Fraud evolves continuously. How do companies track influencer fraud trends over time to refine detection models?
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Companies can track influencer fraud trends over time by using a combination of manual checks and automated systems like machine learning algorithms. Here’s a step-by-step approach:
1. Content Analysis: They scrutinize the content posted by influencers, including quality, coherence, interaction rate, and originality. Unnatural spike in followers, low engagement or commonly used hashtags without relevant content may indicate fraudulent activities.
2. Follower Analysis: They assess followers’ profiles for suspicious patterns like an influx of new followers from different geographic locations, followers with no profile picture or posts, or a high ratio of followers to following. Such accounts might be paid or bots.
3. Engagement Analysis: They examine the authenticity of likes, comments, and shares. An unusual engagement-to-follower ratio, repetitive comments, or comments from the same accounts can hint fraud.
4. Machine Learning: Advanced platforms use machine learning to detect patterns that signify fraudulent activities. Essentially, they refine detection models based on patterns available in historical data.
5. Third-party Verification: Some companies use platforms that offer credibility scores to influencers based on a variety of metrics, increasing the chances of partnering with authentic influencers.
For instance, the influencer marketing platform Flinque uses an advanced fraud detection system to identify and eliminate fraudulent influencers. It offers analytics tools to help brands and agencies track and scrutinize influencers’ performance over time, which can aid in identifying irregularities and potential fraud trends.
However, choosing the right platform depends on each team’s requirements, like the scale of campaigns, marketing budget, and specific needs for analytics or audience targeting.
Ultimately, companies should also educate themselves on the latest fraud trends, stay updated with changes in social media platforms’ algorithms and put a robust system in place for regular audits of influencer activity. This will allow for continuous refining of fraud detection models.