Cyabra Exposes Equal-Opportunity Astroturfing in 2026 Midterms

NEW YORK, NY – October 28, 2026

The battlefield for the 2026 U.S. Midterm Elections is not merely unfolding in swing-state diners, televised debates, or traditional campaign rallies. It is being fought in the algorithmic trenches of our most popular social media platforms, where the line between authentic voter sentiment and coordinated machine manipulation has become alarmingly blurred.

Today, Cyabra, Inc. (Nasdaq: CYAB), an artificial intelligence-powered narrative intelligence company, released its 2026 U.S. Midterm Report. The comprehensive analysis provides an evidence-based, open-source intelligence assessment of inauthentic activity across six of the most fiercely contested races in the country. By analyzing approximately 274,000 posts and comments across X, TikTok, Facebook, and Instagram between August 31 and October 19, the firm has painted a stark picture of the modern digital campaign trail.

The headline finding is as fascinating as it is concerning: roughly one in nine social media posts about the candidates originated from inauthentic accounts. Yet, unlike the highly partisan influence operations of the past decade, this new wave of digital interference is strikingly nonpartisan. The data reveals that automated bots and coordinated networks are targeting Republican and Democratic candidates at roughly the same rate, signaling a strategic evolution in how influence operations are deployed to shape public perception.

Equal Opportunity Astroturfing

The Cyabra report focused on a critical cross-section of the American political landscape, examining U.S. Senate contests in Kansas, Maine, and Michigan, alongside gubernatorial races in Georgia, Nevada, and Ohio. In five of these six races, the company identified coordinated inauthentic accounts with high confidence. More tellingly, in four of those races, coordinated attacks hit both the Republican and Democratic candidates simultaneously.

This phenomenon represents a tactical shift in information warfare. Historically, digital interference was largely viewed through the lens of partisan advantage, with foreign or domestic actors seeking to elevate a specific candidate. Today, the objective appears far more systemic.

"The objective is no longer simply to elect a specific candidate, but to fundamentally fracture public consensus and erode trust in the democratic process itself," noted a senior academic researcher specializing in open-source digital forensics. "By flooding both sides of a debate with automated outrage and synthetic grassroots support—a tactic known as astroturfing—these networks create an illusion of extreme polarization that genuine voters eventually internalize."

The impact on direct candidate engagement is profound. Cyabra found that between 20 percent and 33 percent of comments on a candidate’s own X account came from inauthentic profiles. To the average citizen scrolling through a candidate's feed, this coordinated attack looks like a legitimate groundswell of public opinion. Inauthentic accounts, powered by increasingly sophisticated AI language models, can easily pass for real voters, heavily distorting the perceived popularity or unpopularity of specific policy platforms.

The Platform Scorecard: TikTok's Vulnerability

One of the most revealing aspects of Cyabra's analysis is the stark disparity in platform vulnerability. The report serves as a scorecard for how different social media giants are managing the influx of synthetic content, and the results highlight a widening gap between legacy networks and newer, video-first platforms.

TikTok emerged as the epicenter of inauthentic midterm content, with roughly one in four posts about the candidates originating from fake accounts. This was followed by X at one in seven, and Instagram at one in eleven. Conversely, Facebook demonstrated the lowest rate of inauthentic activity, coming in at under one in twenty posts.

This platform divergence is not accidental. Legacy platforms like Facebook have spent the better part of a decade—and billions of dollars—building robust election integrity infrastructure, developing complex moderation algorithms, and bearing the brunt of public and regulatory scrutiny following the 2016 and 2020 election cycles. Their lower rates of inauthentic activity reflect a mature, albeit imperfect, defensive posture.

TikTok, on the other hand, presents a unique challenge for threat detection. The platform’s algorithm is designed for rapid virality, prioritizing engagement and content velocity over social graphing. Furthermore, detecting inauthentic behavior in short-form video requires analyzing complex, multimodal data—video, audio, text overlays, and captions—which is computationally heavier and more difficult than scanning text-based posts. As AI-generated deepfakes and synthetic audio become more accessible, video platforms are increasingly becoming the path of least resistance for coordinated influence operations.

Narrative Intelligence: The New Defense Frontier

As the volume and sophistication of online manipulation grow, the technology required to combat it is evolving from basic "social listening" into a specialized field known as "narrative intelligence." Cyabra’s recognition as a Market Shaper in the inaugural June 2026 Gartner Emerging Market Quadrant for Narrative Intelligence underscores this shift.

For enterprise businesses and government agencies alike, simply counting likes, shares, and mentions is no longer sufficient. Narrative intelligence relies on advanced machine learning algorithms to map behavioral signals, trace network coordination, and identify the hidden actors amplifying specific talking points. It is a transition from passive observation to active threat detection.

“Influence operations are now a weapon system and public trust is being shaped by forces most citizens can’t see,” said Dan Brahmy, Co-Founder and Chief Executive Officer of Cyabra. “We tracked the run-up to Election Day, so the picture is in hand while the cycle is still live, when knowing which activity is inauthentic and which activity is coordinated is most useful to the institutions responding to it. Our standard is the same for every candidate and every party. Evidence first, and conclusions only as far as the evidence goes.”

This approach is rapidly becoming a critical component of national security. Earlier this month, Cyabra formed a National Security Advisory Council, signaling its deep commitment to the U.S. public sector. The company’s methodology is designed to give election agencies early warnings of influence operations, providing confidence-rated findings that allow officials to act before false narratives take root in the public consciousness.

Institutionalizing Digital Trust

The commercial implications of this technology are vast, particularly in the realm of government procurement. On December 9, 2026, Cyabra, in partnership with government IT solutions giant Carahsoft, will host a luncheon for public-sector leaders and election stakeholders. At this event, Cyabra plans to introduce a global protocol for narrative open-source intelligence collection and analysis in elections.

This protocol, drawn from the company's analysis of national elections across five countries, aims to standardize how online political conversation is collected, how coordinated activity is identified, and how findings are reported to authorities. By partnering with a major federal contractor like Carahsoft, Cyabra is positioning its AI-driven platform as essential infrastructure for state and local election boards going forward.

“Agencies need findings they can defend under scrutiny and brief up the chain,” said Keith E. Noble, Ph.D., VP, Public Sector North America, at Cyabra. “This report shows what that looks like in a live election. On December 9, 2026, we’ll introduce a global protocol, drawn from elections in five countries, that agencies can apply to their own elections.”

As the 2026 midterms draw to a close, the data provided by narrative intelligence firms makes one thing abundantly clear: the arms race between inauthentic actors and AI detection systems is only accelerating. For business leaders, technologists, and government officials, understanding the mechanics of these digital influence operations is no longer optional. The ability to separate authentic human engagement from coordinated machine manipulation will be the defining technological challenge for safeguarding digital trust in the years ahead.