Behavioral analysis in social networks: an approach based on intelligent system
Pages 193 - 196
Abstract
This paper presents a new methodology to classify Twitter users based on Artificial Neural Networks and Fuzzy Logic. Simulations are carried out using a SOM (Self Organized Maps) neural network for classifying users into four distinct groups: (0)Unimpressive User; (1)Desired User: Follower; (2)Desired User: Follower and Publisher; (3)Desired User: Publisher. The proposed methodology was validated through an autonomous agent, whose interactions with others were modeled by means of Fuzzy Inference System. The results obtained show that neural networks can be used for user classification in social networks, and we observed that the interaction of the agent with other users occurred in a transparent way, i.e., showing typical behaviors of real users.This paper presents a new methodology to classify Twitter users based on Artificial Neural Networks and Fuzzy Logic. Simulations are carried out using a SOM (Self Organized Maps) neural network for classifying users into four distinct groups: (0)Unimpressive User; (1)Desired User: Follower; (2)Desired User: Follower and Publisher; (3)Desired User: Publisher. The proposed methodology was validated through an autonomous agent, whose interactions with others were modeled by means of Fuzzy Inference System. The results obtained show that neural networks can be used for user classification in social networks, and we observed that the interaction of the agent with other users occurred in a transparent way, i.e., showing typical behaviors of real users.
References
[1]
F. Abel, Q. Gao, G. Houben, and K. Tao. Analyzing temporal dynamics in twitter profiles for personalized recommendations in the social web. Journal of the American Society for Information Science and Technology, 2011.
[2]
S. G. Arlei Silva, Hérico Valiati and W. M. Jr. From individual behavior to influence networks: A case study on twitter. In WebMedia, 2011.
[3]
E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts. Everyone's an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011.
[4]
E. Bakshy, B. Karrer, and L. A. Adamic. Social influence and the diffusion of user-created content. In Proceedings of the 10th ACM conference on Electronic commerce. ACM, 2009.
[5]
C. Bigonha, T. N. C. Cardoso, M. M. Moro, V. A. F. Almeida, and M. A. Gonçalves. Detecting evangelists and detractors on twitter. In WebMedia, 2010.
[6]
M. Brennan and R. Greenstadt. Coalescing twitter trends: The under-utilization of machine learning in social media. In SocialCom, pages 641--646. IEEE, 2011.
[7]
P. Brown and J. Feng. Measuring user influence on twitter using modified k-shell decomposition. In The Social Mobile Web, volume WS-11-02, 2011.
[8]
M. C. Fabricio Benevenuto, Tiago Rodrigues and V. Almeida. Characterizing user behavior in online social networks. Usenix/ACM SIGCOMM Internet Measurement Conference (IMC), Chicago,USA, 2009.
[9]
Z. Liu, S. He, and W. Xiong. A Fuzzy Logic Based Emotion Model for Virtual Human., pages 284--288. IEEE Computer Society, 2008.
[10]
C. Meeyoung, H. Hamed, B. Fabricio, and G. Krishna. Measuring user influence in twitter: The million follower fallacy. 2010.
[11]
W. Pedrycz and M. Reformat. Evolutionary fuzzy modeling, pages 652--665. Springer-Verlag, 2003.
[12]
M. Pennacchiotti and A.-m. Popescu. A machine learning approach to twitter user classification. Artificial Intelligence, 46(39):281--288, 2010.
[13]
B. Prieto, J. Merelo, A. Prieto, and F. Tricas. Analyzing a Web-Based Social Network Using Kohonen's SOM, pages 911--918. 2007.
[14]
J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010.
[15]
G. P. Zhang. Neural networks for classification: a survey. IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews, pages 451--462, 2000.
Index Terms
- Behavioral analysis in social networks: an approach based on intelligent system
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Published In
October 2012
426 pages
ISBN:9781450317061
DOI:10.1145/2382636
- General Chairs:
- Graça Bressan,
- Regina Melo Silveira,
- Program Chairs:
- Ethan V. Munson,
- André Santanchà,
- Maria da Graèa Campos Pimentel
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Published: 15 October 2012
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WebMedia '12: Brazilian Symposium on Multimedia and the Web
October 15 - 18, 2012
São Paulo/SP, Brazil
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