Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites

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Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science (2017, background): “5,25,26 While lack of contextual understanding is often at the center of big data critiques, context is also a central concept for the work of human computer interaction, ubiquitous computing, and data science.”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “7 In other words, it shows that individuals do not randomly self-select when using social media platforms (Hargittai, 2015), with demographic attributes such as age, gender, race, socioeconomic status, and Internet literacy correlating with how likely someone is to use a social platform.”
Critical analysis of Big Data challenges and analytical methods (2017, background): “Extant studies surrounding BD challenges have paid attention to the dif ﬁ culties of understanding the notion of BD (Hargittai, 2015), decision-making of what data are generated and collected (Crawford, 2013), issuesof privacy (Lazer et al.,2009) andethical considerations relevant to mining such…”
From internet access to internet skills: digital inequality among older adults (2019, background): “It is important to acknowledge that collecting these data online likely biased participants toward more experienced and skilled users thereby resulting in conservative estimates of skill variation among the broader older adult population [39].”