Finding Relevant Content and Influential Users based on Information Diffusion

Transcription

1 ProfileRank: Finding Relevant Content and Influential Users based on Information 13, Chicago, IL Arlei Silva 1, Sara Guimarães 2, Wagner Meira Jr. 2, Mohammed Zaki 3 1 Computer Science Department University of California, Santa Barbara, CA 2 Computer Science Department Universidade Federal de Minas Gerais, Brazil 3 Computer Science Department Rensselaer Polytechnic Institute, NY

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3 Social Media in Numbers Twitter: 5M users, 34M tweets/day Tumblr: 1M users, 75M posts/day Facebook: 1.15B users, 1B pieces of content shared/day Instagram: 3M users, 5M photos shared/day

4 Influence and Relevance in Social Media: Questions Who are the influentials? influence: ability of popularizing information personalized influence What is relevant? relevance: capacity of satisfying a user s information needs personalized relevance Why are these questions important? Information diffusion mechanisms Recommender systems Viral marketing

5 Information Diffusion Data Content creation/propagation represented as tuples: A RT@user_ A BB!BB? B C RT@user_ B RT@user_1 C user, A, t user, B, t 1 user 1, A, t 2 user 1, C, t 3 user 2, B, t 4 user 3, C, t 5 (a) Twitter (b) Diffusion data How can we measure influence and relevance?

6 ProfileRank Random walks over a content-user graph Relevant content is created and propagated by influential users and influential users create relevant content Relies on content propagation, instead of a social network In some scenarios, there is no social network available # of followers capacity to propagate content [Cha et al. 1] user, A, t user, B, t 1 user 1, A, t 2 user 1, C, t 3 user 2, B, t 4 user 3, C, t 5 (a) Diffusion data (b) Diffusion model

7 ProfileRank: Formulation Information diffusion data information diffusion graph G(U, C, F, E) G can be represented as two matrices: 1. M: User-content matrix 2. L: Content-user matrix Relevance r and influence i computed as: r = im r (k) = r (k 1) LM) r = (1 d)u(i dlm) 1 i = rl i (k) = i (k 1) ML i = (1 d)u(i dml) 1 These equations always have a unique solution

8 Related Work Social influence and information diffusion [Gruhl et al. 4, Leskovec et al. 7, Tang et al. 9, Cha et al. 9, Cha et al. 1, Weng et al 1, Goyal et al. 1, Romero et al. 11] Content search and recommendation [Baluja et al. 8, Chen et al. 1, De Choudhury et al. 11, Kim and Shim 11] Link prediction in social networks [Liben-Nowell and Kleinberg 3, Hannon et al. 1, Leroy et al. 1, Gomez Rodriguez et al. 1] Relevance in hyperlinked environments [Kleinberg 98, Page et al. 99]

9 Evaluation Problem: Absence of ground truth information Influential users Relevant content Solution: Considering personalized assessments A user is influential to another user A content is relevant to a given user ProfileRank can be personalized to provide recommendations Assumption: Recommendation accuracy model quality

10 Evaluation: Datasets Dataset content #users #pieces of content #propagations s TW-CARS tweet 529,63 369,287 1,368,8 T TW-SOCCER tweet 837,559 3,485, ,144 T TW-ELECTIONS tweet 3,86,251 4,67,221 15,844,788 T TW-LARGE tweet 17,69, ,553,56 71,835,17 T MEME meme 96,68,34 21,999, ,95,936 M Table: Information diffusion datasets. Dataset edge #edges source TW-SOCCER follower-followee 269,217,548 Twitter TW-LARGE follower-followee 1,47,, Twitter Table: Network datasets.

11 Evaluation: Content Recommendation Task: Predicting content users will propagate 5/5% split training/test Content: tweets and memes ProfileRank (global and personalized): Recommendations based on relevance scores Baselines: collaborative filtering MyMediaLite library

12 Evaluation: Content Recommendation 1 1 true positive rate precision false positive rate recall (a) ROC (b) Prec-recall PPR precision.1.5 recall WRMF POPULAR PR n (c) Precision@n n (d) Recall@n

13 Evaluation: User Recommendation Task: Predicting influence links Cold-start Follower relationships on Twitter data ProfileRank (global and personalized): Recommendations based on influence scores Baselines: cold-start link prediction [Leroy et al. 1] # content shared Adamic-Adar score # content shared + common neighbors Adamic-Adar score + common neighbors

14 Evaluation: User Recommendation 1 1 true positive rate precision false positive rate recall (a) ROC (b) Prec-recall precision recall PPR AA CC AA+CN CC+CN PR n (c) Precision@n n (d) Recall@n

15 Evaluation: Top Influentials - US Elections user BarackObama Obama212 UberFacts BorowitzReport StephenAtHome truthteam212 Real Liam Payne MittRomney thinkprogress realdonaldtrump description US President and Democrat candidate Obama s campaign Comedy facts Comedy news Comedian Obama s campaign Pop singer Republican candidate Political blog Businessman

16 Evaluation: Top Relevant Tweets - US Elections hi mr Obama have you got up all night yet? That was one of the strangest days ever will smith taylor swift justin bieber michelle obama wow what it going on with my life!! Obama, congratulations on being the first sitting President to support marriage equality. Feels like the future, and not the past. #NoFear Same-sex couples should be able to get married. President Obama Summertime description Message from Liam Payne to Barack Obama Liam Payne about the 212 Kid s Choice Award Lady Gaga about Obama s support for gay marriage Obama about his support for gay marriage Josh Devine about a picture including a Obama s statue

17 Concluding Remarks We proposed a simple model that accurately measures user influence and content relevance in information diffusion data. Model based on random walks over a content-user graph Extensive evaluation: Quantitative: User and content recommendation Qualitative: Intuitive results in real data Future work: ProfileRank+filtering for search on Twitter Incorporating temporal dynamics for updated assessments Incorporating textual and network information

18 ProfileRank: Finding Relevant Content and Influential Users based on Information Diffusion More information: This student received a travel award. Thanks!

19 Evaluation: Content Recommendation 1 1 true positive rate precision false positive rate recall (a) ROC (b) Prec-recall.3.6 PPR precision.2.1 recall WIKNN POPULAR PR n (c) Precision@n n (d) Recall@n

20 Evaluation: Content Recommendation Method AUC BEP PPR PR WRMF WBPRMF WIKNN WUKNN POPULAR (a) TW-CARS Method AUC BEP PPR WIKNN WUKNN WBRMF WRMF POPULAR PR (b) MEME

21 Evaluation: User Recommendation Method AUC BEP PPR PR AA+CN CC+CN AA CC Table: TW-SOCCER

22 Evaluation: Pairwise Ranking Correlations ProfileRank PageRank #propag. #followers ProfileRank - n/a.89 n/a PageRank.28 - n/a n/a #propag n/a #followers (a) User metrics ProfileRank #content PageRank #user #followers propag. propag. ProfileRank -.36 n/a.42 n/a #content propag n/a.44 n/a PageRank n/a n/a #user propag n/a #followers (b) Content metrics

23 Evaluation: Execution Time Dataset ProfileRank PageRank TW-CARS TW-SOCCER TW-ELECTIONS TW-LARGE MEME Table: Running time (in seconds).