” and “method-driven”
research works are labelled as “big data” studies.
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Methods that are associated with “big data”
§ Text-mining ( )
§ data-mining ( )
§ automatic content analysis ( )
§ computer-assisted text analysis ( )
§ automatic annotation ( )
§ sentiment analysis ( )
§ geographic information system ( )
§ network analysis ( )
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• 2010. “A Method of Automated Nonparametric Content Analysis for Social Science.”
• 2012. “Social Science Research Methods in Internet Time.
• 2014. “Restructuring the Social Sciences: Reflections from Harvard’s Institute for Quantitative Social Science.”
• 2015. “Computer-Assisted Text Analysis for Comparative Politics.”
• 2015. “No! Formal Theory, Causal Inference, and Big Data Are Not Contradictory Trends in Political Science.”
• 2015. “We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together.”
• 2015. “Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites.”
• 2016. “Machine Translation: Mining Text for Social Theory.”
• 2008. “Recognizing Citations in Public Comments.”
• 2008. “Parsing, Semantic Networks, and Political Authority Using Syntactic Analysis to Extract Semantic Relations from Dutch Newspaper Articles.”
• 2008. “Good News or Bad News? Conducting Sentiment Analysis on Dutch Text to Distinguish Between Positive and Negative Relations.”
• 2008. “Media Monitoring by Means of Speech and Language Indexing for Political Analysis.”
• 2012. “Media Coverage in Times of Political Crisis: A Text Mining Approach.”
• 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.”
• 2014. “Echo Chamber or Public Sphere? Predicting Political Orientation and Measuring Political Homophily in Twitter Using Big Data.”
• 2017. “Critical News Reading with Twitter? Exploring Data-mining Practices and their Impact on Societal Discourse.”
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§ 2003. “Extracting Policy Positions from Political Texts Using Words as Data.”
§ 2008. “A Scaling Model for Estimating Time-series Party Positions from Texts.”
§ 2014. “Scaling Politically Meaningful Dimensions Using Texts and Votes.”
§ 2015. “Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook.”
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§ 2013. “How Censorship in China Allows Government Criticism but Silences Collective Expression.”
§ 2013. Media Commercialization & Authoritarian Rule in China.
§ 2017. "How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument."
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§ 2005. “Using Geographic Information Systems to Study Interstate Competition.”
§ 2014. “’Big Data’ in Research on Social Policy.”
§ 2015. “Analyzing Big Data: Social Choice and Measurement.”
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§ 2008. “Automatic Annotation of Semantic Fields for Political Science Research.”
§ 2015. “Uncovering Social Semantics from Textual Traces: A
Theory Driven Approach and Evidence from Public Statements of US Members of Congress.”
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§ 2014. “Political Campaigns and Big Data.”
§ 2017. “The Pulse of the People: Can internet data outdo costly and unreliable polls in predicting election outcomes?”
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§ 2012. “Richardson in the Information Age: Geographic
Information Systems and Spatial Data in International Studies.”
Your epistemological and methodological stances and attitudes toward methods decide how you evaluate (if not distain) “big data”.
Illustrated by Prof. Chengshan (Frank) LIU
“Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.”
~ Wikipedia
§ Gary King
Gary King Gary King Gary King
Institute for Quantitative Social Science, IQSS
http://ppt.cc/Aqutw
• Evaluate public policy
• understand what social posts say
• estimate the causes of death,
• ensure fair legislative redistricting,
• reverse engineer Chinese government’s censorship program,
• forecast elections and international conflict
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à
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Evans & Aceves (2016) “Machine Translation: Mining Text for Social Theory.”
March 2016. Google watched how people use a phone in a van for over an hour at a time. Goal: complete interviewing 500 people.
Holmes, J. (2015). Nonsense: The Power of Not Knowing (First Edition). New York: Crown Publishers.
Lindstrom, M. (2016). Small Data: The Tiny Clues That Uncover Huge Trends. New York City: St. Martin’s Press.
Madsbjerg, C. (2017). Sensemaking: The Power of the Humanities in the Age of the Algorithm. New York, NY: Hachette Books.
fact vs. truth vs.
reality vs.
knowledge
Let’s make our exploration DAMN right.
Factor analysis & Explorative data analysis
Lakatos, Z. (2015). Traditional values and the Inglehart constructs. Public Opinion Quarterly, 79(S1), 291–324.
https://doi.org/10.1093/poq/nfv005
§ F2F Survey:
Taiwan Social Change Survey (tscs) 2013 (n=1,952)
§ CATI Telephone survey 2015 (n=1,100)
§ Web panel 2015-2016 (n=468)
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§ Taiwan Election and Democracy Studies 2016
§ Data Collection Period: 2017.1.17 ~ 4.28
§ N=1,690
§ $$$: > NTD 1,000,000
Illustrated by Prof. Chengshan (Frank) LIU
vs. vs.
VS.
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§ Blackburn, S. (2012). What Do We Really Know? The Big Questions in Philosophy. London: Quercus.
§ Cohen, L. H. (2013). I don’t know: In Praise of Admitting Ignorance.
New York: Riverhead Books.
§ Holmes, J. (2015). Nonsense: The Power of Not Knowing (First Edition). New York: Crown Publishers.
§ Madsbjerg, C. (2017). Sensemaking: The Power of the Humanities in the Age of the Algorithm. New York, NY: Hachette Books.
§ Sesno, F., & Blitzer, W. (2017). Ask More: The Power of Questions to Open Doors, Uncover Solutions, and Spark Change. New York:
AMACOM.
§ Zarkadakis, G. (2016). In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence (1 edition). Pegasus Books.
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Blasius, J., & Greenacre, M. (Eds.). (2014). Visualization and Verbalization of Data. CRC Press.
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Husson, F., Le, S., & Pages, J. (2010). Exploratory Multivariate Analysis by Example Using R (1 edition). CRC Press.
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Pagès, J. (2014). Multiple Factor Analysis by Example Using R (1 edition). Boca Raton: Chapman and Hall/CRC.
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Pasek, J., Jang, S. M., Cobb, C. L., Dennis, J. M., & Disogra, C.
(2014). Can marketing data aid survey research? Examining accuracy and completeness in consumer-file data. Public Opinion Quarterly, 78(4), 889–916.
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