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A Networked Take on Influence: what we learn from data

Date post: 15-Jan-2015
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Analyzing data from social interactions on Twitter and Facebook gives us clues as to how people choose to manage their attention. When intelligently predicted, captured and visualized, we witness emerging patterns. Some swear by a certain time of day. Others spend an inordinate amount of time trying to reach certain users deemed "influencers." But its clear that the networked dynamics at play are much more complicated. While there is no simple recipe for predicting information flows in online social spaces, there's plenty of room for optimization.
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A Networked Take on Influence Symposium & Workshop on Measuring Influence on Social Media | #Influence12 Social Media Lab, Dalhousie University, Halifax Gilad Lotan | @gilgul
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A Networked Take on Influence

Symposium & Workshop on Measuring Influence on Social Media | #Influence12 Social Media Lab, Dalhousie University, Halifax

Gilad Lotan | @gilgul

Illustra(on:  Ma.  Taylor  

Sculpting Audiences

Alex  Dragulescu  

Sculpting Audiences

Alex  Dragulescu  

purchasing patterns of peers television viewing behavior

Influence: The ability to disproportionately affect the spread of information

Within Context => Wanted Action

“Influence” as an Exposed Metric is Problematic

•  Simple quantifiable measure •  Innate nature •  People aren’t always rational – may trust

someone despite past transgressions •  Exogenous – may come from outside the

observed network •  Based on Context

Flickr:  mangpages  

Receptivity of your Audience

f(Topic, Network, Timing, Authority, Trust, Historical Behavior, Network Density, Activity…)

Given tweet(s) => if/when to post

Diurnal Patterns

Daily Routines

Geographic Distribution

USA  

Indonesia  Germany  

Data Reflects Us

49ers:  36,    Saints:  32  

Identifying Spikes

Game  starts  

Game  ends!  

49ers  

Typical Sports Event

Social  Movement  

TV  Show  

Awards    Ceremony  

Breaking News (zipfian distribution)

Audience & Regional Trends

Meme  

#UglyNames  

#truthbomb  

#IWishIHad  

#ThoughtsWheniLookInTheMirror  

#10ThingsIFindA.rac(ve  

Entertainment  

#TheDarkKnightRises  

JonahHill  

Emma  Stone  Edward  Scissorhands  Foo  Fighters  

Lifestyle  

#WhenIGetDrunk  

#TodayImWearing  #TextMessagesFromAStalker  The  Boy  is  Mine  

#mydivorcesong  

Sports  

Eli  Manning  

#ElClassico  

#GoLakers  

ESPNU  

Giants  Won  

Poli(cs  

Obama  

StateOfTheUnion  

World  War  Web  

Arab  Spring  

Michele  Bachmann  

Holidays  or  Date  

Halloween  

Thanksgiving  

9/11  Pi  Day  Happy  Mothers  Day  

Place  or  Loca(on  

Chilean  

Spain  

Staples  Center  

Wembley  

Baghdad  

Trends by Categories

GeoSpan

Persistence  =  A.en(on  

Persistence

Vola(lity  =  sum  of  edit  distance  in  consecu(ve  slots  of  the  trending  topics  list    

Volatility (across cities)

Audience Attention: Real World Events How do audiences of different clients react to real world events?

Networked Audiences

@Pepsi  @AP  @AJEnglish  

Audience Activity

Whitney  Houston’s  Tragic  Death  Announced  

Pepsi  Promo(on  

#Kony2012

Shape of an Audience

Pi.sburgh  

Oklahoma  City  

Noblesville,  IN  

Engelwood,  Dayton,  OH  

Birmingham,  AL  

#IGoToASchool

Miami

Vancouver

New Jersey

Teen Micro-Celebs

Audience Authorities

•  Highly central users often have many followers

•  Some people are exceptions: not that many followers, but very central (e.g. Jezebel)

•  Most valuable users to engage with? Fewer demands on attention (less popular globally), but well-connected within network.

Information Flows

Bridging by Providing Context

•  Deviation from the Norm •  Audience Receptivity •  Network Attributes: shape, centrality, clustering

•  Bridges: generating trust

•  Context Setting

Instead of “Influencer”, think about:

Thank You

Gilad Lotan | @gilgul [email protected]


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