Detection of Malicious Bots in Twitter Network
Abstract & Details
Research Area
Computer Science Engineering
Keywords
Malicious Social Bots
Spearman’s Correlation
Decision Tree
Random Forest
Support Vector
Machine
Naïve Bayes
Logistic Regression
Cross Validation
Ensemble
Abstract
Malicious social bots generate fake tweets and automate their social relationships either by pretending like a
follower or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post
shortened malicious URLs in the tweet in order to redirect the requests of online social networking participants
to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most
important tasks in the Twitter network. In this project we are detecting Twitter bots within the network using
machine learning algorithms, namely Naive Bayes, Random Forest, and Decision Trees. We collected and pre-
processed a comprehensive dataset of bot and genuine user profiles, extracting features like posting frequency,
content patterns, and follower relationships for classification. Through extensive experimentation, we compared
the accuracy, precision, recall, and F1- score of these algorithms.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Akash J | ATME College of Engineering |
| 2 | Ravikumar N | ATME College of Engineering |
| 3 | Shishir Somapur | ATME College of Engineering |
| 4 | Sumit Rathod | ATME College of Engineering |
| 5 | Mr. Sandesh R | ATME College of Engineering |
How to Cite
Use the following formats to cite this article in your research.
APA Style
J, Akash, N, Ravikumar, Somapur, Shishir, Rathod, Sumit, & R, Mr. Sandesh (2024). Detection of Malicious Bots in Twitter Network. International Journal of Advance Research and Innovative Ideas In Education, 10(3), 942-947.
MLA Style
J, Akash, et al. "Detection of Malicious Bots in Twitter Network." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 3, 2024, pp. 942-947.
IEEE Style
Akash J, Ravikumar N, Shishir Somapur, Sumit Rathod, and Mr. Sandesh R, "Detection of Malicious Bots in Twitter Network," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 3, pp. 942-947, 2024.
Vancouver Style
J Akash, N Ravikumar, Somapur Shishir, Rathod Sumit, R Mr. Sandesh. Detection of Malicious Bots in Twitter Network. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(3):942-947.
Harvard Style
J, Akash, N, Ravikumar, Somapur, Shishir, Rathod, Sumit, & R, Mr. Sandesh (2024) 'Detection of Malicious Bots in Twitter Network', International Journal of Advance Research and Innovative Ideas In Education, 10(3), pp. 942-947.
Chicago Style
J, Akash, et al. "Detection of Malicious Bots in Twitter Network." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 942-947.
Turabian Style
J, Akash, et al. "Detection of Malicious Bots in Twitter Network." International Journal of Advance Research and Innovative Ideas In Education 10, no. 3 (2024): 942-947.
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