Exploring Tie Strength in Micro-blogs: Analyzing Interaction Behaviors

1. TIE STRENGTH IN MICRO-BLOGS He Yaxi 2012.4.26

2. INTRODUCTION • Micro-blogs • A kind of person-based RSS service. • Forming a tie through follow even does not need permission of the two. • A recent study of Facebook showed that users only poke and message a small number of people while they have a large number of declared friends. • Both acquaintances and close friends appear in the same social list with same methods. • The declared social networking is incompetent to reflect the real social relation of users.

3. STRUCTURE • Introduction • Methodology • Experimental results • Network comparison

4. INTRODUCTION • Tie-strength • Mark Granovetter was the first to propose the notion of tie strength in “The Strength of Weak Ties”. • He highlighted the important role that tie strength plays in information exchange between people. • Modeling tie strength in SNS contributes to the understanding of the exchange and transmission of information and influence between users. • In fact, tie strength represents unequal contacts as the weight of links in real interactive social network instead of the one that forms through follow.

5. INTRODUCTION • Weak ties • In job finding practices, job opportunities were found mostly through word-of-mouth communication with weak ties, for the reason that acquaintances who traveled in different circles and had more access to different information than strong ties or close friends • Strong ties • When one may wish to use only close contacts to gather or acquire information, for instance, one may be interested in assembling a team or otherwise gathering information that is distributed in different parts of a social network using only strong ties. • Tie strength in Micro-blogs • One may follow lots of users such as friends in reality, celebrities or enterprises. • It is unreasonable for user to get the flow of information in the same way as not all of his followees appeal to him equally. • It means a lot to distinguish different ties in terms of strength in the static network forming through follow.

6. IDEA • As two users forming a tie is much more easier than maintaining a tie in SNS, we focus on the records of interaction behaviors. • Interaction behaviors • Comment • Retweet • Mention

7. CONTRIBUTIONS • Measurement of tie strength between users. • Key factors that matter tie strength significantly. • Acquisition of a weighted, dynamic network. • Property variation of network topology.

8. STRUCTUER • Introduction • Methodology • Experimental results • Network comparison

9. METHODOLOGY • Dimensions of tie strength • Data collection • Variables mapping • Model construction

10. DIMENSIONS OF TIE STRENGTH • Contact frequency • SNS providers try to present user with information in terms of stream to promote the amount of interactive behaviors. • Contact frequency promotes other dimensions. • Emotional intensity • The recognition of entities produces intrinsic emotions. • Stress more on cognition of the other. • Intimacy • The closeness of relationship • Deep affection between two entities acting as a sense of reliance and security. • They are willing to talk with open mind to get or provide recognition and support. • Reciprocity • the basic condition to establish and maintain a link. • measured by cost and profit including time, energy ,emotion, etc. • Cost less and gain more will increase the tie strength.

11. DATA COLLECTION: • Users from Sina Micro-blogs through API. • The original network is defined by users’ followees. • 10 users are defined as participants from BUPT. • Participants requirement • Account is created more than six months • With over 100 followers, 100 followees, 200 statuses • Followees requirement • NO Verified-users. • NO users with more than 2000 followers or 2000 statuses. • NO users with under 20 statuses or10 followers. • With 40~50 followees left randomly.

12. VARIABLES MAPPING Independent variables

13. VARIABLES MAPPING Dependent variables Participants mark the relationship with some of his followees in the questionnaire.

14. DATASET • 关于微博、粉丝、关注、评论、回复的几个统计图

15. MODEL CONSTRUCTION • Data normalization • Polynomial Regression • Native Bayesian classification

16. Data normalization • Data normalization for each participant • This linear transformation locates all data in [0, 1], and the numeric difference is relevant. • Notes • Every time calculating strength for a new tie, it depends on other ties the user owns. • It happens in reality, for the reason that relationship difference is relevant .

17. POLYNOMIAL REGRESSION • Estimate tie strength where variables affect tie strength as a linear combination. • With stepwise regression in three datasets to determine key variables affecting tie strength significantly.

18. RESULTS WITH POLYNOMIAL REGRESSION

19. RESULTS WITH POLYNOMIAL REGRESSION

20. NATIVE BAYESIAN CLASSIFICATION • Goals • Test the results of the polynomial regression. • Method • Define the normalized value of tie strength under mean value as weak ties and others are strong ties. • Test the result of classification with U1_Set. • Results • With variables obtained above, higher precision of classification could be obtained than with all of variables.

21. RESULTS WITH NATIVE BAYESIAN CLASSIFICATION

22. CORRELATION ANALYSIS • Analyze the correlation in U1_Set. • Correlation between each dimension with tie strength =》effective service to improve use experience • Correlation between variables with its dimension =》mapping reason

24. STRUCTUER • Introduction • Methodology • Experimental results • Network comparison

25. TOPOLOGICAL COEFFICIENTS • Density • The closeness of nodes in the network. • Actual links/all possible links • For directed graph: L/N(N-1) • For undirected graph: 2L/N(N-1) • Clustering coefficient • For a node • the coincidencedegree of friends. • between neighbor nodes: actual links /all possible links • For a network • average clustering coefficient of all nodes • higher in SNS than in random network

26. TOPOLOGICAL COEFFICIENTS • Shortest path length • The average number of steps along the shortest paths for all possible pairs of network nodes. • It is a measure of the efficiency of information or mass transport on a network. • Small world network theory predicts that the average path length changes proportionally to log n, where n is the number of nodes in the network. • Diameter • Maximal distance between any two nodes in the network. • Six-degree Thoery.

27. Distribution of in-degree Distribution of clustering coefficient Distribution of out-degree

28. COMPARISON OF NETWORK PROPERTY

29. APPLICATION • Display of information stream. • Access control for users in Micro-blogs. • …………

30. Thank you！