Ensuring the Data-Rich Future of the Social Sciences

By Gary King. 2011

412 citations recorded by the source corpus.

Massive increases in the availability of informative social science data are making dramatic progress possible in analyzing, understanding, and addressing many major societal problems. Yet the same forces pose severe challenges to the scientific infrastructure supporting data sharing, data management, informatics, statistical methodology, and research ethics and policy, and these are collectively holding back progress. I address these changes and challenges and suggest what can be done.

Subfields: General Social Sciences, Information Systems, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In General Social Sciences

In Information Systems

In Sociology and Political Science

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Yet, while the ability to capture large volumes of data brings along important scientiﬁc opportunities (Lazer et al., 2009; King, 2011), size by itself is not enough.”
Long short‐term memory networks for CSI300 volatility prediction with Baidu search volume (2018, background): “The increasing volumes of ’big data’ reflecting various aspects of our everyday activities represent a vital new opportunity for scientists to address fundamental questions about the complex world we inhabit [11, 12, 15].”
Lab And/Or Field? Measuring Personality Processes and Their Social Consequences (2015, background): “…2014; Onnela et al., 2014), conduct only local computation on the mobile device (e.g. computing parameters regarding covariance of focal phenomena, such as content of conversation and liking of conversation partner) and discard all raw data immediately (King, 2011; Miller, 2012; Shilton, 2009).”
The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good (2016, background): “This availability of large-scale data is profoundly changing the world we live in and has led to the emergence of a new discipline called computational social science [45]; finance, economics, marketing, public health, medicine, biology, politics, urban science and journalism, to name a few, have al”
The Science of the Individual (2013, background): “Modeling individuals requires massive amounts of data that a decade ago would have been difficult and expensive to collect (King & King, 2011).”
Social media analytics - Challenges in topic discovery, data collection, and data preparation (2018, background): “” The authors go beyond the detection of topics and also consider the evolvement of trends in their research paper, by proposing a multi-modal social event tracking and evolution framework (mmETM), which is evaluated on Google News data. Finally, Vavliakis et al. (2013) also state that the identific”
Big Data for Development: A Review of Promises and Challenges (2016, positive): “One common distinction is whether or not the tracked data is generated actively or passively, and voluntarily or involuntarily (King, 2011), leading to a 2x2 matrix.”
The Smartphone Psychology Manifesto (2012, background): “…sprinting toward Gate 12 in Heathrow Airport’s Terminal 5, or burping a baby on their back porch— raising new privacy and human subjects issues for institutional review boards (IRBs; Gasson, Kosta, Royer, Meints, & Warwick, 2011; King, 2011; Krontiris, Freiling, & Dimitriou, 2010; Shilton, 2009).”
Investigating the Relations between Twitter Sentiment and Stock Prices (2015, background): “The interaction with technological systems is generating massive datasets that document collective behaviour in a previously unimaginable fashion [1, 2].”