I don’t think it will have any negative impact. It’s implied that perhaps this knowledge can be used by another service to build an advertising product that is similarly effective as Trending Topics, hence creating new competition in the online advertising marketplace. The harder problem is figuring out how to get 100,000,000 or more active users, at which point having solid monetization products becomes invaluable. A group of smart people at MIT can dissect personalization algorithms, but there isn’t a similar path to dissecting what it takes to build a product that reaches a huge scale like Twitter’s. So, there are plenty of other businesses that can learn from this and that may inform the algorithms they build for developing personalized product experiences. But unless they can figure out how to grow to a scale that matters, having really sharp personalization algorithms just doesn’t matter very much.
It’s also unlikely that this sort of information is valuable for other companies at comparable scale. From what I’ve experienced, when your product grows to that scale, you can hire the people smart enough to develop such sophisticated systems. So you can’t assume that another company (LinkedIn for example) can all of a sudden have this huge epiphany based on this reverse engineered data. LinkedIn already has a world-class data science team. It’s likely they have come to the same conclusions independently.
If anything, I would argue that this may help Twitter’s advertising chances because Twitter should spend $1M per head in equivalent stock and buy/hire this team to come work at Twitter, where they can then use Twitter’s production data/tools to advance the algorithms even further, yielding more revenue upside for Twitter.
I don’t think it will impact it significantly and certainly won’t undermine Twitter’s promoted trending topic strategy. Being able to predict trending is much different than being able to make one.
We know that trending topics is a function of tweet frequency and freshness (and almost certainly a component of who is tweeting, I’m sure they weight a tweet by someone with 100,000 followers more than a ten minute old account), so the best way to become a trending topic is to have lots of tweets very quickly with a word that wasn’t being tweeted before. For example, hash tag joke set-up tweets, such as #99thingsihateaboutmydog is something that would never be tweeted until someone does, and it takes off. That’s why we see that kind of tweet often hit the leaderboard, and then drop off as people exhaust all the funny possible tweets that can fit in 140 characters minus the hash tag.
It’s also why sports players are often trending. Pablo Sandoval hits a home run? Every Giants fan on Twitter will tweet something about it all at once. It’s very, very hard to build any sort of advertising campaign that can compete with something like that.
The one part where I think it will make a difference is the calculation of “weak” trending topics. On a slow news day, the trending topics will be, maybe, an order of magnitude “weaker” than on a strong news day. For example, a trending topic right now in the United States is “Mitch Lucker,” who just died in a motorcycle accident. He was the frontman for the band “Suicide Silence.” This is a relatively slow day. The election, however, will probably take over the entirety of Twitter trending topics.
Do you think it would be harder to create a trending topic in the United States today, or on election day? Knowing exactly how strong or weak current trending topics are (and being able to predict how strong they will be hours ahead) will give advertisers a chance to time spreading of their story/hashtag/whatever to hit when it’s most likely to go trending.
For example, a chart of this information might look like this:
See how this can be useful? At 8:00pm there are a lot of users on line, but the strength of trending topics is relatively low. An advertiser would want to target their trending topic to hit maximum “strength” at around 8pm to achieve maximum number of eyeballs with the easiest competition.
Of course, as this information spreads, the number of advertisers using this information to try to maximize their own campaigns will grow as well. This will mean the times with weak topic strength will be lower, and the competition for those spots will be higher.
Overall, I don’t think it’s going to be an effective marketing strategy for brands except those with a very high social influence, and even then it’s a matter of whether the tag is good enough to go viral or not. If a brand really wants everyone to see their hash tag or trending topic, they’re still going to pay for a promoted trend.