“Social Relevance: Toward Understanding

the Impact of the Individual in an

Information Cascade”

April 19th, 2016

Robert Hall, Joshua White, Jeremy Fields

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● Introduction● Information Cascades● Current CGUD Modeling

● Representation using FOAF● Limitations

● Introduce FGUD● Posts and Inferencing ● Actors

● Future Work

Overview

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Information Cascades (IC)– Given input (object), a person (subject), engages

(predicate) in the same act• Even when the act is contrary to expected behavior

Information Cascades in Social Networks– The dissemination of content

• Defined as User Diffusion (UD)

Most IC models treat a user as a Node in a graph– Reception of a idea is represented by an activation state

• The probability of activation is a function of connectedness to activated nodes

Introduction

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Epidemiological models have been employed to describe the spread of information in social networks [4]

– If knowledge of key individuals in the network is known then method is more effective

– GGUD (Coarse-Grained User Diffusion) models result

• Knowledge of the group dynamic and a few key individuals

Models

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● CGUD● Utilize a connection graph to describe the existence

of a communication link between users of a social network.

● Information dissemination is modeled as a traversal of the graph emanating from activated users.

● The object of interest within an information cascade: URL mention, a hashtag mention, or an unambiguously resolved named entity, may be represented as well.

CGUD

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CGUD

● CGUD modeling extended with FOAF ● Represents Posts and Nodes

● Our CGUD extensions include● Metadata about posts as nodes● Allows inferring, *foaf:made, *:mentions

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CGUD / FOAF Limits

● FOAF for CGUD ● Missing relationships between individuals

● (Dublin Core) also missing this● You can infer these relationships

● Shared vocabulary and interactions● We represent these as an addon

● Infer relationships between foaf:OnlineAccounts by introducing :communicatesTo

● Not all networks will have reciprocation (Twitter vs. Facebook)

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FGUD

● At some point, CGUD is not enough● To many enhancements with FOAF

● Missing many FOAF descriptors

● We present FGUD (Fine Grain User Diffusion) Modeling● Extends the CGUD Model, introduces semantics for

advanced diffusion concepts● How do “all” actors affect diffusion● Represent certain inferred properties as “static”

communication links

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FGUD Additional Vocabulary

● Additional vocabulary has been included in our FGUD model (Mostly NLP Related)

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FGUD Continued

● Data representation reduction● Rather than keep entire “documents” in the graph

● If a document mentions a concept then the concept is stored for the analysis, the document is then linked

● We can take inferred values to a new level● “Sentiment belongs to a Subject, an Object and a

Community”● We can infer “ownership” of an account, or object

● Even when the “owner” did not create the post

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FGUD Continued

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FGUD Continued

● Reply Concept● Abstract away from the idea that a reply it to a

person● SoundCloud: Reply is a tag on an audio file● Mailing list: Reply is to a list with mention of original

message● Twitter: Reply is directed @ an individual with no

message chain● Reply is an instance of communication

● Inferring a reply is is also possible

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FGUD Continued

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FGUD Continued

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● Influence measurement in FGUD● CGUD didn't allow for inferred sna:communicatesTo● We are working on adding PageRank to measure

actor relationships ● Initialized as an adjacency matrix and then normalized

to form a stochastic matrix● Weights are applied differently to sna:communicatesTo

vs inferred sna:communicatesTo● Weights are applied differently for Actor types

Ongoing Work

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References