Social media spam detection using different text feature selection technique and machine learning
By: Sharma, Anubha
.
Contributor(s): Ramaiya, Manoj.
Publisher: Chennai ICT Academy 2022Edition: Vol.13(1), Oct.Description: 2756-2764p.Subject(s): Computer EngineeringOnline resources: Click here
In:
ICTACT Journal on Soft Computing (IJSC)Summary: The messaging systems and social media is popular and has essential
contributions to our social and professional life. Similarly, Spam is a
part of the messaging system and social media. In social media, spam
is found in various places (i.e. in posts, in comments, in reviews, and in
chatting). Social media Spam is aimed to influence the user’s decision,
point of view, and credibility of the service or brand. Therefore, social
spam detection is essential. However, using the social media data a
number of contributions are available in literature, but a fewer amount
of work is available for social media spam detection. In this paper, we
proposed a social media spam detection technique using machine
learning and text feature extraction techniques. In this context first, a
review on social media spam detection techniques has been carried out.
Using this review, we extract the different machine learning techniques
used, techniques of text feature selection, and experimental datasets
used. In this review, we found that the spam messages with the URLs
are more critical and harmful. Next step, we design a theoretical model
for social media spam detection, which includes text feature selection
techniques (i.e. TF-IDF, POS, and Information Gain) and their
combinations (POS+TF-IDF and POS+IG). These features are used
with Support Vector Machine (SVM), Artificial Neural Network, and
Naïve Bayes classifier for training. Experimental analysis with dataset
available in Kaggle we found that hybrid features is more effective for
accurate classification as compared to individual features.
Additionally, we found for classification the SVM and ANN are more
accurate as compared to the Bayes classifier.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
School of Engineering & Technology Archieval Section | Not for loan | 2023-0512 |
The messaging systems and social media is popular and has essential
contributions to our social and professional life. Similarly, Spam is a
part of the messaging system and social media. In social media, spam
is found in various places (i.e. in posts, in comments, in reviews, and in
chatting). Social media Spam is aimed to influence the user’s decision,
point of view, and credibility of the service or brand. Therefore, social
spam detection is essential. However, using the social media data a
number of contributions are available in literature, but a fewer amount
of work is available for social media spam detection. In this paper, we
proposed a social media spam detection technique using machine
learning and text feature extraction techniques. In this context first, a
review on social media spam detection techniques has been carried out.
Using this review, we extract the different machine learning techniques
used, techniques of text feature selection, and experimental datasets
used. In this review, we found that the spam messages with the URLs
are more critical and harmful. Next step, we design a theoretical model
for social media spam detection, which includes text feature selection
techniques (i.e. TF-IDF, POS, and Information Gain) and their
combinations (POS+TF-IDF and POS+IG). These features are used
with Support Vector Machine (SVM), Artificial Neural Network, and
Naïve Bayes classifier for training. Experimental analysis with dataset
available in Kaggle we found that hybrid features is more effective for
accurate classification as compared to individual features.
Additionally, we found for classification the SVM and ANN are more
accurate as compared to the Bayes classifier.
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