In an illustration of the socio-technical gap, people mostly consider Occupy Wall Street a trending topic, but Twitter’s algorithms mostly do not.
Amid rumors that Twitter is suppressing #occupy tags from trending, Gilad Lotan looked at data on tweets containing occupy-related terms and on occupy-related trending topics since September 25th. In Lotan’s analysis, trending topics require a spike in the rate of activity, rather than a slow and steady increase in volume. #OccupyWallStreet, in Lotan’s example, was never a trending topic in New York where the action started. Instead, it first broke through as a trending topic in Madrid.
Presumably OWS failed to trend in New York because momentum built at a steady rate as the movement grew there. When the protest crossed some threshold of media visibility, the tag was rapidly adopted in other geographic areas. Lotan suggests that Madrid might have been particularly receptive to the story following political protests in Spain.
“It’s not about volume” Lotan points out “or else Justin Bieber would be forever trending” . Topics seem to trend, in part, as a function of rate of increase relative to a baseline.
Similar questions about the failure of wikileaks to trend as strongly as many people expected last year revealed that trending topics may also relate to “the diversity of people and tweets about a term” as Josh Elman wrote in the comments on a post questioning wikileaks trends by Angus Johnston. In addition to representing spikes in activity, Twitter’s trending topics algorithm may attempt to capture a broad range of activity across social networks. Elman added that a topic may not be as “widespread across the twitter userbase” as it seems from the perspective of one person’s social network: “Each of us gets a personalized timeline of things that are relevant to us, so even if we think *everyone* is talking about something, it still may not be widespread enough to be in top trends.”
So “everyone” may not really be everyone.
While the gap between social expectations and automatically generated trending topics can suggest “implications for design,” it can also have social and cultural implications. Who is everyone? What does everyone expect, in which contexts, and why? Which topics and/or tags cross weakly linked social networks, and why?
 Trending topic on Twitter while writing this: EVERYONE LOVES BIEBER.