Name
Abstract | ||
|---|---|---|
Social networking platforms, particularly sites like Twitter and Facebook have grown tremendously in the past decade and has solicited the interest of millions of users. They have become a preferred means of communication, due to which it has also attracted the interest of various malicious entities such as spammers. The growing number of users on social media has also created the problem of fake accounts. These false and fake identities are intensively involved in malicious activities such as spreading abuse, misinformation, spamming and artificially inflating the number of users in an application to promote and sway public opinion. Detecting these fake identities, thus becomes important to protect genuine users from malicious intents. To address this issue, we aim to use a feature-based approach to identify these fake profiles on social media platforms. We have used twenty-four features to identify fake accounts efficiently. To verify the classification results three classification algorithms are used. Experimental results show that our model was able to reach 97.9% accuracy using the Random Forest algorithm. Hence, the proposed approach is efficient in detecting fake profiles.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3361758.3361784 | Proceedings of the 3rd International Conference on Big Data and Internet of Things |
Keywords | Field | DocType |
Fake Profiles, Machine Learning, Security, Social Media Platforms | Internet privacy,Social media,Social network,Computer science,Misinformation,Public opinion,Feature based,Random forest,Statistical classification,Spamming | Conference |
ISBN | Citations | PageRank |
978-1-4503-7246-6 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jyoti Kaubiyal | 1 | 0 | 0.34 |
| Ankit Kumar Jain | 2 | 81 | 7.77 |