Social Media Account Resolution and Verification
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
In this SAND report, we discuss various authorship attribution techniques for social media platforms to identify separate accounts controlled by one user. We look at three main categories of techniques: stylometric, semantic, and temporal, and we test several algorithms in each category. We then combine these techniques to increase predictive power, and perform cross-platform account verification on Twitter and Stack Exchange datasets. Additionally, we briefly examine graphical techniques, and analyze the predictive power of unsupervised approaches. Finally, we apply our techniques to unbalanced data sets. Our experiments reveal that normalized compression distance shows great promise in analyzing social media.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1494166
- Report Number(s):
- SAND-2017-10473; 672176
- Country of Publication:
- United States
- Language:
- English
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