Rumor Detection on Twitter Using Features Extraction Method

Jabir, Hussam Mohammed; Naser, Mohammed Abdullah; Al‐Mamory, Safaa O.

doi:10.1109/it-ela50150.2020.9253027

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Hamidian et al [21] developed J48 classifier to train the model on the WEKA platform and achieved an f-score of 82%. The efficiency of pre-processing work was not good because of the limitation of the WEKA tool.…”

Section: Machine Learning Techniques For Rumour Detectionmentioning

confidence: 99%

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Rumour detection on social media through Ensemble deep learning model

Rani

2023

Preprint

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The widespread dissemination of several rumours and fake news has extremely affected many business organizations. Rumours or fake news spread and go viral on social websites causing significant social, business and political problem around the world. Fake news distorts public belief, affects companies reputation, disturbs community, reduces government integrity, makes business vulnerable and creates an enormous danger to the social constancy. This paper proposes a novel algorithm to detect rumour spread through tweeter. Various advanced machine learning techniques: Random Forest, Gradient Boosting, Adaboost and Hard Voting classifier are applied to detect the rumours from online social media through tweeter and Facebook. The proposed model is based upon the CNN-BiLSTM-BiGRU approach which efficiently classifies tweets among rumours and non-rumours category. The proposed model provides 99% accuracy and outperforms other baseline models like Random Forest, AdaBoost, Gradient Boosting, and Hard Voting. This hybrid recommender framework can be a more beneficial system to detect rumours at earlier stages from online platforms.

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Section: Machine Learning Techniques For Rumour Detectionmentioning

confidence: 99%

“…From table 1 we analyze that existing researchers Hamidian et al [21] having the problem of tool restriction, Castillo et al [9] research work discussed about manual labeling of data, Zhao et al [22] research work lacking the problem of slow response time, Ma and Asghar et al [27,30]…”

Section: Gap Analysismentioning

confidence: 99%

Rumour detection on social media through Ensemble deep learning model

Rani

2023

Preprint

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“…Microblogging websites like Twitter, Instagram, Facebook, Telegram, etc., may quickly spread news, rumor, and authentic information. Moreover, rumors may not be safe for anyone [1][2][3][4]. False or unconfirmed information travels on the internet the same way that truthful information does, potentially going viral and affecting public opinion and choices.…”

Section: Introductionmentioning

confidence: 99%

Text Mining based Rumor Detection on Twitter data

Manita¹,

Jain²

2022

SKITRJ

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Rumors are misleading information that is not sustained at the time of circulation and are not true at the time of verification. In other words, Rumors are a set of linguistic, symbolic or tactile propositions whose veracity is not quickly or ever confirmed. As the popularity of social networking sites has increased, in recent years, incorrect information and rumors have circulated widely causing a significant influence on people's lives. Microblogging platforms are an excellent way to spread rumors and automatically disprove them in critical situations. Existing approaches to detecting rumors have depended on hand-crafted features for utilizing machine learning algorithms, which necessitates a significant amount of manual effort. In this work, we have used stylometric and word vector features and put them into machine learning models. These features are extracted from the twitter-16 dataset and by applying SVM, we have attained the highest accuracy in comparison to existing newest studies.

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