Extraction and Analysis of Social Networks Datasets

1. Extraction and Analysis of Social Networks Datasets Luis Perez Cruz University of Houston Dr. RongZheng REU Program

2. Background • Usefulness of Online Social Networks. • Spread of Influence • Word-of-Mouth.

3. Example Inactive Node 0.6 Active Node Threshold 0.2 0.2 0.3 Active neighbors X 0.1 0.4 U 0.3 0.5 Stop! 0.2 0.5 w v

4. Purpose • Collect datasets from online social networks. • Extract Quantitative Analysis from such Datasets.

5. Exp. Procedure • Started crawling the web looking for public datasets corresponding to different online social networks. • Many Repositories were found. • Some flaws were found in most of the datasets: 1) Weak Networks. 2) Ambiguous Connections.

6. Some Ambiguous Connections.

7. Weak Network

8. Exp. Procedure Cont’d: Use of APIs • API stands for Application Programming Interface. • Consists of a set of methods that allows communication or requests between software programs. • Common applications, operating systems, libraries and so on usually have their own API.

9. Problem Faced • Most of them require to handle some kind of authentication in order to get the information we need. Oauth Workflow

10. Solution • We ended up using the Twitter REST API. • It allows some unauthenticated requests to its API. • We can extract data such as: - UserId - FollowersId - Tweets sent to that user.

11. Limitations of REST API • Only allows 150 requests per hour (unauthenticated calls). • Is changed constantly, usually due to deprecation of some features. • Abusing the rate limit can lead to my script being blacklisted.

12. Analysis of Datasets • Analysis of datasets was divided into three categories: Average Number of Nodes: 48K Average Number of Edges: 113K Average Degree: 188 Average Max. Degree: 5,083

13. Some Graphs Obtained

15. Future Work • Use the Twitter Streaming API.It is the most reliable and secure way to get data from Twitter API. • Design some kind of useful application for the social-network user using the API, so the user can grant access to that application to his/her profile.

16. References • JGRILLI. “Word of Mouth.” Photo. http://wildpitchmarketing.com/word-of-mouth-marketing-how-to-generate-positive-word-of-mouth-for-your-business. (05 Aug 2011). <http://wildpitchmarketing.com/word-of-mouth-marketing-how-to-generate-positive-word-of-mouth-for-your-business>. • “Word of Mouth Marketing”. Photo. http://www.12manage.com/description_word_of_mouth_marketing.html. (10 Aug 2011). <http://www.12manage.com/description_word_of_mouth_marketing.html>. • D’Antonio, Mila. “Colloquy Reveals a Decline in Word of Mouth”. Photo. http://www.1to1media.com/weblog/2011/05/colloquy_reveals_a_decline_in.html (11 May 2011). <http://www.1to1media.com/weblog/2011/05/colloquy_reveals_a_decline_in.html>

17. “Word of Mouth: The Magic of Social Media”. Photo. http://www.indyposted.com/84258/word-of-mouth-the-magic-of-social-media/ . 28 Aug 2010. <http://www.indyposted.com/84258/word-of-mouth-the-magic-of-social-media/>. • Kempe, D., Kleinberg, J., Tardos, E. (2003). Maximizing the Spread of Influence through a Social Network. [PowerPoint Slides]. Retrieved from: www.cs.cmu.edu/~xiaonanz/Maximizing-the-Spread-of-Influence.ppt • Gjoka, Minas; Kurant, Maciej; Butts, Carter; Markopoulou, Athina. (March, 2010). Walking in Facebook: A Case Study of Unbiased Sampling of OSNs.Retrieved from: http://odysseas.calit2.uci.edu/doku.php/public:online_social_networks • Viswanath, Bimal; Mislove, Alan. (August, 2009). On the Evolution of User Interaction in Facebook. Retrieved from: http://socialnetworks.mpi-sws.org/data-wosn2009.html

18. Streppone, C. “Gentle Introduction to OAuth.” Image. Gentle Introduction to OAuth. 03 Nov. 2010. 05 Aug. 2011. http://dev.opera.com/articles/view/gentle-introduction-to-oauth/.