User review manipulation is a growing problem that skews analytics and consumer decisions. Studies show 25-35% of mobile game reviews exhibit suspicious patterns: identical phrasing, clustered timestamps, or accounts with no other activity. The impact is significant - games with manipulated early reviews see 40% higher initial downloads but 60% worse long-term retention (suggesting quality-gameplay mismatch). Interestingly, the most reliable reviews come from users who played 10-50 hours - not superfans or critics. Sentiment analysis algorithms are improving but still struggle with sarcasm and cultural context. Platform verification helps: Steam's "purchase verified" reviews are 3x more predictive of player retention than unverified ones. What's your approach to filtering review noise in your analytics? Do you weight reviews by playtime or other engagement metrics?