Bot Detection on Online Social Networks Using Deep Forest

Daouadi, Kheir Eddine; Rebaï, Rim Zghal; Amous, Ikram

doi:10.1007/978-3-030-19810-7_30

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…Their proposed architecture achieved 96% AUC score in bot detection. [52] 2019 Deep Forest Algorithm Dataset collected from the Twitter timeline(API)…”

Section: Deepfake Text Generation Methodsmentioning

confidence: 99%

Deepfake Detection on Social Media: Leveraging Deep Learning and FastText Embeddings for Identifying Machine-Generated Tweets

Sadiq,

Aljrees,

Ullah

2023

IEEE Access

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Recent advancements in natural language production provide an additional tool to manipulate public opinion on social media. Furthermore, advancements in language modelling have significantly strengthened the generative capabilities of deep neural models, empowering them with enhanced skills for content generation. Consequently, text-generative models have become increasingly powerful allowing the adversaries to use these remarkable abilities to boost social bots, allowing them to generate realistic deepfake posts and influence the discourse among the general public. To address this problem, the development of reliable and accurate deepfake social media message-detecting methods is important. Under this consideration, current research addresses the identification of machine-generated text on social networks like Twitter. In this study, a straightforward deep learning model in combination with word embeddings is employed for the classification of tweets as human-generated or bot-generated using a publicly available Tweepfake dataset. A conventional Convolutional Neural Network (CNN) architecture is devised, leveraging FastText word embeddings, to undertake the task of identifying deepfake tweets. To showcase the superior performance of the proposed method, this study employed several machine learning models as baseline methods for comparison. These baseline methods utilized various features, including Term Frequency, Term Frequency-Inverse Document Frequency, FastText, and FastText subword embeddings. Moreover, the performance of the proposed method is also compared against other deep learning models such as Long short-term memory (LSTM) and CNN-LSTM displaying the effectiveness and highlighting its advantages in accurately addressing the task at hand. Experimental results indicate that the streamlined design of the CNN architecture, coupled with the utilization of FastText embeddings, allowed for efficient and effective classification of the tweet data with a superior 93% accuracy.

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“…Their proposed architecture achieved 96% AUC score in bot detection. [52] 2019 Deep Forest Algorithm Dataset collected from the Twitter timeline(API)…”

Section: Deepfake Text Generation Methodsmentioning

confidence: 99%

Deepfake Detection on Social Media: Leveraging Deep Learning and FastText Embeddings for Identifying Machine-Generated Tweets

Sadiq,

Aljrees,

Ullah

2023

IEEE Access

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“…The deep Q-network architecture designed by Lingam et al [28] showed approximately 5-20% improvement of precision, recall, F1-score and G-measures over the baseline algorithms (FFNN, RDNN, SNA-DRL, C-DRL and ADQL), from a combination of tweet-based, user profilebased and social graph-based features. The deep forest algorithm proposed by Daouadi et al [58] yielded an accuracy of 97.55% information from the metadata of user profiles and posts, which was * 2% higher than the RF model and outperformed other traditional ML algorithms such as bagging (B), AdaBoost (AB), random forest (RF) and simple logistic (SL), in terms of AUC measures.…”

Section: Comparison Between DL and Traditional Ml Techniquesmentioning

confidence: 95%

“…? and DenStream) [58] and generic statistical approaches in [59]. Kudugunta and Ferrara [33] showed a detailed comparison of their proposed contextual LSTM architecture performance for social media bot detection at tweet level, with a number of ML methods.…”

Section: Comparison Between DL and Traditional Ml Techniquesmentioning

confidence: 99%

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Social media bot detection with deep learning methods: a systematic review

Hayawi

Saha²,

Masud

et al. 2023

Neural Comput & Applic

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Social bots are automated social media accounts governed by software and controlled by humans at the backend. Some bots have good purposes, such as automatically posting information about news and even to provide help during emergencies. Nevertheless, bots have also been used for malicious purposes, such as for posting fake news or rumour spreading or manipulating political campaigns. There are existing mechanisms that allow for detection and removal of malicious bots automatically. However, the bot landscape changes as the bot creators use more sophisticated methods to avoid being detected. Therefore, new mechanisms for discerning between legitimate and bot accounts are much needed. Over the past few years, a few review studies contributed to the social media bot detection research by presenting a comprehensive survey on various detection methods including cutting-edge solutions like machine learning (ML)/deep learning (DL) techniques. This paper, to the best of our knowledge, is the first one to only highlight the DL techniques and compare the motivation/effectiveness of these techniques among themselves and over other methods, especially the traditional ML ones. We present here a refined taxonomy of the features used in DL studies and details about the associated pre-processing strategies required to make suitable training data for a DL model. We summarize the gaps addressed by the review papers that mentioned about DL/ML studies to provide future directions in this field. Overall, DL techniques turn out to be computation and time efficient techniques for social bot detection with better or compatible performance as traditional ML techniques.

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“…These methods employ machine learning classifiers to detect malicious SMBs, treating the detection as binary classification and relying on a substantial amount of annotated data for training. Daouadi et al [13] proposed an augmented set of features that leverage the interaction volume between accounts, combined with other features from previous research, to detect bot accounts on Twitter. Kudugunta et al [14] employed the content of individual tweets and six account features to identify bots on Twitter.…”

Section: Node-based Detection Approachesmentioning

confidence: 99%

MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection

Zeng

Sun

2023

Electronics

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Social media bots pose potential threats to the online environment, and the continuously evolving anti-detection technologies require bot detection methods to be more reliable and general. Current detection methods encounter challenges, including limited generalization ability, susceptibility to evasion in traditional feature engineering, and insufficient exploration of user relationships. To tackle these challenges, this paper proposes MRLBot, a social media bot detection framework based on unsupervised representation learning. We design a behavior representation learning model that utilizes Transformer and a CNN encoder–decoder to simultaneously extract global and local features from behavioral information. Furthermore, a network representation learning model is proposed that introduces intra- and outer-community-oriented random walks to learn structural features and community connections from the relationship graph. Finally, the behavioral representation and relationship representation learning models are combined to generate fused representations for bot detection. The experimental results of four publicly available social network datasets demonstrate that the proposed method has certain advantages over state-of-the-art detection methods in this field.

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Bot Detection on Online Social Networks Using Deep Forest

Cited by 14 publications

References 16 publications

Deepfake Detection on Social Media: Leveraging Deep Learning and FastText Embeddings for Identifying Machine-Generated Tweets

Deepfake Detection on Social Media: Leveraging Deep Learning and FastText Embeddings for Identifying Machine-Generated Tweets

Social media bot detection with deep learning methods: a systematic review

MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection

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