Klout Score:Measuring Influence Across Multiple Social NetworksOctober 29, 2015Mining Big Data in Social Networks WorkshopIEEE International Conference on Big Data, Santa Clara

*Adithya Rao, Nemanja Spasojevic, Zhisheng Li, Trevor DSouza

Link to paper: pdf arxiv

● Klout is a social influence measurement tool.

● Users register on Klout.com and connect their social network accounts.

● Klout collects authorized/public information from connected networks.

● Klout derives influence scores and topics for users from collected data.

● Klout recommends:○ content to post○ times when to post.

● Klout Website (klout.com)

What is Klout ?

What is Klout ?

Paper Contributions● Scalable Production System:

○ Full production system○ 750 million public and registered user profiles○ 45 billion interactions from 9 different networks

● Feature Generation: ○ How to generate features that signify influence?○ Over 3600 features generated.

● Hierarchical Scoring: ○ How to combine networks into a single score?

● Validation: ○ Experiments and comparisons that validate effectiveness of the Klout score

Scoring Methodology

Problem Statement

Formal Definition:

For each user u in a network G, let G_u be the subset of the network containing the users who may directly or indirectly interact with u, via a set of reactions R ⊆ A. Then an influence score I(u,T) is a measure of the degree and quantity of reactions that u can induce in G_u over a specified time period T.

In simpler words, an influence score may be defined as the ability of a user to drive actions among other users.

System Overview

Networks and Sources

MentionsLikesCommentsSubscribersWall PostsFriends

RetweetsMentionsList MembershipsFollowersReplies

Facebook Twitter

TitleEducationConnectionsRecommendersComments

LinkedInCheck-in’s and TipsFriends and Mayorship

+K received

Klout

Foursquare

InlinksInlinks to OutlinksPage ImportanceCategory counts

Comments+1’sReshares

Google+

Wikipedia Youtube

Instagram

PostsFollowersLikes and Comments

SubscribersViewsLikes

Scoring

Step 1: Acquire Labeled Ground truth data● 100k labels from human evaluators ● Each network has its own labels

Step 2: Derive Features from interaction graph

● Long Lasting● Dynamic

Step 3: Generate a score per network / community

● Fit a model for the labels with features using Supervised Learning models

● Non negative least squares

Step 4: Hierarchically combine scores● Use heuristics such as graph size to

determine weights

Long Lasting Features

Dynamic Features

● Who: ○ The characteristics of the audience who

reacted to the original post from the user. ● When:

○ The difference between the current time and the time at which the reaction occurred.

● Where: ○ The social network on which the reaction

was performed.● What:

○ The unit of original content or action on which the reaction was performed.

● How: ○ The type of reaction.

HIGHER_(SCORED)-D3-FACEBOOK-POST-COMMENT

All-D3-FACEBOOK-POST-COMMENT

Dynamic Features - Cont.

Hierarchical Combining

Hierarchical Combining - Cont.

● Treat networks as orthogonal vectors since networks are mostly independent.

● Use heuristics such as network size to determine weights.

● Final Klout score is the Euclidean norm of the combined vector.

Key Insights

● Features are log-normalized => Klout scores are on a log scale○ eg. Order of magnitude difference between users scored 50 and 60

● Network models achieve 70-75% F1 scores. ○ Human evaluators do not always agree on influence ordering

● Wikipedia and LinkedIn are important sources for less active, high influence users○ eg. Warren Buffett => low social network activity, high score

● Twitter and Facebook are important sources for long tail users:○ eg. Low scored users with less influential interactions

● Temporal Dependence:○ Combining long lasting and dynamic features allows influence measurement on

different time scales

Validation

Spreading information

● 87k Users targeted with perks, encouraged to post messages● 18k posts created, 394k reactions received● Order of magnitude difference for users with Klout Score 60 vs 30

Comparison - Real world rankings

nDCG = 0.878 nDCG = 0.874

Comparison - Google Trends

Influencers by Topic

Conclusion

● A hierarchical scoring system called the Klout Score and a feature generation framework to capture different dimensions of influential interactions.

● Framework scales to hundreds of millions of users and billions of interactions across 9 social networks.

● Sources like Wikipedia and LinkedIn provide partial signals for real world influence. Temporal dependence is also considered.

● The Klout Score is only a partial representation of the influence of a user.

● However, an extensible system that is able to easily incorporate new sources of information can grow more accurate over time.

Paper Reference

● Klout Score: Measuring Influence Across Multiple Social Networks

Adithya Rao, Nemanja Spasojevic, Zhisheng Li, Trevor DSouza

Arxiv link to paper

Thank you!