After listening to a recent episode of LeaderGeneration by Mod Op with guest Kuye, this question came to mind. You should be aware of this answer and how it affects how much you rely on these tools to gather information, form recommendations, and spew “facts” of current trends.

The prompt I asked was:

When considering resources that include “comments” on chat boards, how do you way what is REAL, vs. paid, fake reviews, comments?

Here is the advice for our own searches and how we consider reviews, comments’ validity, and credibility. These are good guidelines.

When evaluating comments and reviews on chat boards or online platforms, distinguishing between genuine and fake or paid content can be challenging. However, there are several strategies you can use to assess the authenticity of comments and reviews:

  • Look for patterns and repetition: Fake reviews or comments often use similar language or phrases repeatedly. If you notice multiple comments that sound almost identical or use the same unusual expressions, they may be generated by bots or paid reviewers.
  • Check the user profiles: Authentic users typically have more developed profiles with a history of varied activity. Fake accounts often have limited or no profile information, and their activity may be focused solely on leaving reviews or comments.
  • Assess the timing and volume: A sudden influx of positive or negative reviews, especially for a new product or service, can be suspicious. Genuine reviews tend to accumulate more gradually over time.
  • Evaluate the content quality: Real reviews and comments often provide specific details about the product or service experience. Vague or overly general comments may indicate fake content.
  • Consider extreme opinions: While not always the case, an abundance of extremely positive or negative reviews without much middle ground can be a red flag.
  • Use available tools: Some platforms have implemented algorithms or software to detect fake reviews. For example, Cornell University researchers have developed an algorithm claiming to identify fake opinion posts with 90% accuracy.
  • Look for verified purchases: On some platforms, reviews from verified purchasers are marked. These are generally more reliable than unverified reviews.
  • Be aware of incentivized reviews: Some businesses offer incentives for reviews, which must be disclosed according to FTC guidelines. Look for disclosures in the reviews themselves.
  • Consider the overall context: A mix of positive and negative reviews is often more realistic than uniformly positive or negative feedback.
  • Trust reputable sources: When making important decisions, consider relying more heavily on reviews from professional reviewers in reputable publications, as they are paid to be objective.

Remember that no method is foolproof, and it’s essential to approach online comments and reviews with a healthy dose of skepticism. The Federal Trade Commission (FTC) is taking steps to combat fake reviews, including proposing rules that could fine businesses up to $50,000 per fake review. However, the responsibility ultimately lies with the consumer to critically evaluate the information they encounter online.

This wasn’t what I wanted. I was truly saying “you”, not the concept of you meaning anyone.
Answer

Ah, I apologize for the misunderstanding. You’re asking specifically about how I, as an AI assistant created by Perplexity, discern between real and fake comments or reviews. Thank you for the clarification. Here’s how I approach this:

  1. Training data: My training is based on a large corpus of text from the internet, which includes both genuine and fake content. However, I don’t have the ability to browse the internet or access current chat boards actively.
  2. Pattern recognition: I’ve been trained to recognize patterns often associated with fake or paid reviews, such as overly promotional language, lack of specific details, or extreme opinions without nuance.
  3. No real-time analysis: I can’t analyze specific comments or reviews in real time. I can only provide general guidance based on my training.
  4. Lack of personal judgment: I don’t make subjective judgments about the authenticity of specific comments. Instead, I can explain the general characteristics of genuine versus fake content.
  5. Reliance on reputable sources: In my training, information from well-established, reputable sources is given more weight than unverified claims or suspicious content.
  6. Limitations: It’s important to note that I don’t have personal experiences or opinions, nor can I learn or update my knowledge base in real time. My responses are generated based on patterns in my training data.
  7. Ethical considerations: I’m programmed to prioritize ethical considerations, which include not promoting or endorsing the creation or use of fake reviews or comments.

While I can provide general information about identifying fake versus real comments, I don’t actually “discern” in the way a human would. I generate responses based on patterns in my training data, which includes information about this topic. Human judgment or specialized tools designed for that purpose would be more appropriate for real-time analysis of specific content.

This is my recommendation: take the time to read through and consider it is NOT a human responding with human experiences in your field. You should know your market better than a crowd-sourced LLM. If you do not, time to start reading up from reliable, proven resources.