BACKGROUND

Documents and videos containing fraudulent content, sometimes known as scam, can frequently be found in printed material, in videos and on the Internet. The fraudulent content can include misleading or untrue statements, pictures or videos that have been edited to include misleading or untrue depictions of events and people and misleading or fraudulent offers of products, vacation trips and other items.

Advertisements, both in printed material and in videos, can also include scam content. However, sometimes it may not be easy to distinguish scam advertisements from reputable advertisements.

SUMMARY

Embodiments of the disclosure are directed to a method implemented on an augmented reality (AR) electronic computing device for determining a likelihood of fraudulent content in an advertisement. The method comprises: using the AR electronic computing device, receiving a digital image of the advertisement; extracting a dataset of information relating to the advertisement from the digital image; identifying content related to the advertisement from the dataset of information; using the identified content, requesting identification information regarding the advertisement; and using the identification information and supplemental information, calculating a scam score for the advertisement, the scam score indicating the likelihood of fraudulent content in the advertisement.

In another aspect, a method implemented on an augmented reality (AR) electronic computing device for identifying fraudulent content in an advertisement comprises: using the AR electronic computing device, receiving a digital image of the advertisement; extracting a dataset of information relating to advertisement from the digital image; identifying actionable content related to the advertisement from the dataset of information; using the actionable content to request identification information regarding the advertisement; receiving the identification information; using the identification information, requesting supplemental information regarding the advertisement; receiving feedback information regarding the advertisement and an advertiser associated with the advertisement; using the feedback information, calculating values for coefficients in an equation for a scam score for the advertisement; calculating the scam score for the advertisement, the scam score indicating the likelihood of fraudulent content in the advertisement; and when the scam score is greater than a predetermined threshold, determining that the advertisement has a high likelihood of containing the fraudulent content.

In yet another aspect, an electronic computing device comprises: a processing unit; and system memory, the system memory including instructions which, when executed by the processing unit, cause the electronic computing device to: receive a digital image of an advertisement; extract a dataset of information relating to advertisement from the digital image; identify actionable content related to the advertisement from the dataset of information; use the actionable content to request identification information regarding the advertisement; receive an identifier for the advertisement and an identifier for an advertiser associated with the advertisement; request feedback information regarding the advertisement and the advertiser, the request for the feedback information comprising one or more commands for an application programming interface (API) on a server computer; receive feedback for a plurality of categories regarding the advertisement and the advertiser; analyze feedback received for the plurality of categories; for each item of feedback received, determine a value of a coefficient for an equation for calculating a scam score for the advertisement, the value representing an extent to which the item of feedback received is positive or negative; determine a weighting factor to be used for the feedback in each of the plurality of categories; calculate the scam score for the advertisement comprising: for each of the plurality of categories, calculate a sum of each coefficient value multiplied by the weighting factor; and add each calculated sum to calculate the scam score; and when the scam score is greater than a predetermined threshold, determine that the advertisement has a high likelihood of containing fraudulent content.

The details of one or more techniques are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of these techniques will be apparent from the description, drawings, and claims.

