Can One Tamper with the Sample API?: Toward Neutralizing Bias from Spam and Bot Content
By Fred Morstatter, Harsh Dani, Justin Sampson, Huan Liu. 2016
25 citations recorded by the source corpus.
Subfields: Information Systems, 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 Information Systems
- Kai Shu
- Huan Liu
- Alexandra Olteanu
- Carlos Castillo
- Fred Morstatter
- Jürgen Pfeffer
- Jiliang Tang
- Fernando Diaz
In Sociology and Political Science
- Kai Shu
- Alexandra Olteanu
- Fernando Diaz
- Huan Liu
- Carlos Castillo
- Xinyi Zhou
- Emre Kıcıman
- Suhang Wang
In Statistical and Nonlinear Physics
- Alexandra Olteanu
- Carlos Castillo
- Fernando Diaz
- Kai Shu
- Huan Liu
- Fred Morstatter
- Emre Kıcıman
- Jürgen Pfeffer
Citations in the corpus
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, methodology): “Such accounts use tricks such as hijacking “trending” hashtags of keywords to gain visibility (Thomas et al., 2011), and can (purposefully or accidentaly) distort the statistics of datasets collected from social platforms (Morstatter et al., 2016; Pfeffer et al., 2018).”
- Tampering with Twitter’s Sample API (2018, methodology): “Our work focuses on issues resulting from sampling [42, 43] of Twitter data.”
- Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “eople, which enables the echo chamber eect for the propagation of fake news. Previous approaches for bot detection are based on social network information, crowdsourcing, and discriminative features [23;55;54]. Thus, both spammer and social bots could provide insights about target specic malicious s”