Methods and tools for semantic social network analysis

Transcription

1 Methods and tools for semantic social network analysis Big Open Data Analysis, Roma 2 nd February, 2018 Antonio De Nicola (DTE-SEN-APIC, ENEA)

2 INFLUENCERS D Agostino G., D Antonio F., De Nicola A., Tucci S.. Interests diffusion in social networks. Physica A: Statistical Mechanics and its Applications 436, , (2015). 2

3 SOCIAL NETWORKS IN THE BIG DATA ERA Security Services Friends Interests Best Practices Projects Activities Places Events Business Services Social good Services 3

4 Influencers in Social Networks How to detect influencers? Most of the existing approaches consider the number of friends/ followers Issues People behavioral attitudes are not taken into account Robot factories (mainly a Twitter problem) Followers and retweets can be bought Fake accounts Source of the picture: Wall Street Journal 4

5 Objectives 1. Exploring the role of the structure of social networks (SN) in the dynamics of SN members interests 2. Analysis at the individual level of psychological features characterizing SN members 5

6 Main Idea The diffusion mechanism is a leading part in the dynamics of interests in social networks 6

7 Simulation Approach 7

8 Simulation Approach NOT BASED ON REAL DATA 8

9 Information Cascades Approach 9

10 Information Cascades Approach 10

11 Information Cascades Approach IS INFLUENCE REAL? 11

12 Perturbation Approach 12

13 Perturbation Approach DRUGS DON T WORK 13

14 This Approach

15 This Approach

16 This Approach

17 This Approach

18 Proposed Framework 1. Defining a model for Social Networks phenomena concerning interests diffusion 2. Software platform - Gathering social network knowledge - Estimating some individual features of Social Networks members - Assessing the validity of the propagation model 3. Experimentation - Research social networks in computer science and physics 18

19 Foundations INTERESTS DIFFUSION IN SOCIAL NETWORKS COMPLEXITY SCIENCE + SEMANTIC WEB Hybridizing Complexity & Semantics SEMANTIC MODELS SOCIAL SCIENCE Knowledge representation of Social Networks SN MODELS RANDOM GRAPHS SMALL-WORLDS SCALE-FREE MULTIPLEX EPIDEMICS SIS SIR SIRS DATA MINING ICM LTM Diffusion on Social Networks Enabling technologies ONTOLOGY ENGINEERING GRAPHS SIMILARITY REASONING CLUSTERING TECHNIQUES DIFFUSION DYNAMICS SENTIMENT ANALYSIS GRAPH VIZ 19

20 Case Studies Computer Science researchers Digital Bibliography & Library Project Coauthorship Physics Scientists American Physical Society Scientist 20

21 Weighted Interest Graph NoSQL Database Web Services Ontologies +1.0 Data warehouse Topics extracted from titles by means of Natural Language Processing (NLP) techniques Weight: estimated degree of interest of an author in a topic 21

22 Semantic Social Network 22

23 Our friends influence us 23 23

24 Environment influence us 24

25 We keep our own beliefs 25

26 Interests Propagation Model Probability a member h i is interested in a topic c k at a given time instant L hi (c k,t + Δt) = [1 x i ( c k ) x is ( c k )] L hi (c k,t)+ 1 +x is ( c k ) L s (c k,t) N hi x ij (c k ) L h j (c k,t) h j N hi Our beliefs Friends influence Environment influence x i (c k ), x is (c k ), x ij (c k ) are parameters that characterise individual features x ij (c k ) is a posi4ve number represen4ng the a7tude of a member h i to be influenced by his or her neighbours (h j ) with regard to the topic c k x i represents the sum over all j s: x i (c k ) def 1 = x ij (c k ) N hi h j N h i 26

27 Individual Features Susceptibility - The state of being easily affected, influenced, or harmed by something [from Merriam-Webster] - x i, x si (individual susceptibility) Scientist 3 Scientist 2 x i = 1 N hi h j N h i x ij - x ij (individual susceptibility contribution by the specific friend) Scientist i Scientist 4 Scientist 1 Authority - Power to influence or command thought, opinion, or behavior [from Merriam-Webster] Scientist 3 Scientist 2 a i = h j N hi x ji - a j (individual authority) Scientist i Scientist 4 Scientist 1 27

28 Architecture for Social Networks Analysis 28

29 Clustering Module Agglomerative clustering paradigm Based on a measure of semantic correlation between two topics E ck,c j : number of papers simultaneously indexed by two topics S ck,c j = E ck,c j E ck + E cj E ck,c j. Iteration of the clustering algorithm 1: topicsset = {c} 2: max 1 3: for all (c i topicsset) do 4: for all (c j topicsset) do 5: if (S ci,c j >max) then 6: max S ci,c j 7: c ichamp c i 8: c jchamp c j 9: end if 10: end for 11: end for 12: merge(c ichamp,c jchamp ) Merging algorithm 1: E cichamp E cichamp + E cjchamp 2: for all (c k topicsset) do 3: if (c k c ichamp c k c jchamp ) then 4: d i S cichamp,c k 5: d j S cjchamp,c k 6: dmax d i 7: if (d j >dmax) then 8: dmax d j 9: end if 10: S ck,c ichamp dmax 11: S cichamp,c k dmax 12: S cjchamp,c k : S ck,c jchamp : end if 15: end for 16: S cichamp,c jchamp : S cjchamp,c ichamp

30 Clusters Set Identification Problem Determining the best set of clusters of topics maximizing the intracluster similarity and minimizing the inter-cluster one Solution Observing the energy of the new cluster of topics resulting from merging two clusters Number of papers indexed by the topics of the cluster 30

31 Assessment of model parameters Mean square differences between the predicted L s and the observed ones χ 2 = [L hi (c k,t+ t) L hi (c k,t) δξ hi (c k.t)] 2 t,h i,c k Optimization using the χ 2 as an objective function Minimizing the deviation of prediction from observed values The optimum values of the parameters are achieved analytically if the solutions of θ χ2 =0corresponds to a feasible solution 31

32 Case Studies: Some Figures DBLP Case Study APS Case Study Number of papers: Number of authors Numbers of treatable authors* Number of topics à Observation period * Treatable authors: having publishing papers in, at least, two different years 32

33 Computer scientists Social Network 1980 Physicists social network

34 Testing Hypotheses For the sake of simplicity, we assume x i, x ij, x si do not depend on the specific topic c k L hi (c k,t +Δt) =[1 x i x si ] L hi (c k,t)+ 1 x ij L hj (c k,t) +x si L s (c k,t) N hi h j N hi Hypothesis Description HP 1 All members have the same susceptibility to trends and are not influenced by neighbours Parameters to be estimated!# x si =x s0 " $# x i, j = 0 HP 2 HP 3 All members have the same susceptibility to trends and to neighbours Members have an individual susceptibility to trends and to neighbours!# x si =x s " $# x i, j = x " $ x si 0 # %$ x i, j = x i 0 34

35 Testing Hypotheses For the sake of simplicity, we assume x i, x ij, x si do not depend on the specific topic c k L hi (c k,t +Δt) =[1 x i x si ] L hi (c k,t)+ 1 x ij L hj (c k,t) +x si L s (c k,t) N hi h j N hi Hypothesis Description HP 1 All members have the same susceptibility to trends and are not influenced by neighbours Parameters to be estimated!# x si =x s0 " $# x i, j = 0 HP 2 All members have the same susceptibility to trends and to neighbours!# x si =x s " $# x i, j = x Best performing hypothesis HP 3 Members have an individual susceptibility to trends and to neighbours " $ x si 0 # %$ x i, j = x i 0 35

36 Susceptibility from Neighbours Frequency DBLP x = Feasible Neighbours Susceptibility x i = 1 N hi h j N h i x ij Frequency Frequency Neighbours Susceptibility as fit APS x = Feasible Neighbours Susceptibility

37 Authority Frequency DBLP a = Authority a i = h j N hi x ji Frequency APS a = Authority 37

38 Famous Authors in Computer Science Name x i x si a i Wil M. P. van der Aalst Jack Dongarra John Mylopoulos Georg Gottlob Ian Horrocks Erol Gelenbe x Average values x s a 38

39 Physicists Authority and coauthors Number of coauthors There exist real huge influencers due to large facilities: CERN, ESRF Authority 39

40 SEMANTIC MULTIPLEX D'Agostino G., De Nicola A. Interests diffusion on a semantic multiplex. The European Physical Journal Special Topics, Springer, Volume 225, Issue 10, pp , October

41 Semantic (Social) Multiplex 41

42 Comparing APS and DBLP The overlap is extremely limited to some 5000 authors About the same susceptibility to trends APS authors are about twice susceptible to their co-authors than what DBLP ones are Success is related to authority not to susceptibility APS exhibits stronger influencers (facilities: CERN, ESRF etc) (huge authorities) 42

43 Comparing Authorities Super-Influencers 43

44 Some Takeaways Quality of semantic analysis affects the number of negative values of individual features Completeness of semantic analysis affects the number of null values of individual features Names Ambiguity affects authority Influence by neighbours is higher than influence by trends Authority is not just proportional to the number of friends 44

45 IMPACT 45

46 New marketing services Political campaigns 46

47 Spreading of fake news Chemtrails Terrostic networks Source of the photo: Daily Mail 47

48 48

49 GENDER DIVERSITY D'Agostino G., De Nicola A. : Analysis of Gender Diversity in the Italian Community of Information Systems. In: Proc. of itais 2017 Conference (2017). To appear in LNISO Springer series 49

50 Only 17.9 % of Computer Science bachelor s degrees in North America in went to women [From Taulbee Survey 2016] Similar numbers can be found in related fields as Information Systems and Information Technology fields 50

51 itais dataset We study the role of women in the itais community Digital dataset extracted from Observation period: From 2007 to papers 1127 authors % men % women 51

52 Methodological Framework DIMENSION METRIC INDEX Context Success Semantics Community Empowerment Self-realization - Clustered-topics segregation - Entropy of gender trends - Polarity - Semantic distance of genders - Gender rate - Clan segregation - Centrality indices - Authority - Citations - Keynotes, - H-index, - Charges - Papers Susceptibility - Neighbours susceptibility - Trend susceptibility Attitude Creativity - Novelty - Combinatorial creativity 52

53 Ontology building steps Six steps, each producing readily usable output De Nicola, A. and Missikoff, M.: A lightweight methodology for rapid ontology engineering. Communications of the ACM, 59(3):79-86 (2016). De Nicola, A., Missikoff, M., Navigli, R.: A software engineering approach to ontology building. Information Systems 34 (2), (2009). 53

54 Semantics of the IS field Topics used by: MALES FEMALES BOTH GENDERS 54

55 Italian Information Systems (IS) Community MALES FEMALES 55

56 Centrality indices Betweeness How important were a node if all nodes would try to communicate along the networks by the shortest path Closeness The average harmonic distance for a member to reach any other member of the community Degree The number of average coauthors of a member Eigen-centrality Probability of news to reach a node upon spreading on the network Betweenness Closeness Degree Eigen-centrality FEMALES MALES ALL

57 Insights from the authority index INSIGHTS: Both males and females have high values of authority Females influence the Italian IS community to the same extent as males 57

58 Insights from the susceptibility index Females are more influenced by neighbours than males Females are less influenced by trends than males 58

59 Creativity indices Novelty Authors that were the first to introduce a new topic in the community Combinational creativity Authors that were the first to combine different existing topics 59

60 Creativity assessment Gender Rates Members proposing novel topics Members combining topics Females % % % Males % % % 60

61 Considerations Males and females play an equally relevant role in the advancement of the Information Systems discipline Managerial implications for hiring policies Women and men exhibit equivalent leadership attitude and creativity 61

62 Conclusions Social networks - Big data analysis - Analysis of social phenomena Semantic social networks - Social networks + semantics Several methods and tools are available, the challenge is the insight 62

63 Acknowledgement Gregorio D Agostino Salvatore Tucci International reviewers for useful comments and observations Fulvio D Antonio for providing a preliminary list of DBLP topics Mark Doyle for providing the APS dataset Maria Luisa Villani for exchanging ideas on creativity 63

64 Thank you for your attention!