Fake Account Detection Using Machine Learning

Kondeti, Priyanka; Yerramreddy, Lakshmi Pranathi; Pradhan, Anita; Swain, Gandharba

doi:10.1007/978-981-15-5258-8_73

Cited by 15 publications

(2 citation statements)

References 2 publications

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“…They proposed a scalable and generalizable bot detection method and used a data selection criterion to find the best model. Most of the methods that use only the metadata information from the user profile are trained using Random Forest or Adaboost classifiers (Daouadi et al 2020 ; Kondeti et al 2021 ). Deep learning techniques are not much explored when using these sets of features.…”

Section: Related Workmentioning

confidence: 99%

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data

Hayawi

Mathew

Venugopal

et al. 2022

Soc. Netw. Anal. Min.

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Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework ‘DeeProBot,’ which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.

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Section: Related Workmentioning

confidence: 99%

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data

Hayawi

Mathew

Venugopal

et al. 2022

Soc. Netw. Anal. Min.

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“…Towards improving the performance in detecting fake profiles different normalization approaches like Min-Max and Z score has been presented. The method is evaluated with the twitter data set [1,2]. A machine learning model is presented towards securing the social media accounts which calculate followers and friends of any account to measure the trust of any user [3].…”

Section: Related Workmentioning

confidence: 99%

Real Time Profile Analysis and Fake Detection Model for Improved Profile Security in Online Social Networks

2022

IJIES

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The problem of social network security has been well studied. Numbers of approaches are identified towards securing the profile of social network users and identifying the fake profiles. Still the methods suffer to produce efficient results on detecting fake profiles. To improve the performance, an efficient profile analysis and fake detection model is presented in this article. The proposed Real Time Profile Analysis and Fake Detection Model (RTPAFDM) works on three phases: first the Post Level Trust Analysis (PLTA) is performed, which monitors the post updated on every day and share the posts internally with other users to claim the trustworthy of the post which computes the value of User Post Trust Weight (UPTW); second the method performs Profile Level Trust Analysis (PrLTA) which extracts the profile information like user name, personal details, contact details and friends list. According to the information extracted, the method searches on the entire social network to measure the Profile Match Similarity (PSM). Based on the PSM value, the method would identify the originality of the profile; finally, the method performs Profile Historic Trust Analysis (PHTA) which works on the earlier posts made and their truth values. It computes the value of Historical Trusted Post Weight (HTPW). Using the result of all these trust analysis values, the profile security and fake detection is performed. The method is evaluated with Twitter data set and achieved fake detection accuracy up to 97% and supports the improvement profile security efficiently.

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Fake account detection in twitter using logistic regression with particle swarm optimization

Bharti

Pandey

2021

Soft Comput

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Fake Account Detection Using Machine Learning

Cited by 15 publications

References 2 publications

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data

Real Time Profile Analysis and Fake Detection Model for Improved Profile Security in Online Social Networks

Fake account detection in twitter using logistic regression with particle swarm optimization

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