knowsis / Novel Twitter Anomalies Pydatalondon2016
Detect novel anomalies on Twitter
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Detecting novel anomalies in Twitter
PyData London 2016
Description
In recent years Twitter has gained importance as a datasource for finance professionals alongside news. However, several challenges appear when identifying relevant information and finding the latest unexpected developments.
The system we built is aimed at informing the user whenever it identifies an unexpected and novel tweet. Anomaly detection is driven by social volume, i.e. not all tweets which deviate from what is expected are anomalies, but a critical number of tweets is required. In addition, we do not want to alert the user about old information.
Repository Content
- PyData London 2016 presentation
- Jupyter notebooks with example anomaly and novelty detection code
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