Algorithm Measures Human Pecking Order

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By looking at the changes in linguistic style that occur when people make the transition from non-admin to admin roles, Kleinberg and co cleverly show that the pattern of linguistic co-ordination changes too. Admins become less likely to co-ordinate with others. At the same time, lower ranking individuals become more likely to co-ordinate with admins.

A similar effect also occurs in the Supreme Court (where power differences are more obvious in any case).

Curiously, people seem entirely unware that they are doing this. “If you are communicating with someone who uses a lot of articles ā€” or prepositions, orpersonal pronouns ā€” then you will tend to increase your usage of these types of words as well, even if you don’t consciously realize it,” say Kleinberg and co.

If you can measure actual influence, there will be an entire industry and market that grows up around trying to hack it and influence it. It’ll amplify the positive feedback loop, where the influential will become even more influential, since we’ll look to the ranking to help us decide who to listen to.

This would also affect social networks, especially those like Linkedin, where ladder climbers and recruiters try to find influential people. I can imagine it would help Facebook do rankings on its news feed.

However, those are just retroactively applying a new technique to existing products. The question we should be asking is, what other types of things does this technology allow?

Off the top of my head, you can provide real-time feedback during a conversation or chat of how you’re being perceived. Advertising would find it interesting on the assumption that more influential people can get others to buy. The other thing you can do is see if the measure of power correlates with virality.

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