Discovering Credible Twitter Users in Stock Market Domain

Kamkarhaghighi, Mehran; Chepurna, Iuliia; Aghababaei, Somayyeh; Makrehchi, Masoud

doi:10.1109/wi.2016.0020

Cited by 6 publications

(7 citation statements)

References 22 publications

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“…Another valuable information source from social networks is user-related data, including the user's influence within the network and the accuracy of their predictions. Kamkarhaghighi et al [12] examined the relationship between a Twitter user's influential power in stock market prediction and their social network information, including details about their followers. They identified several active users in the stock exchange as valuable users and calculated a score for the accuracy of each user's predictions.…”

Section: Feature-based Methodsmentioning

confidence: 99%

“…Some researchers have also explored the trends in market changes or market indexes, such as the Dow Jones index [9,10]. With the growing use of social networks and their impact on financial markets, the sentiment analysis of messages [11] and scoring the members [12] of these networks have attracted the attention of researchers. In this paper, our focus is on predicting the daily price trend of certain shares in the DOW30 index.…”

Section: Introductionmentioning

confidence: 99%

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User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

2023

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Predicting stock market trends is an intriguing and complex problem, which has drawn considerable attention from the research community. In recent years, researchers have employed machine learning techniques to develop prediction models by using numerical market data and textual messages on social networks as their primary sources of information. In this article, we propose User2Vec, a novel approach to improve stock market prediction accuracy, which contributes to more informed investment decision making. User2Vec is a unique method that recognizes the unequal impact of different user opinions on specific stocks, and it assigns weights to these opinions based on the accuracy of their associated social metrics. The User2Vec model begins by encoding each message as a vector. These vectors are then fed into a convolutional neural network (CNN) to generate an aggregated feature vector. Following this, a stacked bi-directional long short-term memory (LSTM) model provides the final representation of the input data over a period. LSTM-based models have shown promising results by effectively capturing the temporal patterns in time series market data. Finally, the output is fed into a classifier that predicts the trend of the target stock price for the next day. In contrast to previous attempts, User2Vec considers not only the sentiment of the messages, but also the social information associated with the users and the text content of the messages. It has been empirically proven that this inclusion provides valuable information for predicting stock direction, thereby significantly enhancing prediction accuracy. The proposed model was rigorously evaluated using various combinations of market data, encoded messages, and social features. The empirical studies conducted on the Dow Jones 30 stock market showed the model’s superiority over existing state-of-the-art models. The findings of these experiments reveal that including social information about users and their tweets, in addition to the sentiment and textual content of their messages, significantly improves the accuracy of stock market prediction.

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Section: Feature-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

2023

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“…Working with this multitude of features in data analysis can be a complex task, but it can be streamlined via reducing the dataset's dimensionality and pinpointing the most relevant features for accurate classification [41]. Several studies [7,10,13,[41][42][43]50,51,[55][56][57][58][59][60]] have adopted various feature selection methods to concentrate on the most pertinent and significant features for prediction, concurrently minimizing computational complexity. Among the methods employed in these studies are:…”

Section: Feature Selectionmentioning

confidence: 99%

“…The study in [55] employs this method to identify the most discriminatory features for user credibility classification and remove irrelevant and biased ones. Meanwhile, the authors of [60] measured X-Platform users' credibility in the stock market using the correlation between each user's credibility and his or her social interaction features.…”

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confidence: 99%

The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning

Abid-Althaqafi,

Alsalamah

2024

Electronics

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In the era of digital information, online platforms play a crucial role in shaping public opinion. However, the extensive spread of misinformation and fake news poses a significant challenge, largely fueled by non-credible users. Detecting user credibility is vital for ensuring the reliability of information on these platforms. This study employs supervised machine learning algorithms, leveraging key user features to enhance credibility detection. Feature selection methods, specifically SelectKBest and correlation-based algorithms, are explored for their impact on X-Platform user credibility detection. Utilizing various classifiers, including support vector machine, logistic regression, and XGBoost, experiments are conducted on the ArPFN dataset, which is a labeled, balanced, publicly available dataset. The evaluation includes measures like accuracy, precision, recall, and F1-score to assess efficiency. This research considers feature categories and selection methods with SML to detect their impact on the accuracy of X-Platform user credibility detection, making this research a reference for researchers and practitioners working in the field of SML, feature engineering, and social media analysis. We aim to advance the field’s understanding of effective strategies for mitigating the spread of fake news. The novelty of this study lies in the comprehensive exploration of feature selection methods and their influence on credibility detection, contributing valuable insights for future research in this domain.

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“…Working with a large number of features is a complex task that emphasizes the role of feature selection which reduces the dimensionality of the dataset and identifies the features that best suit the classification process [53]. Several studies [2] [74] have employed different feature selection methods to focus on the most relevant and important features to be involved in their prediction, as well as to lower the required computational processes. The authors in [19] [42] and [74] used correlationbased feature selection methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting User Credibility on Twitter using a Hybrid Machine Learning Model of Features’ Selection and Weighting

Abid-Althaqafi,

Alsalamah

2024

ijacsa

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With the pervasive and rapidly growing presence of the internet and social media, creating untrustworthy accounts has become effortless, allowing fake news to be spread for personal or private interests. As a result, it is crucial in this era to investigate the credibility of users on social networking platforms such as Twitter. In this research, we aim to integrate existing solutions from previous research to create a hybrid model. Our approach is based on selecting and weighting features using supervised machine learning methods such as ExtraTressClarifier, correlation-based algorithm methods, and SelectKBest to extract new ranked and weighted features in the dataset and then use them to train our model to discover their impact on the accuracy of user credibility detection issues. The research objective is to combine feature selection and weighting methods with Supervised Machine Learning to evaluate their impact on the accuracy of user credibility detection on Twitter. In addition, we measure the effectiveness of different feature categories on this detection. Experiments are conducted on one of the online available datasets. We seek to employ extracted features from a user's profile and statistical and emotional information. Then, the experimental results are compared to discover the effectiveness of the proposed solution. This study focuses on revealing the credibility of Twitter (or X-platform as recently renamed) accounts, which may result in the need for some adjustments to the generalization of Twitter's outputs to other social media accounts such as LinkedIn, Facebook, and others.

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Discovering Credible Twitter Users in Stock Market Domain

Cited by 6 publications

References 22 publications

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning

Detecting User Credibility on Twitter using a Hybrid Machine Learning Model of Features’ Selection and Weighting

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