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Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
Research Literature on Social Scores
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Research Literature on Social Scores

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This presentation discusses some literature that I have looked, for my research project on Social Scores

This presentation discusses some literature that I have looked, for my research project on Social Scores

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  • LDA-based generative process for generating a doc:For each document, pick a topic from its distribution over topic,Sample a word from the distribution over the words associated with the chosen topic.The process is repeated for all the words in the document.
  • each element DT’i j captures the probability that twitterersiis interested in topic tj.
  • Transcript

    • 1. SOCIAL SCORES Supervised by Dr. Dilum Bandara E.A.M.M Edirisinghe 138211N
    • 2. Outline • TunkRank – A Twitter Analog to PageRank • TwitterRank – Finding Topic-sensitive Influential Twitterers • Influence Rank – An Efficient Social Influence Measurement • Why your Klout score is meaningless • Research Questions Addressed 2
    • 3. Research Questions • How do existing systems calculate social scores ? • Which parameters are representative of a user’s true social influence ? • What are the desirable properties of a social score ? • How to vary the parameter weights across different topics and applications ? • How to come up with a performance efficient algorithm ? • How to calculate social score and update it in real time ? 3
    • 4. TunkRank A Twitter Analog to PageRank 4
    • 5. TunkRank • Proposed by Daniel Tunkelang in 2009 • Implemented by Jason Adams • Assumptions: – Influence(X) – Expected number of people who will read a tweet that X tweets – Probability that X will read a tweet posted by Y 1/||Following(X)|| X - a member of Followers(Y) Following(X) - the set of people that X follows – If X reads a tweet from Y, there’s a constant probability p that X will retweet it. TunkRank – A Twitter Analog to PageRank 5
    • 6. TunkRank TunkRank – A Twitter Analog to PageRank 6
    • 7. TwitterRank Finding Topic-sensitive Influential Users 7
    • 8. TwitterRank Two main contributions • Report homophily in Twitter • Introduce TwitterRank to measure topic sensitive influence of twitterers TwitterRank – Finding Topic-sensitive Influential Users 8
    • 9. Framework for the Proposed Approach Topic Distillation Topic-specific Relationship Network Construction TwitterRank – Finding Topic-sensitive Influential Users Topicsensitive User Influence Ranking 9
    • 10. Dataset • Consider a set of top-1000 Singapore-based twitterers S, |S|=996. • Crawled all followers and friends of each s ∈ S & stored them in set S’. • Let S’’= S ∪ S’, & S* = {s|s ∈ S’’, s is from Singapore}. |S*| = 6748. For each s ∈ S*, crawled all the tweets published, T. |T|=1,021,039. TwitterRank – Finding Topic-sensitive Influential Users 10
    • 11. Reciprocity in Following Relationships • 72.4% of the twitterers follow more than 80% of their followers • 80.5% of the twitterers have 80% of their friends follow them back Casual following or homophily? TwitterRank – Finding Topic-sensitive Influential Users 11
    • 12. Homophily in Twitter • Question 1: Are twitterers with “following” relationships more similar than those without according to the topics they are interested in? • Question 2: Are twitterers with reciprocal “following” relationships more similar than those without according to the topics they are interested in? TwitterRank – Finding Topic-sensitive Influential Users 12
    • 13. Topic Modeling • Goal: Automatically identify the topics that twitterers are interested in based on the tweets they published. • Latent Dirichlet Allocation (LDA) model is applied TwitterRank – Finding Topic-sensitive Influential Users 13
    • 14. Topic Modeling Results DT — D×T matrix D : No of users T : No of topics DTij : No of times a word in user si’s tweets has been assigned to topic tj. TwitterRank – Finding Topic-sensitive Influential Users 14
    • 15. Hypothesis Testing • Applied on a set of twitterers who publish more than 10 tweets in * total, S u S u* | = 4050. | • Row normalize the DT matrix as DT’ such that ||DT’i ·||1=1 for each row DT’i . • Thus each row of matrix DT’ is basically the probability distribution of twitterer si’s interest over the T topics. • Measure the topical difference between twitterers • Formalize each question with two sample t-tests and proves the existence of homophily in the Twitter dataset There are twitterers who are serious in following others. TwitterRank – Finding Topic-sensitive Influential Users 15
    • 16. Topic Specific Twitter Rank • Forms a directed graph D(V,E) – edge between two twitterers if there is “following” relationship between them – edge is directed from follower to friend. • A topic-specific random walk model is applied to calculate the user’s influential score. • The transition matrix for topic t, denoted as Pt . The transition probability of surfer from follower si to friend sj is: | Tj | pt (i, j ) | Ta | a:si * simt (i, j ) simt (i, j) 1 | DTit' ' DTjt | sa TwitterRank – Finding Topic-sensitive Influential Users 16
    • 17. Topic Specific Twitter Rank • Topic-specific teleportation: Et • DT ''t The influence scores of twitters are calculated iteratively: TRt • Pt TRt (1 ) Et Aggregation of topic-specific TwitterRank: TR rt TRt t TwitterRank – Finding Topic-sensitive Influential Users 17
    • 18. Review • Homophily does exist • Still some follow not because of the topical similarity • Easy to game • Need to discuss an incremental approach to topic distillation TwitterRank – Finding Topic-sensitive Influential Users 18
    • 19. InfluenceRank An Efficient Social Influence Measurement 19
    • 20. InfluenceRank • Define the influence of a user from two perspectives – Users Relative Influence – Users Network Global Influence • Define the micro blog network as SN = (G,B,I) G - link network structure, B - set of interactive behaviors between each pair of associated users I - set of profile information of each user • Graph G = (V,E) V - set of nodes represented by user’s index E - set of directed edges InfluenceRank – An Efficient Social Influence Measurement 20
    • 21. InfluenceRank • Define the set of behaviors B, B = (R,C,M) (R - Retweets), (C - comment), (M - mention) • Define the profile information set I, I = (P,T,K), P - set of number of postings T - set of users’ interest tags K - set of users’ content keywords. InfluenceRank – An Efficient Social Influence Measurement 21
    • 22. Metrics Explored InfluenceRank – An Efficient Social Influence Measurement 22
    • 23. User Relative Influence Rank InfluenceRank – An Efficient Social Influence Measurement 23
    • 24. Users Network Global Influence Rank InfluenceRank – An Efficient Social Influence Measurement 24
    • 25. Influence Rank Algorithm Time complexity - O(e) InfluenceRank – An Efficient Social Influence Measurement 25
    • 26. Influence Rank Algorithm • Evaluated with a dataset of Tencent Weibo, contrast with the TunkRank algorithm • Emphasis on users’ interactive behaviors • Weight of each metric considered to measure the user’s relative influence is taken as equal • Instead of similarity of topics considers the similarity of keywords • Ignore the impact of negative comments and conversations • Model is based on a snapshot of current relationships and interactions InfluenceRank – An Efficient Social Influence Measurement 26
    • 27. Why your Klout score is meaningless 27
    • 28. Why your Klout score is meaningless ? Klout is far more similar to a derived measurement inconsistent and not trustworthy individually Why your Klout score is meaningless 28
    • 29. What should Klout score satisfy ? • Ordering by Klout should make sense in the real world • The score should not be easy to game • The score should be monotonic Why your Klout score is meaningless 29
    • 30. Klout score comparisons • A set of individuals with Klout in the 40-49 range • A set of individuals with Klout in the 55-64 range • A set of individuals with Klout in the 70-79 range • A set of individuals with Klout >= 80 Why your Klout score is meaningless 30
    • 31. Group3 (Klout 70-79) • U1-Tim Ferriss – Author of the 4 Hour Workweek and 4 Hour Body • U2-Jack Dorsey – Executive Chairman of Twitter and CEO of Square • U3-Matt Cutts– Head of web spam team at Google • U4-MG Siegler – Writer for Techcrunch • U5-Klout – Influence score service • U6-David Pogue – Tech guy from the NYT • U7-Jeffrey Zeldman – designer, writer, and publisher Why your Klout score is meaningless 31
    • 32. Group3 (Klout 70-79) U1 U2 U3 U4 U5 U6 U7 As per 29th May 2011 Why your Klout score is meaningless 32
    • 33. Klout violates the Desirable Properties • Connecting an additional account will always increase the Klout score. • The degree to which followers are influential seems to be irrelevant or matter very little • The differential between number of people someone follow seems to be irrelevant or matter very little. • In terms of value to the Klout score: follow < Retweets < unique Retwitters < unique mention can be inconsistent • In terms of value to the Klout score: like < comment can be inconsistent Why your Klout score is meaningless 33
    • 34. Research Questions Addressed 34
    • 35. Research Questions Addressed How do existing systems calculate social scores TunkRank TwitterRank InflueceRank Influence Measure with a Network Amplification Score Probability that the follower will read a tweet posted by the followee Probability a tweet read will be retweeted Number of followers and their influence Measures the topic-sensitive influence of twitterers Considers the similarity between friends on topics Number of tweets published by all friends Defines a user’s relative and global influence Number of followers Quality of followers Quality of tweets Similarity of interests Accounts the content and conversation generated by considering indegree and outdegree of the social network for multiple levels Research Questions Addressed 35
    • 36. Research Questions Addressed Which parameters are representative of a user’s true social influence • It’s not just the number of followers or the number of friends • Link structure • Following relationship • Similarity • Interactions • Topics and communities • Quality of followers, tweets etc. Research Questions Addressed 36
    • 37. Research Questions Addressed What are the desirable properties of a social score • Ordering by the score should make sense in the real world • The score should not be easy to game • The score should be monotonic • Equation should be simple & easy to understand/interpret • Should be meaningful Research Questions Addressed 37
    • 38. References • Daniel Tunkelang. (2009, Jan 13). A Twitter Analog to PageRank [Online]. Available: http://thenoisychannel.com/2009/01/13/a-twitter-analog-topagerank/ • Neal Richter. (2009, Feb 18). TunkRank Scoring Improvement [Online]. Available: http://aicoder.blogspot.com/2009/02/tunkrank-scoringimprovement.html • Jianshu Weng et al., “TwitterRank: Finding Topic-sensitive Influencial Twitterers,” in WSDM Conf., New York, USA, 2010, pp. 261-270 • Wenlong Chen et al., “InfluenceRank: An Efficient Social Influence Measurement for Millions of Users in Microblog,” in 2nd Int. Conf. on CGC, Xiangtan, 2012, pp. 563-570 • Alex Braunstein. (2011, June 01). Why your Klout score is meaningless [Online]. Available: http://alexbraunstein.com/2011/06/01/why-yourklout-score-is-meaningless/ • Sean Golliher. (2011, June 27). How I Reverse Engineered Klout Score to an R2 = 0.94. [Online]. Available:http://www.seangolliher.com/2011/uncategorized/how-ireversed-engineered-klout-score-to-an-r2-094/ 38
    • 39. Thank You 39

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