Representation and communication: challenges in interpreting large social media datasets

By Mattias Rost, Louise Barkhuus, Henriette S. M. Cramer, Barry A. T. Brown. 2013

94 citations recorded by the source corpus.

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

How researchers see this paper

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

In Communication

In Sociology and Political Science

In Statistical and Nonlinear Physics

Citations in the corpus

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “The same mechanism or process may capture diﬀerent signals across contexts (Tufekci, 2014), but such distinctions are often unintelligible and hard to make in an automated fashion when looking at a system output or at an analysis results in aggregate (Rost et al., 2013; Tufekci, 2014).”
Fairness in Machine Learning: A Survey (2020, unknown): “intrinsic biases in the sample will manifest themselves in any model built on the data [42, 33], inappropriate uses of data leading to (un)conscious bias(es) [41, 42], data veracity and quality [297], data relativity and context shifts [42, 233, 114], and subjectivity filters [41].”
Understanding Urban Human Mobility through Crowdsensed Data (2018, background): “Because social media is used for communication purposes and the geographic tags are added by users, the collected data can be highly affected by users’ moods, thoughts, and outside incentives [9].”
There's No Such Thing as the Perfect Map: Quantifying Bias in Spatial Crowd-sourcing Datasets (2015, background): “Few studies exist in this domain, with a notable exception being the recent work by Hetch and Stephens [19]: they examined three of the most common sources of volunteered geographic information (VGI [14]) - that is, Twitter, Flickr, and Foursquare - and showed that all of them are strongly biased to”
Sentiment analysis on evolving social streams: how self-report imbalances can help (2014, background): “In other words, the communicative nature of social media makes social data a side effect of intentional and deliberate communication between users, rather than as a representation of some underlying activity [39, 31].”
A Critical Review of Online Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2018, background): “In addition, we discuss existing solutions aiming at correcting or accounting for biased or innacurate datasets, and existing recommendations for adequately evaluate and methods and observations (including the need for disclaimers and standards [6, 8]), or to interpret results in context [28, 30].”