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A Structural Approach to Community-level Social Influence AnalysisPh.D. VivaVclav Belk

Im going to present how to use structure of social interactions to quantify and explain influence between communities.1Context and Motivation I

Our earlier study suggested communities influence each other2 / 25 topics flow between communitiescommunities may have a position suggesting an important role as a bridging communityWouldnt you want to know whether the community you regularly engage with as a researcher is gaining or loosing influence? My research provides answers to such questions.2Context and Motivation IINetwork represents flow between actors

Actor-level social influence in healthcare, innovations, marketing, etc.

Actors embedded in communities

No suitable model of community-level influence

high in-degree3 / 25network represent flow, e.g. frequent information exchangein-degree: frequently responded actor (e.g. cited) is influentialreply as activity stimulationreply as information flowhigh in-degree: control over flow3Research Problem and QuestionsProblem: measurement, analysis, and explanation of influence between various types of social communities Questions

How can we model influence between communities?

How do we detect communities acting as global authorities/hubs?

Can we exploit the model to maximise information diffusion?

4 / 25HITS cannot be used to address these questions because it is global measure 1 node vs rest4Q1: How can we model influence between communities?

5 / 25Methodology: COIN

HowWhatcentralityactorscommunitiesimpactcommunitiescommunitiesmembershipactorscommunitiesT6 / 25impactsdepends onmethodological core of our model: COmmunity INfluence Hypothesis of cross-comm impactInfluence measured by impactMembership distribution of engagement, core vs restCentrality ~ position: control over flow of resource, high/low Cin-degree: tendency to stimulateInfluence ~ stimulation of responses (citations, replies, etc.) by the core members: high/low JDependence ~ communitys activity is driven by core members of other communities6Impact and Its Aggregates7 / 25impactsdepends oncommunitiescommunitiesrow impact of a community on others column impact of others on a communitydiagonal independenceimportance = total impact of a community on othersdependence = total impact of others on a communityimportance/dependence heterogeneity measured by entropy

independence used to threshold strong impact community influences activity more than the community itself7Experiments8 / 25Influence Over TimeQuestions: Which communities influenced a given community over time? How do we measure that by COIN?

HypothesisFrequent impact higher than independence indicates influence

Experimentssegment data by time windowfind impact higher than independence of influenced community

Discussion fora datalinks represent repliesforum as a proxy of community

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Boards: 10 yearsSAP: 8 years9Personal Issues vs Moderators

Personal Issues influenced first by ModeratorsLater by a specific moderating community, PI Modsemphasised: strong impact10 / 25

Q2: How do we detect communities acting as global authorities/hubs?11 / 25HITS is a node-level measure and cannot be applied1112 / 25importanceimportance entropyglobal authoritieslocal authoritieslowwidespread lowGlobal Authorities: Widespread High Importance12

13 / 25importanceimportance entropyModeratorsModerators: Authority of

1314 / 25dependencedependence entropyhubsdrivenlowwidespread lowGlobal Hubs: Widespread High Dependence14

15 / 25dependencedependence entropyAfter Hours: Hub of

After Hours15

16 / 25SAP Business One: CoreCore: Hub ofdependence entropy

dependenceCOIN integrated to SAP PULSAR16Cross-Community Dynamics in ScienceQuestionsHow can we measure and explain influence between scientific communities?How does the influence relate to communitys performance?How do we adapt COIN?

DataScientists linked by citationsAI communities defined as conferences

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19 years of data17COIN for Scientific Communitiescitations as a proxy of impact and information flow

Aggregate Measuresimportance: how much information flows out of the communityindependence: how introspective the community is18 / 25

citationinformation flow

Exporters and Isolated AI CommunitiesHypothesisimportance indicates exportersindependence and importance indicates isolated islands

19 / 25independenceimportanceexportersislandsmainstreamloose exportersCBRCOLTIJCAImiddle period: 1997-2002COLT strong exporter, Conference on Learning TheoryIJCAI exports, but consists of core members of other communitiesCBR isolated, may lead to decline: hard to get external resources like funding or attract new memberswe have much more supportive evidence that CBR declined: size or citation impact19Q3: Can we exploit the model to maximise information diffusion?

20 / 25Influence and Information Diffusion

Actor-level diffusion maximisation problem: Which actors to target? Cross-community diffusion maximisation problem: Which communities to target? high in-degree21 / 25Actor-level: Application in public health, marketing, innovation managementCommunity-level: online fora, conferences, any mass-medium; recently gained more attentionSimulation used to simulate the spread

21Hypothesis: product of importance and entropy identifies seed communities that induce high overall adoptionOverall adoption estimated by a diffusion model onFour targeting strategies:

Impact Focus (IF) COINGreedy (GR)Group In-degree (GI)Random (RA)Information Diffusion ExperimentsIF = importance entropy22 / 25

Selection vs Prediction22

COIN Optimises Information DiffusionSelectionPredictionGreedy strategy overfitsImpact Focus is more robust23 / 25Part of the resultsWeek 497, uss=1

23Summary and Future WorkCOIN: computational model for community influenceCommunities influencing a particular communityRoles of communities: authorities vs hubsIsolated communities loosing influenceSeed communities for information diffusion

General (3 systems) and extensibleTensor-based extension of COIN captures topics

Future WorkMay be applicable to e.g. email networksImpact Focus may be improved by discounting overlapSentiment-informed community influence

24 / 2524Contributionsproposes a solution to the problem of measurement, analysis, and explanation of influence between communitiespurely structural approachextended to capture topicsempirical analysis of 3 systems common/different phenomenafirst approach to novel problem of cross-community information diffusion

Dissemination1 journal, 3 conference, and 1 workshop papersbest poster at NUIG research day 2013complete results, software, data, thesis, etc. at:

25 / 25http://belak.net/doc/2014/thesis.htmlPersonal Issues and Moderators

26CBR community: isolated

CBRJELIA27JELIA - European Conference on Logics in Artificial Intelligence27CBR: isolated and shrinking

rising impact factor driven by self-citationsdecreasing sizerigid member-baseCBR was unable to attract new members and decayedCannot be revealed by introspective analysis28size as a cardinality of the set of the membersdecrease in # papersdecrease in Google Trends since 200528Greedy Strategy29

----- Meeting Notes (17/02/2014 19:16) -----remove or fix29Group In-Degree30

GI = # links from outsideTopical Dimensions of InfluenceCOIN extended to capture topics Based on tensor algebraBetter interpretability and sensitivityConsistent with purely structural COIN

Example: V-TFL Admin vs V-TFL Discussion

actorscommunitiestopics3113 strong impacts V-TFL Admin -> V-TFL Discussion31Rise of Hubs and Authorities in Boards

32Exporters and Introspective Communities

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