Look, it's not that the jet doesn't look pretty sweet flying over Mount Damavand, it's just that if the majority world opinion is that your country is still using Russian war technology because you can't build working models on your own, an easily-discovered photoshop of your plane... you know... actually flying probably isn't going to impress anyone. Put some effort into it, guys. At least figure out a way to alter the cloud formations, so they aren't identical.

Google slashed the cumulative view counts on YouTube channels belonging to Universal Music Group, Sony/BMG, and RCA Records by more than 2 billion views Tuesday, a drastic winter cleanup that may be aimed at shutting down black hat view count-building techniques employed by a community of rogue view count manipulators on the video-sharing site.

Universal's channel is the one that took the biggest hit. According to figures compiled by the YouTube statistics analysts at SocialBlade, the record company's YouTube channel lost more than 1 billion views from its preexisting tally of 7 billion views Tuesday.

The answer comes in the second way that YouTube changed its view count. The company recently decided to remove view counts for videos that are no longer live on the channel, or so-called "dead videos." For Universal and Sony, that meant thousands of music videos that over the past three years slowly have migrated to the VEVO channel, which is jointly owned by the two companies. A senior label executive confirmed the migration....

That meant high-profile videos that once lived separately on the Universal and Sony YouTube channels have been relocated to Vevo. As a result, the views that those videos received during their time on the dedicated label channels were taken away in YouTube's latest "clean up" effort.

In other words, those views happened; they weren't "faked" or even double counted when they went on to Vevo. But because the videos are no longer on the channel, YouTube considers them "dead videos." They still live on in YouTube, just under a different channel.

To solicit gold-standard deceptive opinion spam using AMT, we create a pool of 400 Human- Intelligence Tasks (HITs) and allocate them evenly across our 20 chosen hotels. To ensure that opinions are written by unique authors, we allow only a single submission per Turker. We also restrict our task to Turkers who are located in the United States, and who maintain an approval rating of at least 90%. Turkers are allowed a maximum of 30 minutes to work on the HIT, and are paid one US dollar for an accepted submission.

Each HIT presents the Turker with the name and website of a hotel. The HIT instructions ask the Turker to assume that they work for the hotel’s marketing department, and to pretend that their boss wants them to write a fake review (as if they were a customer) to be posted on a travel review website; additionally, the review needs to sound realistic and portray the hotel in a positive light. A disclaimer indicates that any submission found to be of insufficient quality (e.g., written for the wrong hotel, unintelligible, unreasonably short, plagiarized, etc.) will be rejected

For truthful opinions, we mine all 6,977 reviews from the 20 most popular Chicago hotels on TripAdvisor. From these we eliminate:

• 3,130 non-5-star reviews;
• 41 non-English reviews;13
• 75 reviews with fewer than 150 characters since, by construction, deceptive opinions are at least 150 characters long...
• 1,607 reviews written by first-time authors— new users who have not previously posted an opinion on TripAdvisor—since these opinions are more likely to contain opinion spam, which would reduce the integrity of our truthful review data...

Finally, we balance the number of truthful and deceptive opinions by selecting 400 of the remaining 2,124 truthful reviews, such that the document lengths of the selected truthful reviews are similarly distributed to those of the deceptive reviews. Work by Serrano et al. (2009) suggests that a log-normal distribution is appropriate for modeling document lengths. Thus, for each of the 20 chosen hotels, we select 20 truthful reviews from a log-normal (left-truncated at 150 characters) distribution fit to the lengths of the deceptive reviews.

We observe that truthful opinions tend to include more sensorial and concrete language than deceptive opinions; in particular, truthful opinions are more specific about spatial configurations (e.g., small, bathroom, on, location). This finding is also supported by recent work by Vrij et al. (2009) suggesting that liars have considerable difficultly encoding spatial information into their lies. Accordingly, we observe an increased focus in deceptive opinions on aspects external to the hotel being reviewed (e.g., husband, business, vacation)...

[....]

... we find increased first person singular to be among the largest indicators of deception, which we speculate is due to our deceivers attempting to enhance the credibility of their reviews by emphasizing their own presence in the review.