1. Modeling and Visualizing Information Propagation in Microblogging Platforms Chien-Tung Ho, Cheng-Te Li, and Shou-De Lin National Taiwan University ASONAM 2011 Jul 26, 2011 1

2. Microblog? Information Propagation? Microblog – Real-time social stream – Short messages and rich media – E.g. Twitter, Facebook, Plurk Information Propagation – Information News / Articles / Announcements Music/ Photos / Videos – Propagation In Twitter: Retweet In Facebook: Share In Plurk: RePlurk 2

3. Retweet and Information Propagation [Kwak et al. WWW’10] 3

4. Goals 1.How to measure an individual’s capability of distributing information in a microblog? – Finding Influential Individuals (for marketing) 2.How to estimate the strength of propagation for a certain topic in a microblog? – Detecting hot topics (for browsing) 3.Can we determine that a topic is propagated internally (e.g. gossip) in a microblog or come from external sources (e.g. mass media)? 4

5. Basic Model of Information Propagation Propagation Graph, PG – Vertex Information Provider Information Translator – Edge (u, v) u is the one who first affect v For a topic – PG is a forest – Each tree has one provider and many translators u World Cup Start! Time v 5

6. Approach Overview Micro-Blog Corpus query Q time period P relevant users construct influence trees Influence trees Topic Score LBIUBI A A B B score Influence Score Quantity Speed Speed of Propagation Quantity Speed Internal or External Influence Score Individual Ranking Geo-Distance Score Tree Quantity Weighted Tree Speed of Propagation Tree Score 6

7. Influence Tree Construction: Finding Relevant Users Micro-Blog Corpus query Q time period P Early Late Determine the “Relevant Users” based on (1) Post (2) Reply (3) Time Given a Topic & a time period Relevant Users 7

8. Influence Tree Construction: Propagation Links Given relevant users A, B Upper-bound Influence (UBI) --> B reply A’s post Lower-bound Influence (LBI) --> B reply A’s post B post relevant message Lower-bound Influence (LBI) (Strict) Upper-bound Influence (UBI) (Loose) : Reply & Post : Reply A B A B 8

9. Influence Tree Construction: Influence Trees A B C A B C ED F G H D E F G H We propose Influence Trees to capture the propagation behavior of each relevant user Given a query Q, RelUsers = {user 1,user 2 …,user n } LBI : F lb = {T lb1, T lb2,..., T lbn } UBI : F ub = {T ub1, T ub2,..., T ubn } Propagation Graph Influence trees A : Provider others : Translator 9

10. Approach Overview Micro-Blog Corpus query Q time period P relevant users construct influence trees Influence trees Topic Score LBIUBI A A B B score Influence Ability Quantity Speed Speed of Propagation Quantity Speed Internal or External Influence Score Individual Ranking Geo-Distance Score Geo-Distance Tree Quantity Weighted Tree Speed of Propagation Tree Score 10

11. Scoring Influence Trees: Influence Ability How many influenced people? For each user, – Use either UBI or LBI case to compute the influence score T t1t1 t2t2 t3t3 t4t4 Time Sequence Number ofInfluenced People(t k ) T t1t1 t2t2 t3t3 t4t4 Time Sequence Number of influenced peopleInfluence Score 11

12. Scoring Influence Trees: Propagation Speed How quickly does the information spread? For each user, – Use either UBI or LBI case to compute the propagation speed Time Sequence Number ofInfluenced People(t k ) T t2t2 t3t3 t4t4 Time Sequence AccumulativeInfluenced People(t k ) T t0t0 t1t1 t2t2 t3t3 t4t4 Spread quicklySpeed Score t1t1 Time Sequence AccumulativeInfluenced People(t k ) t0t0 t1t1 t2t2 t3t3 t4t4 Propagate Faster 12

13. Scoring Influence Trees: GeoDistance How widely does information spread in geography? For each user, – Use either UBI or LBI case to compute the influence score A B Country, City -> (longitude, latitude) -> Distance(x,y) 2 km C D E 4 km 2 km 1 km Time Sequence Total Distance betweenthe root and users(t k ) X Y t1t1 t2t2 t3t3 t4t4 t1t1 t2t2 t3t3 t4t4 2km 4km 2km 1km 13

14. Topic Score Compute the sum of influence scores of all information providers (i.e., roots of trees) – Info Provider: an individual who starts to spread the info about a certain topic Early Late Influence Score Term Influence Score Speed Score Term Speed Score Distance Score Term Distance Score 14

15. Internal vs. External Propagation Internal Propagation E.g. personal articles, gossip External Propagation E.g. news, earthquake events, We define a measure – Low prop => Internal – High prop => External NEWS External Propagation Internal Propagation 15

16. Review of Proposed Measures Lower Bound Influence (LBI) Upper Bound Influence (UBI) 16

17. Experimental Studies We use the Plurk data, which is one of the most popular microblogging services in Taiwan We study the following parts: – Individual Ranking – Topic Ranking – Internal vs. External Propagation 17

18. Visualizing Propagation Tree Given the topic “NBA”, we show the propagation tree from a certain users under UBI vs. LBI cases Upper Bound Influence (UBI)Lower Bound Influence (LBI) ReplyReply & Post 18

19. Individual Ranking by Influence Score 19

20. Topic Ranking Topics 1.MAC 2.DELL 3.ACER 4.MSI 5.LENOVO 20

21. Ranking Internal and External Topics We compute the proportion scores for 50 selected topics 21

22. Conclusions We model the microblogging information propagation by proposing influence trees with two propagation conditions (i.e., UBI and LBI) Based on our model, we propose a series of measures to estimate the propagation capability for individuals and topics – Find the internally or externally propagated topics Future directions – Predicting the future propagation paths – Tracking the sentiment flows 22

23. Thank you! Cheng-Te Li http://mslab.csie.ntu.edu.tw/~odd/ 23