A Hybrid Approach to Rumour Detection in Microblogging Platforms

Vijeev, Abhishek; Mahapatra, Anushreya; Shyamkrishna, Arundhati; Murthy, Srinivasa

doi:10.1109/icacci.2018.8554371

Cited by 11 publications

(7 citation statements)

References 4 publications

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“…Vijeev et al [6] dealt with rumors as a detection problem and extracted both user features and content features using NLP techniques. Since the number of extracted features was large, they employed a Chi-square statistical test to select and rank the best features.…”

Section: Conventional Machine Learning Methodsmentioning

confidence: 99%

“…In general, most of the current methods treat rumor detection as a binary classification problem where the content (defused information via OSN) is classified as rumor or non-rumor [2,3]. Currently, the research interests around rumor detection are focused on applying either traditional machine learning methods [2,[4][5][6] or deep learning methods [7][8][9][10][11][12][13] (The interested reader can refer to [14]).…”

Section: Introductionmentioning

confidence: 99%

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Detecting Rumors on Social Media Based on a CNN Deep Learning Technique

Alsaeedi

Al-Sarem

2020

Arab J Sci Eng

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Currently, it is easy to create content and share it via social media platforms such as Twitter, Facebook, and Sina Weibo. However, some problems can occur when the shared content includes untrustworthy or misleading information. Thus, researchers from different domains have tried to investigate the impact of rumors on the global community. Several machine learning approaches have been used to detect rumors at their early stage. However, the achieved accuracies demonstrate that the existing state-of-the-art rumor detection approaches still require improvement. In this paper, we propose a deep learning model based on a conventional neural network (CNN) to detect rumors spreading on Twitter. Several experiments were conducted to find the best hyperparameter settings to improve the model's performance. We compared our results with other relevant rumor detection approaches that used the same publicly available benchmark dataset to demonstrate our model's performance regarding the accuracy, precision, recall and f -measure. The results show that our CNN model outperforms all the existing approaches and achieves the best balance of recall and precision.

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Section: Conventional Machine Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting Rumors on Social Media Based on a CNN Deep Learning Technique

Alsaeedi

Al-Sarem

2020

Arab J Sci Eng

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“…The model used a recurrent neural network and it outperformed four standard baselines. Similarly, a system is developed by Vijeev, et al [39] in the same year to identify rumours on Twitter microblog. Content-based and user-based features are used, and three machine learning models are tested.…”

Section: Research Questions and Contributionsmentioning

confidence: 99%

Rumour identification on Twitter as a function of novel textual and language-context features

Ali

Malik

2022

Multimed Tools Appl

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Social microblogs are one of the popular platforms for information spreading. However, with several advantages, these platforms are being used for spreading rumours. At present, the majority of existing approaches identify rumours at the topic level instead of at the tweet/post level. Moreover, prior studies used the sentiment and linguistic features for rumours identification without considering discrete positive and negative emotions and effective part-of-speech features in content-based approaches. Similarly, the majority of prior studies used content-based approaches for feature generation, and recent context-based approaches were not explored. To cope with these challenges, a robust framework for rumour detection at the tweet level is designed in this paper. The model used word2vec embeddings and bidirectional encoder representations from transformers method (BERT) from context-based and discrete emotions, linguistic, and metadata characteristics from content-based approaches. According to our knowledge, we are the first ones who used these features for rumour identification at the tweet/post level. The framework is tested on four real-life twitter microblog datasets. The results show that the detection model is capable of detecting 97%, 86%, 85%, and 80% of rumours on four datasets respectively. In addition, the proposed framework outperformed the three latest state-of-the-art baselines. BERT model presented the best performance among contextbased approaches, and linguistic features are best performing among content-based approaches as a stand-alone model. Moreover, the utilization of two-step feature selection further improves the detection model performance.

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“…We utilised some techniques reported in the literature for achieving good performance on text classification from various studies. The selected classification techniques are Support Vector Machines (SVM) [7], [11], [27] Logistic Regression (LR) [7], [11], [27], Naïve Bayes Classifier (NBC) [7], [27], AdaBoost [28], [29], and K-Nearest Neighbors (KNN) [7], [27]. Additionally, we also used BERT vectors to train the classifier model based on Multilayer Perceptron (MLP).…”

Section: B Classifiers To Identify Rumoursmentioning

confidence: 99%