Title :
Mining pharmaceutical spam from Twitter
Author :
Shekar, Chandra ; Wakade, Shruti ; Liszka, Kathy J. ; Chan, Chien-Chung
Author_Institution :
Dept. of Comput. Sci., Univ. of Akron, Akron, OH, USA
fDate :
Nov. 29 2010-Dec. 1 2010
Abstract :
This paper presents a method of applying text mining techniques and data mining tools for pharmaceutical spam detection from Twitter data. A simple method based on a manually selected list of 65 pharmaceutical discriminating words is used for labeling spam training tweets. Preliminary experimental results show that J48 decision tree classifier has better performance over Naïve Bayesian algorithm.
Keywords :
data mining; decision trees; pattern classification; social networking (online); text analysis; unsolicited e-mail; J48 decision tree classifier; Twitter; naive Bayesian algorithm; pharmaceutical discriminating words; pharmaceutical spam mining; text mining techniques; Twitter; data mining; social networking; spam;
Conference_Titel :
Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-8134-7
DOI :
10.1109/ISDA.2010.5687162
