Osun Election Exposed a New Era of Disinformation: Manufacturing Doubt, Not Just Lies
The 2026 Osun State governorship election has become a defining case study in how election misinformation in Nigeria is evolving beyond the simple invention of false stories. According to Habeeb Adisa, a fact-checking expert who led FactCheckAfrica’s information-integrity operation during the August 15 poll, the most revealing pattern was a shift toward “manufacturing uncertainty” — taking something real and giving it false context, taking something old and presenting it as new, presenting a faction as an entire political party, and increasingly using artificial intelligence to make fabricated political content look believable. Adisa, in a detailed post-election analysis, argued that Nigeria’s next electoral information challenge will no longer be just about detecting AI-generated content, but about defending the very meaning of evidence itself. The TruthGuard Situation Room, built collaboratively by FactCheckAfrica, BallotEyes observers, and partners, deployed 75 polling-unit observers across all 30 local government areas, supplemented by 30 stationary LGA observers and three roving observers. Online, the operation tracked misinformation, disinformation, manipulated media, suspected AI-generated content, and coordinated attacks, while citizens could forward suspicious claims via WhatsApp. What emerged, Adisa said, was an information environment where the goal was not always to convince voters of one elaborate lie, but to create expectations and sow confusion that could shape the election’s outcome before, during, and after voting.
Before election day, the misinformation landscape was dominated by context manipulation rather than crude fabrication. FactCheckAfrica documented claims about candidate selection, endorsements, rigging, violence, polling locations, BVAS, and security, finding that authentic photographs were attached to false claims, old footage was presented as current, and genuine statements were stripped of context. One of the clearest examples was the claim that APC chieftain Akin Ogunbiyi had endorsed Governor Ademola Adeleke. The photograph was real, but reverse-image searching revealed it was from the 2022 Osun election — the falsehood lay not in the pixels but in the misleading context. Similarly, a real declaration supporting the APC candidate came from a faction of the Accord Party associated with Christopher Imumolen, yet it was presented online as though the entire party had changed its position. FactCheckAfrica rated the claim misleading after examining competing party positions and official records. Another striking case involved a graphic circulating on WhatsApp claiming to show candidates’ scores after an Arise TV town hall; no evidence suggested Arise News or event partners produced it, and it excluded other candidates who participated. Adisa noted that AI is accelerating this problem by lowering the cost of producing convincing political material, but the deeper danger is that “appearance itself becomes evidence” — a realistic screenshot, a convincing voice note, or a professionally designed statement can acquire credibility simply because it looks official.
On election day, the battle shifted from persuasion to confusion, as speed itself became the problem. Claims about polling-unit events, who was leading, and whether incidents had occurred circulated while voting was still underway. A viral claim said Adeleke was already leading in 13 local government areas, while another graphic showed APC candidate Bola Oyabemiji ahead with specific figures. Both claims appeared before voting and collation had concluded, when no official result could establish either candidate as leading. FactCheckAfrica rated the Adeleke claim false at the time it circulated, not because an eventual result could never show such a lead, but because it presented an unofficial picture as an established result. As Adisa explained, “A premature declaration of victory can create expectations among supporters, shape how subsequent developments are interpreted and make later official results appear suspicious if they do not match what people have already seen online.” The Modakeke incident also illustrated the complexity of verification. A video circulating during voting showed security intervention, tear-gassing, and alleged removal of a ballot box. FactCheckAfrica subjected the footage to digital keyframe analysis and confirmed the video was genuine and connected to the election; police also confirmed a tear-gas incident. However, the specific allegation that a ballot box had been snatched could not be independently verified at the time, leading to a “partly true” verdict. This distinction, Adisa stressed, is critical: a genuine video can authenticate an incident without authenticating every claim attached to it. Similarly, the alleged voter-importation incident involving Accord lawmaker Abiola Ibrahim was confirmed in part — police had arrested him — but the specific claim that 146 people were imported and that he confessed remained unverified. The human verification layer, involving reverse-image searches, digital keyframe analysis, official records, and direct contact with authoritative sources, proved essential.
The most interesting patterns emerged after voting, once INEC declared Adeleke the winner. Misinformation changed form, from “Who is winning?” to “Can we trust the result?” FactCheckAfrica documented a claim that the Returning Officer, Professor Joshua Ogunwole, had been arrested and detained in Abuja after declaring Adeleke the winner; this appeared to conflate two facts — that he travelled to Abuja and that someone alleged he had been arrested. The university confirmed he remained free and at work, and no credible evidence of detention was found. Another viral image claimed APC governors held an emergency meeting because of the party’s defeat in Osun, but the photograph was real yet from a July meeting in Kebbi, not a post-election emergency. The appetite for turning individual polling-unit results into broader narratives also surged. One claim that former Governor Olagunsoye Oyinlola lost his polling unit was verified as true, with Accord winning 125 votes to APC’s 85 in Okuku. But a similar claim that former Governor Bisi Akande had been defeated at his polling unit was false: the INEC result showed APC winning there with 181 votes against Accord’s 121. Adisa noted that while polling-unit results are real evidence, their political meaning can become distorted when individual outcomes are transformed into sweeping claims about voter rejection. The case of Alhaja Falilat Yusuf, known as Ero-Arike, revealed a distinctly gendered dimension. A viral claim circulated figures purporting to show Accord defeating APC at her polling location, but the verified result showed APC winning 392 to 54. Worse, the false figures became a vehicle for sexist and humiliating personal attacks targeting her as a woman, demonstrating that misinformation can be used to legitimise personal and gendered narratives.
The clearest post-election example of AI weaponisation was a fabricated image showing Adeleke apparently presenting his Certificate of Return to Peter Obi. FactCheckAfrica identified the image as AI-generated and found no evidence of such an event; what was real was that Obi congratulated Adeleke after the election. Adisa warned that AI can manufacture not only false events but false political relationships — who met whom, who endorsed whom, who helped whom win — and because politics is heavily influenced by symbolism, a photograph can become political evidence even when it proves nothing. Another revealing case involved a supposed ₦15,000 cash transfer from Adeleke to celebrate his victory. The claim was not only false; the link was designed to collect personal information and encourage forwarding to WhatsApp groups. FactCheckAfrica found the domain had been registered only days before the election and described it as a phishing tactic, showing that the information war is also becoming an economic and cyber-security problem. Adisa argued that AI’s greatest electoral power may not be making people believe something completely false, but making them doubt something that is true. This is the “liar’s dividend”: once citizens know that politicians can be deepfaked, a politician can dismiss genuine evidence as AI-generated, a real recording can be called fake, and a legitimate result can be labelled fabricated. The question shifts from “What is true?” to “Can we agree on what counts as evidence?” Adisa emphasised that “seeing is believing” is increasingly unreliable, and verification now requires asking where material came from, who produced it, when, and what corroborates it. He also stressed that WhatsApp deserves much more attention, as the most consequential misinformation may be a voice note forwarded through wards, churches, mosques, or community groups, and comprehensive visibility into such closed environments remains a major operational gap.
Osun should be treated as a warning ahead of Nigeria’s 2027 elections, Adisa said. The information battle is becoming faster, more personalised, and harder to monitor, but the answer cannot simply be building better AI detectors, because detection will always be chasing production. The more sustainable response is to strengthen the entire information ecosystem: local journalists who understand verification, election observers who provide ground truth, institutions that communicate quickly, fact-checkers with forensic capacity, platforms that respond to coordinated manipulation, and citizens who understand that a viral post is a claim, not evidence. The TruthGuard model demonstrated the value of connecting these layers — field observers provided physical context, fact-checkers tested online claims, and citizens fed suspicious material directly into the verification process. However, there is a limit to what fact-checking can do after a lie has already travelled; corrections must reach the same networks that carried the falsehood. During Osun, FactCheckAfrica converted checks into short graphics and used a network of more than 70 trained journalists to distribute verified information beyond its own platforms. Adisa concluded that the future of election integrity will depend not on fact-checkers alone, but on whether Nigeria can build a culture where citizens pause before forwarding, journalists verify before amplifying, political actors resist weaponising falsehood, institutions respond quickly, and technology companies recognise that information manipulation is part of electoral infrastructure. Osun showed that misinformation does not have to change every vote to damage democracy; it can work by creating fear before people vote, confusion while they vote, and doubt after the votes have been counted — and that may be the most sophisticated form of AI-enabled political manipulation yet.