LinkedIn's initial approach was to identify and establish a set of words and phrases known as blocklist. When an account contained any of these inappropriate words or phrases, it was marked fraudulent and removed from LinkedIn. However, this approach includes a few drawbacks, such as:
- Scalability because it is a fundamentally manual process and needs to be taken care of while evaluating words or phrases.
- Words with both appropriate and inappropriate contexts.
- Tracking performance on a phrase-by-phrase basis takes a significant amount of time as well as engineering efforts.
To mitigate such challenges and improve the overall performance, the ML team at LinkedIn decided to change the machine learning approach. The new approach is a machine learning model which is a text classifier trained on public member profile content. To train this classifier, the researchers built a training set consisting of accounts labelled as either “inappropriate” or “appropriate.” The “inappropriate” labels consist of accounts that have been removed from the platform due to inappropriate content.
Read More about how LinkedIn keeps itself professional by detecting and removing inappropriate profiles....
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