When the Outrage Isn’t Real: Bots in Your Brand’s Comment Section

In August 2025, Cracker Barrel unveiled a new logo. Within days the online backlash was massive, and on August 26 the company announced it was bringing the old one back. From the outside, it looked like a textbook case of a brand misreading its customers and getting a handslap. A few weeks later, a media-intelligence firm called PeakMetrics published an analysis that complicated the story considerably.

Ignoring bots is a mistake

We all know that managing social media can be a tumultuous process. A massive amount of angry messages suggest upset customers. Sometimes, brands interpret that negativity as real feedback, and change directions to appease their fans. But if the negativity is engineered by bots, then you are misinterpreting the signal in the wash of noise.

Other times, brands choose to ignore the wave of negativity, thinking It’s just a bot campaign. When you dig deeper and find out that it was indeed bots, it’s easy to just dismiss the entire campaign as “it’s just bots, ignore it.” That’s a convenient narrative, with some confirmation bias, because it absolves anyone of having made a misjudgment.

But neither of these approaches actually aids in making a sound decision, because bots are nearly always involved in any significant online reaction at this point. Instead, the crucial question is to decipher what genuine individuals are trying to tell you, and, just as importantly, how many real people were involved. A bot score can address the former, but it can’t determine the latter.

Bots are very active on social media

Cracker Barrel’s situation captured attention because of its scale, but bots impact social media, every day, sometimes quietly and not some big crisis. Respondology, a company specializing in comment moderation, analyzed 168.8 million comments across 7.6 million posts from roughly 3,400 brand and organization accounts during 2025, spanning Facebook, Instagram, TikTok, YouTube, LinkedIn, Threads, and paid ads on Meta and TikTok. Their report, released in April, detailed:

• One in five comments (20%) exhibiting some form of spam, bot activity, or malicious intent.
• Spam and bots accounted for 41.8% of the comments brands chose to hide.
• Paid media was nearly twice as toxic as organic, with a substantial portion requiring hiding, 38.4% on TikTok ads and 34.1% on Meta ads.
• On sports accounts, more than 80% of spam comments led to illegal streaming or ticketing fraud sites.
• Only 3.5% of all comments received a response from the brands themselves.

Perhaps the most important figure for anyone managing a brand’s reputation is this: 47% of consumers associate negative comments with the brand itself. The automated post linking to a deceptive site might seem like a minor annoyance, but the problem is that have your audience interprets the bad site as an extension of brand.

The broader picture of web traffic echoes these concerns. Imperva’s 2026 Bad Bot Report showed automated traffic accounted for more than 53% of all web traffic in 2025, an increase from 51% the previous year. The financial services sector experienced 24% of all bot attacks and 46% of account takeover incidents, a sobering statistic for those overseeing social media for a bank, as the same actors often target customer accounts and simultaneously seed comment sections with “contact our support agent” links.

Academic scrutiny reinforces this trend. A recent paper in Public Relations Review (titled, rather aptly, “Somebody’s Poisoned the Water Hole!”) argues that the field requires new principles for gathering social media data, given the widespread contamination of the source material by AI-generated content. When researchers studying your audience question the reliability of the data itself, it’s a clear warning for those acting on it.

What the detection tools are genuinely good at

Bot detection stands out as a genuine success story within this field. PeakMetrics arrived at that 44.5% using software tools, as having humans review 52,000 posts doesn’t work at scale. Machines excel at high-volume, pattern-based work, spotting patterns like templated phrasing, unusual posting times, new accounts acting in concert, and scam links across millions of posts.

Platforms’ filters already catch a great deal of obvious spam before it even reaches you. Many of our clients already utilize their own detection and filtering tools, and we often work within those existing setups. The sensible approach is to let software manage the clearing of straightforward noise at a scale that a human team can’t match.

What the tools can’t decide for you

The core objective, then, is to understanding what the remaining feedback means. A detection tool might flag 44.5% of interactions as automated, yet it can’t reveal whether the remaining 55.5% holds significance. It can’t assess if those genuine customers in the discussion represent a small, vocal group or the start of something substantial, nor can it guide your response, determine what to say, or even confirm whether the initial decision that sparked the issue was flawed.

These decisions truly matter for your brand, because your choices on whether to respond or ignore the comments may impact real customers. During a backlash, you may have a strong urge to delete suspected bot comments quickly, but if your criteria for “suspected bot” are too broad (perhaps based on a new account, an angry tone, or wording similar to other posts) you’ll inevitably remove real people, too. Remember, the Cracker Barrel campaign was, in part, seeded by humans, and many genuine customers will repost language they’ve already seen. Deleting those comments is a mistake, which can backfire you, like the kind detailed in sentiment-based suppression we wrote about last week.

How to read a thread like a person

We’ve developed a checklist our team uses, recognizing that no single sign is definitive. Instead, it’s about spotting patterns and thoughtfully considering them all.

The most dependable test we’ve found is the last one listed on the right. Responding to the comment and observing the outcome reveals a lot because bots don’t continue the conversation, but people do.

What to do with what you find

A few practices consistently prove valuable in both crisis situations and regular operations.

• Establish a baseline first. Recognizing an unusual spike requires knowing what’s typical in your social feed. That means tracking comment volume, sentiment mix, and spam rates across each platform. Experienced moderators often hold this knowledge in their heads, a benefit supporting continuity.
• Before you make decisions, analyze the comments. When leaders ask “how bad is it?”The most useful response is, “We’re seeing about X genuine customers expressing Y specific concerns, against a backdrop of significant automated amplification.” That’s a figure that informs a sound decision.
• Consistently remove clearly automated content under a defined policy. Spam floods, phishing redirects, and scam links should be removed consistently, regardless of the sentiment in the surrounding discussion.
• Avoid bulk-deleting comments during a backlash. Any ambiguous comment should remain visible until a person has reviewed it.
• Publicly respond to genuine customers. A specific, human reply within a thread full of automated messages sends the clearest signal, to the customer and everyone reading, that you can distinguish between the two.
• Escalate based on the proportion of genuine customer comments, not the overall volume. An increasing share of comments from actual customers serves as an early warning, even if overall volume is decreasing. I’ll return to the role of human judgment once a situation escalates into a full crisis in a later post.

Where we land

The easy narrative suggests that bots are the issue, and improved bot detection is the answer. Detection certainly matters, and it continues to improve, but the real challenge is about understanding the sentiment of the actual people behind the comments.

That’s what we focus on. Our moderators average over eight years of experience, often tending to the same accounts for years, which allows them to learn the nuances of your community’s voice and recognize when something deviates. We provide coverage in five languages, with native speakers available around the clock, every day, because coordinated campaigns don’t adhere to business hours. And when we tell a client, “this appears largely to be bots, though there are approximately forty real customers with legitimate concerns,” there’s a person on the line who can explain exactly how they reached that conclusion.

If you’re interested in a broader view of where automated and human moderation excel, begin with Human vs. AI Content Moderation. For clarification on what “accurate” truly signifies in AI moderation, see this article. And if you’d like an independent assessment of the content within your comment section, talk to a human here offers that service.

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