Retibus socialibus et legibus momenti -- On social networks and the laws of influence
Online Social Networks (OSNs) are a cutting edge topic. Almost everybody --users, marketers, brands, companies, and researchers-- is approaching OSNs to better understand them and take advantage of their benefits. Maybe one of the key concepts underlying OSNs is that of influence which is highly related, although not entirely identical, to those of popularity and centrality. Influence is, according to Merriam-Webster, “the capacity of causing an effect in indirect or intangible ways”. Hence, in the context of OSNs, it has been proposed to analyze the clicks received by promoted URLs in order to check for any positive correlation between the number of visits and different “influence” scores. Such an evaluation methodology is used in this paper to compare a number of those techniques with a new method firstly described here. That new method is a simple and rather elegant solution which tackles with influence in OSNs by applying a physical metaphor.
in·flu·ence /"/the power or capacityof causing an effect inindirect or intangibleways.
in·flu·ence /"/the power or capacityof causing an effect inindirect or intangible Now, how can iways. measure that?
influence ≈ attention ≈ clicks Any measure of influence is necessarily a subjective one. However, in this case [Twitter], a good measure of influence should have a high predictive power on how well the URLs mentioned by the influential users attract attention and propagate in the social network. We would expect the URLs that highly influential users propagate to attract a lot of attention and user clicks. Thus, a viable estimator of attention is the number of times a URL has been accessed. Romero, Galuba, Asur & Huberman, “Influence and Passivity in Social Media,” arXiv:1008.1253v1, 2010.
influence ≈ attention ≈ clicks I’ll rephrase my question…
influence ≈ attention ≈ clicks How can i measure THat?
Correlation 101The peanut butter vendor and the three twitterers (plus a statistician)
A peanut butter vendorwanted to promote his product on Twitter.
He thought of getting some “influentials” tweeting about hispeanut butter including a URL to his website.
new “influence” scorephysical metaphor to compute influencebased on dynamic friction and uniformly accelerated linear motion.
new “influence” scorephysical metaphor to compute influencebased on dynamic friction and uniformly accelerated linear motion. velocity in Twitter = influence acceleration in Twitter = trending (september 2010)
(october 2010)velocity & acceleration model :( independently proposed very few details disclosed :( different approach?
these spikescorrespond to 2009 Tour de France,Armstrong’s comebak
huge isolated spikearound the 4th week of march! relevant event?
good luck get well soonmost frequent phrases in the best wishes anti-doping tweets mentioning hope ok@lancearmstrong around the recovery just luck surgery 4th week of march 2009 broken clavicle
good luckOn March 17, 2009 Lance Armstrong was get well soonrequired by French anti-doping agency to best wishesprovide a hair sample. anti-dopingOn March 23, 2009 Lance Armstrong broke hope okhis collarbone in a crash during a race in recovery justSpain and had to face surgery. luck surgery broken clavicle
just anecdotal evidencebut seems promising :)
just anecdotal evidencebut seems promising :)
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