#Snowden: Understanding Biases Introduced by Behavioral Differences of Opinion Groups on Social Media
By Q. Liao, Wai-Tat Fu, M. Strohmaier. 2016
18 citations recorded by the source corpus.
Subfields: Communication, 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 Statistical and Nonlinear Physics
- Saptarshi Ghosh
- Krishna P. Gummadi
- Muhammad Bilal Zafar
- Neil Zhenqiang Gong
- Jure Leskovec
- Prateek Mittal
Citations in the corpus
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, neutral): “g, certain ideological or opinion groups (Liao et al., 2016).”
- Debiasing Community Detection: The Importance of Lowly Connected Nodes (2019, neutral): “detection approaches with a special focus on those that we use as baselines in this work. There is a body of work surrounding different types of biases in social networks, such as the retweet network [7, 13]. However, these studies do not specify the root of the existing biases in social networks an”
- Addressing Age-Related Bias in Sentiment Analysis (2018, neutral): “Liao et al. examined the ways Twitter users with minority opinions used design features such as the “retweet” button to amplify their views and introduce bias [50].”
- Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media (2017, background): “Therefore, building on previous studies that have adopted this “black box” view for an algorithmic system while auditing it [4, 9, 14, 15, 20], we characterized the bias of the ranking algorithm in Twitter’s search platform, without knowing its internals.”