DocumentCode
2062689
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
fYear
2010
fDate
Nov. 29 2010-Dec. 1 2010
Firstpage
813
Lastpage
817
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on
Conference_Location
Cairo
Print_ISBN
978-1-4244-8134-7
Type
conf
DOI
10.1109/ISDA.2010.5687162
Filename
5687162
Link To Document