Make media quality
part of the plan.

Ad fraud is the practice of using deceptive or invalid methods to generate advertising activity that appears legitimate but does not represent genuine user engagement.
Common examples include fake clicks, automated bot traffic, fraudulent impressions, click farms, and other forms of invalid traffic. Ad fraud can cause advertisers to waste their budgets, distort campaign performance data, and make it harder to measure genuine audience engagement.
Solutions such as IAS (Integral Ad Science) help advertisers identify and reduce exposure to invalid and fraudulent advertising activity.

IAS (Integral Ad Science) detects ad fraud using a multi-layered approach that combines rules-based detection, artificial intelligence and machine learning, behavioral and network analysis, and browser/device signals.
IAS analyzes large volumes of advertising activity to identify unusual patterns associated with bots and other invalid traffic. Its Threat Lab also investigates emerging fraud schemes using techniques such as malware analysis and reverse engineering.
IAS can detect fraud at multiple stages, including before an ad is served, before the ad renders, and after the ad is displayed, helping advertisers identify and avoid invalid traffic.

IAS (Integral Ad Science) helps advertisers detect and reduce several forms of advertising fraud and invalid traffic, including:
Bot and automated traffic — activity generated by bots rather than genuine users.
Fake clicks and impressions — artificial activity designed to inflate advertising metrics.
Click farms — coordinated human activity generating fraudulent engagement.
Data-center and proxy traffic — traffic patterns associated with non-genuine or manipulated activity.
Sophisticated invalid traffic (SIVT) — more complex fraud schemes designed to imitate legitimate users and advertising activity.
Malware-related and emerging fraud schemes — fraudulent techniques identified through IAS research and threat intelligence.
IAS uses a combination of technology, machine learning, behavioral analysis, and threat intelligence to identify invalid activity and help advertisers reduce wasted ad spend.

Brand safety is the practice of protecting a brand from having its advertisements appear next to content that could negatively affect its reputation or conflict with its values.
For example, advertisers may want to avoid having their ads displayed alongside content involving hate speech, violence, adult content, illegal activities, or other inappropriate or harmful topics.
IAS (Integral Ad Science) helps advertisers identify potentially unsafe content and manage where their digital ads appear, allowing brands to make more informed decisions about their advertising placements.

IAS Context Control helps advertisers control the types of content that appear around their ads. It uses machine learning, natural language processing (NLP), semantic analysis, and sentiment and emotion detection to understand the meaning and context of a webpage—not just individual keywords or the URL.
Advertisers can then create customized suitability settings and use 100+ contextual and industry-specific segments to avoid content that does not fit their brand guidelines. IAS also supports pre-bid filtering and post-bid blocking, helping prevent ads from appearing in unsuitable environments.
In simple terms, Context Control helps brands decide where their ads should and should not appear based on the actual context of the content.

IAS pre-bid segments are targeting and avoidance tools that help advertisers evaluate advertising inventory before they place a bid. They use IAS data and predictive technology to help buyers select impressions that meet their campaign requirements.
Depending on the campaign, IAS pre-bid segments can help advertisers:
Avoid fraudulent or invalid traffic
Filter out unsafe or unsuitable content
Target contextually relevant content
Improve the likelihood that ads are viewable
Apply specific brand safety and suitability settings before purchasing an impression.
In simple terms, pre-bid segments help advertisers decide which ad opportunities to bid on before spending their budget.

Yes. IAS (Integral Ad Science) provides measurement and verification solutions for advertising on major social media platforms.
IAS can help advertisers measure areas such as brand safety and suitability, viewability, invalid traffic (ad fraud), and media quality across supported social platforms. Availability and measurement capabilities can vary by platform, market, and advertising format.
This allows advertisers to better understand the quality and safety of their social media advertising campaigns.

IAS (Integral Ad Science) helps optimize digital advertising campaigns by providing measurement and insights that advertisers can use to improve media quality and campaign performance.
IAS can help advertisers:
Reduce exposure to invalid traffic and ad fraud
Improve brand safety and suitability
Identify opportunities to increase ad viewability
Analyze performance across different publishers, platforms, formats, and environments
Use measurement data to adjust targeting, placements, and media buying strategies
Apply pre-bid controls to help avoid unsuitable or low-quality inventory before an ad is purchased
By using these insights, advertisers can make more informed decisions about where and how their advertising budget is spent.