Automated credibility assessment on twitter

• Autorzy: Lorek K. , Suehiro-Wiciński J. , Jankowski-Lorek M. , Gupta A.

Identyfikatory

DOI

7494/csci.2015.16.2.157

• Języki publikacji: EN

Abstrakty

EN

In this paper, we make a practical approach to automated credibility assessment on Twitter. We describe the process behind the design of an automated classifier for information credibility assessment. As an addition, we propose practical implementation of TwitterBOT, a tool which is able to score submitted tweets while working in the native Twitter interface.

Słowa kluczowe

EN

Twitter; credibility; machine learning algorithms

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2015
• Tom: Vol. 16 (2)
• Strony: 157--168
• Opis fizyczny: Bibliogr. 10 poz., rys., wykr., tab.

Twórcy

autor

Lorek K.

• Polish-Japanese Institute of Information Technology, Warsaw, Poland

autor

Suehiro-Wiciński J.

• Polish-Japanese Institute of Information Technology, Warsaw, Poland

autor

Jankowski-Lorek M.

• Polish-Japanese Institute of Information Technology, Warsaw, Poland

autor

Gupta A.

• Ecole Polytechnique Federale de Lausanne

Bibliografia

• [1] Canini K., Suh B., Pirolli P.: Finding Credible Information Sources in Social Networks Based on Content and Social Structure. In: IEEE International Conference on Privacy, Security, Risk, and Trust , IEEE, 2011.
• [2] Castillo C., Mendoza M., Poblete B.: Information Credibility on Twitter. In: 2011 International World Wide Web Conference , ACM, 2011.
• [3] Gupta A., Kumaraguru P., Castillo C., Meier P.: TweetCred: A Real-time Web-based System for Assessing Credibility of Content on Twitter. CoRR , vol. abs/1405.5490, 2014, http://arxiv.org/abs/1405.5490 .
• [4] Gupta M., Zhao P., Han J.: Evaluating Event Credibility on Twitter. In: Proceedings of the 2012 SIAM International Conference on Data Mining (SDM) , pp. 153–164, 2012.
• [5] Jankowski-Lorek M., Nielek R., Wierzbicki A., Zieli ́ński K.: Predicting Controversy of Wikipedia Articles Using the Article Feedback Tool. In: Proceedings of the 2014 International Conference on Social Computing , SocialCom ’14, pp. 22:1– 22:7, ACM, New York, NY, USA, 2014, http://doi.acm.org/10.1145/2639968.2640074 .
• [6] Kang B., O’Donovan J., Hollerer T.: Modeling Topic Specific Credibility in Twit- ter. In: IUI12 , ACM, 2012.
• [7] Khan J. I., Wierzbicki A.: Foundations of Peer-to-Peer Computing. In: Elsevier Journal of Computer Communication , 2008.
• [8] Morris M., Counts S., Roseway A., Hoff A., Shwartz J.: Tweeting is Believing? In: Understanding Microblog Credibility Perceptions. CSCW12 , ACM, 2012.
• [9] O’Donovan J., Kang B., Meyer G., Hollerer T., Adall S.: Credibility in Context: An Analysis of Feature Distributions in Twitter. In: ASE/IEEE International Conference on Social Computing , IEEE, 2012.
• [10] Wierzbicki A., Szczepaniak R., Buszka M.: Application Layer Mul-ticast For Efficient Peer-to-Peer Applications. In: IEEE Computer Society , 2003