A Comparative Study of HITS vs PageRank Algorithms for Twitter Users Analysis
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Social Networks such as Facebook, Twitter, Google+ and LinkedIn have millions of users. These networks are constantly evolving and it is a good source of information, both explicitly and implicitly. The analysis of Social Network mainly focuses on the aspect of social networking with an emphasis on mapping relationships, patterns of interaction between user and content information. One of the common research topics focuses on the centrality measures where useful information of the connected people in the social network is represented in a graph. In this paper, we employed two link-based ranking algorithms to analyze the ranking of the users: HITS (Hyperlink- Induced Topic Search) and PageRank. We constructed Twitter user retweet-relationship graph using 21 days worth of data. Lastly, we compared the ranking sequence of the users in addition to their followers count against the average and also whether they are verified Twitter accounts. From the results obtained, both HITS and PageRank showed a similar trend, and more importantly highlighted the importance of the direction of the edges in this work.
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A Comparative Study of HITS vs PageRank Algorithms for Twitter Users Analysis
- 1. AComparativeStudy of HITS vs PageRank Algorithms forTwitter UsersAnalysis Ong Kok Chien , Poo Kuan Hoong and Chiung Ching Ho Faculty of Computing Informatics, Multimedia University Cyberjaya.1
- 3. Introduction Graph analysis algorithms on Social Network. Identify worth noticingTwitter Users for a specific bag of topics. 3
- 4. Twitter Maximum 140 characters microblogging site. “ATweet is an expression of a moment or idea. It can contain text, photos, and videos. Millions ofTweets are shared in real time, every day.” Reply Retweet Favorite Hashtags https://about.twitter.com/what-is-twitter/story-of-a-tweet .com 4
- 5. Problem Statement Why is it important to rank users? By ranking users, we aims to differentiate relevant important information sources from those provided by spam accounts. 5
- 6. Objectives To rankTwitter users using HITS and Page Rank. To identify the direction of edges for the graph. 6
- 7. Methods Link-based ranking algorithms (HITS & PageRank) Twitter Users as Nodes. Retweet relationships as Edges. Direction of graph. 7
- 8. Example PageRank (PR) E.g.: BackLinks inWebsites - Referring back to OriginalContent. - Sergey Brin & Larry Page (1998). The anatomy of a large-scale hypertextual Web search engine. Image extracted from Wikipedia 8
- 9. Example Hyperlink-InducedTopic Search (HITS) Hubs : Catalog for relevant contents. Authorities : Great contents itself. - Jon Kleinberg (1999). Authoritative sources in a hyperlinked environment. Image extracted from cornell.edu 9
- 10. Example Minister ofYouth & Sports Khairykj shatyrah2 AyenSanji 10 https://twitter.com/Khairykj/status/410964119521460224
- 11. Architecture Twitter Streaming API Configure Keywords 1 JSON raw data 2 3 HiveQL 4 UnixScript 11
- 12. Keywords TeamMsia every1connects tmrewards yellowpages_my tmsmebiz MaxisComms MaxisListens DiGi_Telco DiGi_Youths helloUMobile HyppTV Streamyx UMobile Digi Maxis Yes4G Celcom xpaxsays TMCorp TMConnects 12
- 13. Basic Statistics Raw Dataset TotalTweets : 230,166 Total Unique Users : 121,461 ( screen_name ) TotalVerified Users : 113 ( verified ) Average Followers Count : 983 ( followers_count ) Experiment Dataset ( Retweets ) No. ofTweets: 56,727 No. of Unique users: 50,636 9th Dec 2013 -> 29th Dec 2013 13
- 14. Results PageRank Ranking HITS TeamMsia 1 TeamMsia ManOlimpik 2 Khairykj Khairykj 3 ManOlimpik OKS_HARIMAUMUDA 4 OKS_HARIMAUMUDA BrooksBeau 5 FIH_Hockey TMCorp 6 BB_Johor LawakLegend 7 AtletMalaysia WTFSG 8 TMCorp JanganPanas 9 Faif_D FIH_Hockey 10 BBST15 60%Top10 were the same bag of users. (70% forTOP20) 14
- 15. Results User Screen Name Verified Follower Counts TeamMsia False 98,469 ManOlimpik False 1,661 Khairykj True 432,259 OKS_HARIMAUMUDA False 35,058 BrooksBeau False 1,226,629 TMCorp False 13,767 LawakLegend False 49,593 WTFSG False 469,909 JanganPanas False 14,642 FIH_Hockey False 24,628 No. of followers ofTwitter user doesn’t directly affect the No. of retweets. Not very important to have a verified account to get retweeted.15
- 16. Results “Football at the 2013 Southeast Asian Games” 9th Dec 2013 -> 29th Dec 2013 16
- 17. Results Closer look of how TeamMsia involved in the conversation. 17
- 18. Summary The use of Link-based ranking algorithms such as Page Rank and HITS does promise us some insights about concerningTwitter Users and their significance. These insights can be useful for Customer Care / Churn Management 18
- 19. FutureWork Additional relationships to be considered. (Conversational Reply, Pure Mentions) Further validation of additional attributes. (Verified,Tweet Count, Followers Count, Following Count etc. ) Extend deeper intoTweet level analysis. 19
- 20. Question? Contact Me Ong Kok Chien ahchienong@gmail.com http://qrs.ly/2t49r7l vCard Download link Thanks… 20
Notes de l'éditeur
- Outline of presentation.
- Brief introduction about myself. Talk about Background.
- Brief Introduction to Twitter
- Explain briefly about how we process the data (e.g. : tools and library we used) JUNG
- Duration of data
- Pure follower counts comparison
- Instant understanding through visualization in Social Network
- Baby steps for me to kick start my journey.
- CrossCheck Twitter Trending API for interesting topic.