We Know What @You #Tag: Does the Dual Role Affect Hashtag Adoption?

1. #France #Lyon #www2012 #In_Action We Know What @You #Tag: Does the Dual Role Affect Hashtag Adoption? Lei Yang1, Tao Sun2, Ming Zhang2, Qiaozhu Mei1 1School of Information, the University of Michigan 2School of EECS, Peking University

2. Mark Content #Obama #Tax Hashtag: Content Tagging

3. Browse and Retrieve Hashtag: Content Tagging

4. Link Relevant Topics and Events Hashtag: Content Tagging • e.g., #BREAKINGNEWS: #earthquake with preliminary magnitude of 3.4 has struck 11 miles north of Indio

5. Hashtag =? Traditional Tag Hashtag Tag

6. The study (Starbird et al., 2011) found that • According to their email interview Hashtag: Another Role hashtag “I had never spoken with all of these people, prior to the earthquake. I would have found all of them via the #Haiti #HelpHaiti or other Haiti hashtags, or occasionally a retweet from someone already in my Haiti network.”

7. A hashtag defines a virtual communityof users • with the same background • e.g., #umich, #Microsoft • with the same interests • e.g., #iphone, #politics • involved in the same conversation or event • e.g., #www2012, #VoteForObama Hashtag: Community Participation Dual Role Content Tagging + Community Participation

8. A user adopts a hashtag Dual Role Hashtag Adoption Create a new bookmark Or Present interests to a topic Content Tagging Initialize a new community Or Participate a community Community Participation

9. Dual Role Hashtag Adoption Factors Content Tagging Community Participation Hashtag Adoption

10. To quantify factors that affect the dual role. • To test whether the proposed factors will affect the behavior of hashtag adoption. • To make predictions of future adoptions of hashtags. What to do

11. Provided a macroscopical analysis of the dual role. • Provided a foundation of the rationality of the behavior of hashtag adoption in terms of the dual role. • Provided an empirical analysis of how the dual role affects the behavior of hashtag adoption. • Provided a feasibility study of hashtag recommendation. Contribution

12. Step by Step Step 1. Quantify the factors associated with the dual role

13. Content Tagging • Relevance to the content (e.g., adaptive filtering) • Closeness to users’ personal Preference (e.g., collaborative filtering) • … • Community Participation • Prestigeof community members (e.g., preferential attachment) • Influenceof friends in the community (e.g., social influence) • … Step 1. Factors Affecting the Dual Role

14. Relevance assesses the similarity between a user u and a hashtag h. Step 1. Content Role - Relevance A new hashtag h Dh: Tweets containing h Du: Tweets u have posted Relevance to my interests = sim(Du, Dh)

15. Preference measures how close a hashtag h is tied to the personal preference of a user u. • Any reasonable function f (.) introduces an instantiation of preference, such as sum, average, maximum or minimum. Step 1. Content Role - Preference H : hashtags I have used before A new hashtag h My preference toh = f { sim (h, h’) | h’in H }

16. Prestige is one of the major factors affecting the behavior of joining communities. • Any reasonable function f (.) introduces an instantiation of prestige. Step 1. Community Role - Prestige A new hashtag h Retweet network G Users who have adopted h Prestige of users in G f {prestigeof u’| u’has used h}

17. Influence assesses how much a user u is influenced by its friends already in the community of hashtag h. • The function f(.) can be realized as any reasonable aggregate function of all the individual influences. Step 1. Community Role - Influence A new hashtag h Retweet network G U = {friends of u who have used h and may influence u} f { influence (u, u’) | u’ in U }

18. Role-Specific Factors • Relevance • Preference • Prestige • Influence • Role-Unspecific Factors • Popularity • Length • Degree • Freshness • Activeness Role-Specific and -Unspecific Factors

19. Datasets

20. Step by Step Step 1. Quantify the factors associated with the dual role Step 2. Correlation Analysis

21. The relationship between role-specific factors and users’ degree of interests in hashtags. Step 2. Correlation Analysis Time Interval Time <u1, h1>, <u2, h2>, …, <un, hn> average degree of interests 1 2 3 … K target factor

22. Step 2. Correlation Analysis Relevance Preference Prestige Influence Degree of Interests Stream Dataset Relevance Preference Prestige Influence Degree of Interests Politics Dataset

23. Step by Step Step 1. Quantify the factors associated with the dual role Step 2. Correlation Analysis Step 3. Regression Analysis

24. We want to further look for evidences of Step 3. Regression Analysis • Whether each of the proposed measures has a predictive power of hashtag adoption? • If yes, how significant they are? • Whether the effect remains significant when the factors interplay with each other?

25. Dependent variable <u, h>: 1 / 0indicating whether u will use h. • Independent variables: one instantiation of each role-specific factor. • Control Factors: five instantiations of role-unspecific factors. • Logistic Regression Step 3. Regression Analysis Never used before Calculate dependent variable Time Interval 1 Time Interval 2 Time Calculate independent variables

26. Step 3. Regression Analysis

27. Step by Step Step 1. Quantify the factors associated with the dual role Step 2. Correlation Analysis Step 3. Regression Analysis Step 4. Prediction of hashtag future adoption

28. Feasibility study of constructing an accurate and effective hashtag prediction and recommendation system. • Given a user and a hashtag,we formulate the binary classification problem as the following: • Support Vector Machine Step 4. Prediction of Hashtag Adoption • Classes: class 1indicates that theuserwill use the hashtagin future, and class 0 denotes that theuser won’t use thehashtagin future. • Features: role-specific factors and role-unspecific factors.

29. Training and Test Step 4. Prediction of Hashtag Adoption Training Test Interval 3 Interval 4 Interval 1 Interval 2 Time Calculate Features Estimate Class Calculate Features Estimate Class

30. Systems • Baseline: all role-unspecific factors • Baseline + relevance / preference / prestige / influence • Baseline + relevance + preference + prestige + influence • Hashtag adoption in retweetsand non-retweets • All: all tweets • NonRTs: all non-retweets • RTs: all retweets Step 4. Prediction of Hashtag Adoption

31. Prediction Performance on POLITICS Step 4. Prediction of Hashtag Adoption

32. Prediction Performance on MOVIE Step 4. Prediction of Hashtag Adoption

33. Prediction Performance on RANDOM Step 4. Prediction of Hashtag Adoption

34. Results of analyses in this work all indicate that a hashtag serves as both a tag of content and a symbol of membership of a community. • The measures we propose to quantify the factors all present significant predictive power to the adoption of hashtags. • The prediction analysis provides a feasibility study of hashtag recommendation systems, suggesting a promising future direction of research. Conclusion

35. Study and differentiate the two roles of hashtags. • Study what role users are adopting when they are adopting a new hashtag. • Study how to better make use of the dual role to do hashtag recommendation. Future Work

36. Thanks!