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Answer: Machine learning reduces the reliance on a static keyword list, enabling the detection of spam based on contextual and linguistic patterns., Machine learning algorithms can prioritize posts for human review based on the likelihood of being spam, optimizing reviewer workload.
Machine learning offers the capability to discern complex patterns and nuances in data that surpass the limitations of a static keyword list. It can uncover new spam-related terms or phrases and interpret subtle linguistic variations, enhancing detection accuracy. Additionally, it considers contextual factors and user profiles, leading to more precise spam identification. By prioritizing posts for human review based on the likelihood of being spam, machine learning optimizes the workload on human reviewers, making the process more efficient and scalable.
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In the context of enhancing an anti-spam service for a global social media platform, which currently uses a static list of 200,000 keywords to flag potential spam posts, the company is considering integrating machine learning to improve the accuracy and efficiency of spam detection. The platform processes millions of posts daily, requiring a solution that is scalable, cost-effective, and capable of adapting to evolving spam tactics without significantly increasing the workload on human reviewers. Given these constraints, what are the two primary advantages of integrating machine learning into the spam detection process? (Choose two.)
A
The system can dynamically update and expand its keyword list without manual intervention, adapting to new spam tactics.
B
Machine learning reduces the reliance on a static keyword list, enabling the detection of spam based on contextual and linguistic patterns.
C
The speed of comparing posts against the keyword list is exponentially increased, reducing processing time.
D
Machine learning algorithms can prioritize posts for human review based on the likelihood of being spam, optimizing reviewer workload.
E
The system can utilize a more extensive list of keywords to detect spam posts, beyond the original 200,000.