Digital Footprints: Opportunities and Challenges for Online Social Research

By Scott A. Golder, Michael W. Macy. 2014

312 citations recorded by the source corpus.

Subfields: Communication, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8574f92ec4294e85c054ba930f7eada86ffae04d

How researchers see this paper

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

In Communication

1. Jeremy Blackburn
2. Hai Liang
3. Diyi Yang
4. Luke Hutton
5. David M. J. Lazer
6. H. Tristan
7. King-wa Fu

In Statistical and Nonlinear Physics

1. Hai Liang
2. Diyi Yang
3. J. Radford
4. David M. J. Lazer
5. King-wa Fu
6. William Yang Wang
7. Sosuke Kobayashi

Citations in the corpus

• Data ex Machina: Introduction to Big Data (2017, background): “The coming generation will witness a transformation of sociological theory through these improvements in our ability to observe dynamic social systems (boyd & Crawford 2012, Golder & Macy 2014).”
• Youth Perspectives on Critical Data Literacies (2017, background): “Although the sources of these new large datasets vary, many of the most popular accounts of the growing importance of data have focused on “digital footprints” [35, 26], “digital traces” [32], or “digital exhaust” [38]: data created as a side effect of human interaction with computing systems [19].”
• The Pushshift Reddit Dataset (2020, background): “Web data, particularly data from application programming interfaces (APIs), has been an enormous boon for researchers using online social platforms’ databases of user-generated activity and content (Freelon 2014; Golder and Macy 2014; Hampton 2017; Lazer and Radford 2017).”
• Sociology in the Era of Big Data: The Ascent of Forensic Social Science (2015, unknown): “With information expansion to the level of social systems combined with exponential accumulation of data on individual behaviors, there follows increasing concern with data privacy and ethics (see Golder and Macy 2014).”
• Testing Propositions Derived from Twitter Studies: Generalization and Replication in Computational Social Science (2015, background): “Few studies have attempted to summarize or generalize the existing findings [4–6].”
• Can Large Language Models Transform Computational Social Science? (2023, background): “, 2009), with both global and fine-grained observations (Golder and Macy, 2014).”
• Toward Reproducibility in Online Social Network Research (2018, background): “Golders and Macy conduct a wide-ranging survey of OSN research in sociology [14], and outline privacy as a research challenge, but not ethics, and discuss methodology but in the context of training sociologists in methods for”