Social media for large studies of behavior

By Derek Ruths, Jürgen Pfeffer. 2014

634 citations recorded by the source corpus.

Subfields: Epidemiology, Sociology and Political Science, Statistical and Nonlinear Physics

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Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Functional biases make conclusions from research studies diﬃcult to generalize or transfer, as each platform exhibits its own structural diﬀerences (Tufekci, 2014), which can lead to platform-speciﬁc phenomena (Ruths and Pfeﬀer, 2014) that are often overlooked.”
Artificial Intelligence for Mental Health and Mental Illnesses: An Overview (2019, background): “Thus, the data could be misconstrued due to the limited contextual information [103].”
#Snowden: Understanding Biases Introduced by Behavioral Differences of Opinion Groups on Social Media (2016, methodology): “warned that large-scale studies using social media data need to be held to higher methodological standard to account for population bias, biases in individual behaviors and biases driven by platform and algorithm designs [39].”
The Effect of Population and "Structural" Biases on Social Media-based Algorithms: A Case Study in Geolocation Inference Across the Urban-Rural Spectrum (2017, background): “Recently, concerns about population bias in social media have been the subject of much discussion (most notably in Science [55]).”
User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest (2016, background): “Social media platforms have been studied extensively (Kietzmann et al. 2011; Ruths and Pfeffer 2014; Lin and Lu 2011), but whether and how users participate in multiple platforms concurrently has only recently been explored (Malinen 2015).”
Population Bias in Geotagged Tweets (2015, background): “Our paper is answering the general call for stronger methodological investigations about the nature of population representation in social media data (Ruths and Pfeffer 2014; Tufekci 2014), as well as the specific call for combining geographic data from user-generated sources with non-usergenerated ”
The Geography and Importance of Localness in Geotagged Social Media (2016, methodology): “Ruths and Pfeffer [37] recently called for “higher methodological standards” for “large-scale studies of human behavior in social media.”
The Future of Work in the ‘Sharing Economy’. Market Efficiency and Equitable Opportunities or Unfair Precarisation? (2016, neutral): “There is, however, a possible drawback to the ‘ big data opportunity’ represented by the rise of what have been called ‘embedded researchers’ (Ruths & Pfeffer, 2014).”
Tackling Climate Change with Machine Learning (2019, background): “ictures.35 Jiang provides areview ofurban computing for mobile phone traces [302]. Relevant information on the urban space can also be learned from social media activity, like Twitter, as reviewed in [303, 304]. There are, however, numerous obstacles to making sense of this wealth of data (see [305]”
Social media analytics - Challenges in topic discovery, data collection, and data preparation (2018, background): “Issues relating to validity and representativeness have often been discussed and explored (Diaz, Gamon, Hofman, Kiciman, & Rothschild, 2016; Jungherr et al., 2016; Ruths & Pfe ﬀ er, 2014; Tufekci, 2014).”
Identifying Platform Effects in Social Media Data (2016, background): “This concern has thus far only been raised theoretically (Tufekci 2014; Ruths and Pfeffer 2014), and not yet addressed empirically.”
Tampering with Twitter’s Sample API (2018, background): “Bias by design results from built-in characteristics of the platform [11].”