Pattern Mining Approaches Used in Social Media Data

Currently, social media networks such as Facebook and Twitter have evolved into valuable platforms for global communication. However, due to their extensive user bases, Twitter is often misused by illegitimate users engaging in illicit activities. While there are numerous research papers available that delve into combating illegitimate users on Twitter, a common shortcoming in most of these works is the failure to address the issue of class imbalance, which significantly impacts the effectiveness of spam detection. Few other research works that have addressed class imbalance have not yet applied bio-inspired algorithms to balance the dataset. Therefore, we introduce PSOB-U, a particle swarm optimization-based undersampling technique designed to balance the Twitter dataset. In PSOB-U, various classifiers and metrics are employed to select majority samples and rank them. Furthermore, an ensemble learning approach is implemented to combine the base classifiers in three stages. During the training phase of the base classifiers, undersampling techniques and a cost-sensitive random forest (CS-RF) are utilized to address the imbalanced data at both the data and algorithmic levels. In the first stage, imbalanced datasets are balanced using random undersampling, particle swarm optimization-based undersampling, and random oversampling. In the second stage, a classifier is constructed for each of the balanced datasets obtained through these sampling techniques. In the third stage, a majority voting method is introduced to aggregate the predicted outputs from the three classifiers. The evaluation results demonstrate that our proposed method significantly enhances the detection of illegitimate users in the imbalanced Twitter dataset. Additionally, we compare our proposed work with existing models, and the predicted results highlight the superiority of our spam detection model over state-of-the-art spam detection models that address the class imbalance problem. The combination of particle swarm optimization-based undersampling and the ensemble learning approach using majority voting results in more accurate spam detection.

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Enhancing stock price prediction with CommentGCN and OPERA for stock comments from social media

Zhu,

Yang,

Feng

et al. 2024

Preprint

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Stock price prediction is an important research topic in the financial field. However, there are two challenges, modeling the graph-structured stock comments effectively and tackling the severe distribution shift of the stock price data. To exploit the structural characteristics of stock comments and address the issue of data distribution shift, this study proposes a novel model, CommentGCN-OPERA, for stock price prediction. In terms of the social media textual data, to effectively process the inherently graph-structured stock comments and their interrelationships, we propose the CommentGCN module for modeling the information contained in these comments. Regarding the stock price data, the OPERA (OPEning pRice normAlization) module alleviates the issue of data distribution shift by employing input normalization and output denormalization operations, thereby reducing the bias of the stock price data. Experimental results on 6 stocks demonstrate that CommentGCN-OPERA outperforms 5 baseline models in most cases. Additionally, the ablation study further validates the independent effectiveness of the CommentGCN and OPERA modules within the model.

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Pattern Mining Approaches Used in Social Media Data

Cited by 3 publications

References 67 publications

Integrating EEMD and ensemble CNN with X (Twitter) sentiment for enhanced stock price predictions

Integrating EEMD and ensemble CNN with X (Twitter) sentiment for enhanced stock price predictions

Bio-Inspired Algorithm Based Undersampling Approach and Ensemble Learning for Twitter Spam Detection

Enhancing stock price prediction with CommentGCN and OPERA for stock comments from social media

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